Ncodedownloads GW30 Tutorial and Quick Start Guide

GlyphWorks 3.0 Tutorials

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Copyright © 2005 nCode International

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Introduction How to use these Tutorials

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Tutorial 1—Data Import and Display This tutorial shows you how to use the GlyphWorks user interface and shows how ASCII time signals can be imported into the software. Basic GlyphWorks display tools are shown that allow data plotting on the screen.

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Tutorial 2—Basic Signal Analysis This tutorial introduces some of the many analysis capabilities of GlyphWorks. GlyphWorks’ process driven interface helps make complicated analyses intuitive and fast. In particular, an introduction is provided on Amplitude Probability Analysis, Statistical Analysis, Rainflow Analysis and Frequency Analysis. The theory behind these is briefly discussed and examples are given to illustrate the differ-ences between good and bad data.

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Tutorial 3—Data Manipulation Here you are introduced to some of the many tools used for manipulating data. You will take the anomalous data found in Tutorial 2 and use the tools to ‘clean’ it for further analysis. In particular, you will extract a sample of good data from a much larger time signal, edit the data using an interac-tive Graphical Editor, then remove the Spikes and carry out Frequency Filtering.

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Tutorial 4—Stress Life (SN) Fatigue Analysis After an overview of SN theory, GlyphWorks is used to calculate the stress fatigue life of the compo-nent introduced in tutorial 3. Next, demonstrations of the glyphs are shown for advanced sensitivity studies, ‘what-if’ scenario calculations and ‘Back’ calculations.

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Tutorial 5—Strain Life (EN) Fatigue Analysis After a brief introduction to EN theory, GlyphWorks is used to calculate the strain fatigue life of the component.

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Contents:

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How to use these Tutorials

Welcome to the ICE-flow GlyphWorks 3.0 Tutorials.

The following topics are considered:

• Conventions used in the tutorials

• How to get more information from the GlyphWorks online documentation

• An overview of the contents of each tutorial

Introduction

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Introduction There are 5 tutorials designed to get you quickly up to speed with the GlyphWorks 3.0 advanced engineering software.

These tutorials are designed to get you up and running via a series of step by step procedures.

By working through the tutorials you will learn how to use the advanced functionality of GlyphWorks. More importantly you will learn how to process data files and carry out fatigue analyses.

The tutorials should take about an hour each to complete and assume no prior knowledge of GlyphWorks or other nCode products such as nSoft. Each tutorial builds on what you learned from the previous tutorials, so you should do them in se-quence. For example, the input file used in the fatigue analysis in tutorial 4 is produced at the end of tutorial 3.

Conventions used in the tutorials

The page layout of the tutorials is designed for ease of navigation: the procedures to follow are shown in the green panel, Information panels can be found in yellow, while more detailed explanations of the analysis and screen shots are shown in the body of the document.

The green Procedure Panel gives concise instruc-tions on what glyphs and files to choose , and what analysis options to use in the examples.

A description and intro-duction to the work and analysis is contained in the main body of the docu-ment along with relevant screen shots to help you through the analysis.

The yellow Information panels give you theory and a conceptual background to the tutorial.

Where you see this icon there is a link to more detailed GlyphWorks help

Introduction

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Getting more information!

GlyphWorks, a part of nCode’s ICE-flow suite of pro-grams, has comprehensive online documentation (over 800 pages of it)! A question mark icons has been placed into the online documentation pointing to where you can get further information about the topic you are doing. For example:

How to access the GlyphWorks online documentation The online documentation is available from within GlyphWorks

• You can view the GlyphWorks manual by select-

ing from the menu Help/On-line Manual.

• Alternatively you could view the entire collection

of ICE-flow manuals by selecting the Help sub-menu from the left hand side applications bar and select the Contents document.

• The document intro_help.pdf tells you

how to locate any information in their 800 pages using Adobe Acrobat’s search feature.

See glyphref.pdf page 27 for more detail

Further Training

While preparing the guide each topic has been intro-duced with a brief technical description of the analysis being undertaken. This guide is aimed at expert fatigue engineers and students alike, but if you would like more detailed information on these topics consider attending a GlyphWorks training courses. More information can be found on the nCode website www.ncode.com or by con-tacting nCode.

After completing these exercises you might want to look at some more worked examples. You will find an Online ‘Worked Examples’ Guide in the help system by selecting the menu Help /On-line Manual.

Introduction

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Introduction

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Data Import and Display

Learning Objectives This tutorial shows you how to operate the GlyphWorks 3.0 interface, and how to carry out essen-tial tasks. The topics covered include:

Topic 1 – Learning about test names and channel names

Topic 2 – Starting a project. an introduction to the GlyphWorks interface.

Topic 3 – Displaying data files in GlyphWorks. Glyphs and their properties.

Topic 4 – Carry out a simple Rainflow analysis. Using multiple glyphs in a flow.

Topic 5 – Exporting data from GlyphWorks to an ASCII CSV file for use in MS Excel

Topic 6 – Importing data into GlyphWorks from an ASCII CSV file

Pre-requisites ICE-flow GlyphWorks 3.0 must be installed on your computer, and the following tutorial files must be installed in your working directory: • Vib01.dac to vib05.dac • Strainrosette.csv

Tutorial 1—Data Import and Display in GlyphWorks

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Topic 1

In this topic you will learn about some standard file naming techniques that you can use to make your data analysis flow

more easily. The concept of breaking down data in the form of ‘Tests’ and ‘Channels’ will then be introduced.

GlyphWorks can handle all sorts of data from a simple single channel test to a complex multi-test multi-channel configuration. If you only measure small amounts of data then this functionality is probably irrelevant, but if you handle lots of measured data and need to process it automatically then this will save you hours of work.

Developing a Test Program Suppose you are designing a new anti-roll bar (or sway bar) for a car. You will need to make sure that the anti-roll bar works properly on all road surfaces, under all loading conditions from a single occupant to fully laden.

To make sure you have covered all eventualities, you develop a testing program that measures the response of the anti-roll bar for each ‘Event’ that it might see. The test program might look something like this:

Now you could choose to measure all the events in one long time signal but it is often preferred to separate them into separate test files so it’s easier to see how the anti-roll bar responds to each event. You can either separate them as you measure them, starting a new file just before each test, or you can use GlyphWorks to separate them later.

What Are Channels? Once you have determined the Tests you wish to perform over all the various events that the roll-bar is expected to see, you must determine what parameters you want to measure. In this example you might observe the vertical displacement at each wheel along with the torque in the bar. To do this you will need two extensometers for displacement and probably four strain gages to calculate the torsion. You therefore need to measure 6 channels of simultaneous data as given below:

File Naming Convention GlyphWorks is designed to handle this sort of data automatically. If you give it a number of tests as input, it will process each of them in turn. Each test, however, can comprise a number of channels of simultaneous measurements, and these are all processed simultaneously by GlyphWorks. This makes it really easy therefore to take the raw strain channels obtained above and run them through an equation to derive the torsion moment. GlyphWorks can automatically determine the channel numbering from the file naming convention or from the raw data acquisition files. If the data filename ends with a pair of numbers then GlyphWorks assumes that these are the channel numbers as shown here. This allows up to 99 channels per test. You can change the properties if you need to use more channels, i.e. 999, 9999, etc...

Test Number

Surface Road Speed

Weight %

01 Smooth straight road 50 50

02 Slow curve 50 50

03 Belgium block 30 50

04 Etc...

Channel Number

Transducer

01 Vertical Displacement Offside Wheel [mm]

02 Vertical Displacement Nearside Wheel [mm]

03 Torsion Strain Gage 01 [me]




…\TestName02.dac

…\Test0102.dac

Various tests shown as fold-ers in a tree

Expanding on the test will show each channel of meas-ured data. You can pick 1 channel at a time or process all channels simultaneously.

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Property Editor This shows the calculation properties of the selected glyph. Each calcula-tion glyph has a number of properties depend-ing on the type of calculation it performs. Most of these are set to sensible defaults by GlyphWorks; however, you might want to change these for a particular calculation. You can make the property editor larger by drag-ging it’s frame. Alternatively, the property editor is used so regularly that you can view a full sized version by right clicking on the required glyph and selecting Properties from the menu. (See later examples)

Topic 2

In this topic the GlyphWorks program will be launched and a project folder containing ex-ample data for later analysis will be set up.

• Create a new working directory on your computer and copy the training files here.

• Start the ICE-flow GlyphWorks program

• Select the working directory you have just created as your new project folder

• Click the GlyphWorks application but-ton from the Processing Menu

GlyphWorks offers you a choice of project folder. This is the location on the hard disk where all data pertaining to this project is stored. When GlyphWorks is running you can pick measured data from any input source; hard disk, network or Library data management system. The folder chosen here is used for GlyphWorks to store any working data you produce.

ICE-flow Menu ICE-flow GlyphWorks is a suite of engineering applications. Each application is launched from the menu positioned along the left hand edge of the screen. Applica-tions are grouped logically to help you find the tools you need.

Analysis History This shows the history of things done in GlyphWorks so far and is used for undo and redo functions.

Diagnostics and Progress This shows a detailed account of the calculation progress so far and any errors or warnings found in the calcu-lation process. It helps you fix any problems quickly and determine how long a complicated calculation is likely to take.

Analysis Workspace This is the workspace where you cre-ate your analysis process. Analysis processes are created by dragging calculation glyphs from the Glyph Palette and dropping them onto the workspace. These are then linked by Pipes to create a calculation flow.

Glyph Palette This palette contains the actual calculation glyphs. These are dragged onto the workspace to construct the analysis process.

Available Data Tree This tree lists all the available data files for analysis. GlyphWorks will auto-matically load all files in the project folder. You can then add additional files to the tree using the menu File/Open Data Files… Data files listed here can originate from many sources including the hard disk, network or a Library data management system. They do not have to be stored locally in the project folder. The tree merely stores a link to the original data so you can find it quickly and easily.


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This button scans all data files in the chosen directory. It then dis-plays the type of data and the number of channels within each file so you can select those chan-nels you require without having to continually handle huge sets of data.

Available Data, Tree or Table View The available data pane can be viewed as either a tree or a table. The tree resembles a standard directory tree, only GlyphWorks allows data to be re-grouped in different ways to improve searching. The tree branches can be re-grouped by right clicking on the tree. The following options are available: 1. Type of data, followed by test name followed by channel 2. Type of data, followed by channel number followed by test 3. Type of data, followed by channel title followed by test name 4. Disk directory name, followed by test followed by channel

The available data can also be displayed in tabular format as shown here. In this view, the data can be sorted in different ways by clicking on the appropriate column title. The tabular view shows more information than the tree view; however, most people find the tree view clearer.

Additional data can be added to the available data pane using the pull-down menu option File/Open Data Files… The dialog shown below opens allowing you to change directories, browse the data in the directory and select which data you wish to import. Data is not physically copied into the working folder as the data tree merely contains links to the actual file. This is particularly useful if you need to use standard company data from a central server. It would be most uneconomical to keep copying this data locally, so this method allows data to be stored once but accessed as if it were local to your work.

You can select the type of data file to display and enter an optional search key if required: e.g. vib*.dac Press the Scan Now button to see all the files matching your search key.

The available data is listed here. This shows the test data file name, the type of data present and the number of channels. Data is loaded by selecting the required tests and clicking the Add Tests buttons adjacent. See below for more detail.

These buttons are used to add and remove the selected tests.

These buttons are used to add or remove all tests.

The selected tests and channels are shown in this pane. These are imported into GlyphWorks by clicking the Add to File List button.

You can import all data channels for the chosen tests by clicking on Ex-pand Channels and select individual channels. The chosen tests are de-noted by a green square.

Importing Files from Other Locations


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Topic 3 In this topic you will look at some typical measured stress data within GlyphWorks.

• From the Available data tree, expand the Time Series branch and find the data called vib (dac).

• Expand vib(dac) to see all 5 data channels.

• Drag vib(dac) onto the work space to create a

new Time Series Input glyph and click the Display option to see the plots.

• Press the glyph Maximize button to make the

display full screen

• Use the mouse and Tool bar to zoom in and

out of the plots. Notice the horizontal slider bar over the plot and see how you can scroll through the data by moving it.

• Experiment with Cursor coordinates option on

the Tool bar. Click on the plot and read various data values.

• Use the Next / Previous Channel buttons to

navigate through all the channels. Notice there are 5 channels in this data.

• Try using overlay and cross plots from the Tool

bar.

• Use the properties option to display all 5

channels simultaneously.

• Zoom-in on a small region of data and experi-

ment with the different line styles from the properties options.

• When you are quite comfortable with the

graphics options go to the next task.

You can drag data directly from the data tree onto the workspace. GlyphWorks automatically recognizes the type of data and provides the appropriate data import glyph. The Time Series glyph has a preview Display option that provides an interactive plot of the data on the glyph. This can be viewed full screen by pressing the glyph maximize button in the top right hand corner of the glyph.

You can zoom in on a block of data by simply clicking either side of the area you are interested in or by dragging the mouse over a window of data. All channels will automatically zoom in on the time interval cho-sen.

Full plot

Full X or Y

Round Y axis to nearest whole

number

Switch between Log and linear axes

Zoom in-out on X and Y axes

Most commonly used graphical display options are available from the Tool bar shown below. This will allow you to quickly zoom on the full plot or zoom in and out in stages. You can scroll through your data and change the axes between log and linear. You can also scroll through multiple data channels using the Next / Previous channel buttons.

Graphical Display Tool bar


Y axis limits

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Separate, overlay or cross-plot multiple

channels

Cursor tracking of data points

Change font size

Select or de-select all data points

Refresh Show cursor coordinates

Scroll bar on/off

Next / previous channel

Next / previous section of data

Channels can be incremented / decre-mented in order by one channel at a time or by groups of channels. So if channels 1-4 are currently showing, the next group would be 5-8. The mode of increment / decrement is chosen by holding the button down and then picking from the menu shown.

Frequently used graphical options are shown on the Tool bar while the remaining options can be found in the glyph properties panel by right clicking on the plot and selecting Properties… from the menu. The Plot-ting options are listed under the XY Graph tab as shown below.

The properties are all listed under generalized headings shown in the left hand tree menu. Click the heading required and the available properties for that appear in the right hand pane. Press the OK button to apply the properties.

Style Options These let you edit the appearance of the graphical plots. Channels can be shown separately, overlaid on the same axes, or as a cross plot of two channels showing the value of one on the X axis and the other on the Y axis. You can display up to 16 channel plots in one display; although the default is usually set to 4. You toggle between successive channels using the Next / Previous Channel buttons. Various axis, grid and colour options are also available from this form.

Data Lines This allows you to change the appearance of the plot. You can change colour, pen thickness, the style of line and the shape of any markers used. Markers are used to highlight the data making it eas-ier to distinguish between channels. Markers do not represent meas-ured points on the data as there are usually too many to plot neatly. However, you can choose the Points option to show the actual meas-ured points if required.

Labels To change the label headings, fonts and the style of numbering.


Axis Limits These allow you to vary the format and limits of the axes. The format can be Log or Linear, and the limits can be set precisely by entering the desired range.

Number of graphs plotted

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Topic 4 In this topic you will set up a very simple rainflow analysis and view the results in a 3D histogram. You will then learn how to use analysis glyphs with Pipes to channel data through a calculation.

• Using the data you loaded in the previous

task, drag the Rainflow Cycle Counting glyph from the Signal glyph palette onto the work-space and connect this to the Time Series glyph from the previous topic.

• Drag a Histogram Display glyph on to the

workspace and connect this to the output of your Rainflow Cycle Counting glyph. Right click on the Rainflow glyph and select Proper-ties… from the menu. Look at the various op-tions available. Right click on any option and select Help on Property… from the menu to see what this option means. Don’t worry if you don’t understand something at this stage, the Rainflow glyph will be considered in greater detail later.

• Run the process to see the rainflow matrices

• Maximize the Histogram Display and change

its properties to show a single channel.

• Rotate the 3D histogram by dragging the

mouse and see how it follows the mouse.

• Use the toolbar buttons to see the different

plotting options available.

Input data flows into the glyph

Output data flows out of the glyph

Input pads on the left

Output pads on the right

All connectors are colour coded to represent the type of data that passes

through them

Using Glyphs and Pipes Glyphs are pictorial representations of analysis components. There are input glyphs, output glyphs and various function glyphs. Input glyphs provide a source of data, like a link to a time history file; Output glyphs provide a sink for the data, either by graphically displaying the data or by writing to the file system, and Function glyphs take raw data in and pass process data out. The glyphs are divided into multiple palettes. Basic glyph operations and utilities are found under the Functions palette: other glyphs are grouped under their own product options such as; Signal, Frequency, Fatigue, etc. Glyphs are linked together using Pipes connected to pads on the glyphs themselves. Data flows into a glyph through the left hand pad and flows out through the right hand pad. Input glyphs only have pads on the right and output glyphs only have pads on the left. Pads are colour coded to represent the type of data that flows through them, The colour codes are shown below. Pipes can only be used to connect like coloured pads. (Note Grey pads can take any data.) To link two pads with a pipe simply click on one of the pads to be joined, GlyphWorks will then highlight all the compatible pads on the other glyphs. Simply move the mouse over the other pad and click on it. Pipes can be removed by right clicking on the pipe and selecting Dis-connect from the menu. Alternatively, pipes are automatically removed if you delete a glyph. Quick Tip: You can drag a glyph from the glyph palette and drop it on the pad of another glyph. This will automatically create a pipe between them if they are compatible.

Blue Red Green

Grey

Time Series Data Histogram Data Metadata Any Data Type

Colour conventions used for Pads

Maximize Plot


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Number of graphs plotted

Advanced Properties These options are available by right clicking on the plot and select-ing the Properties… option from the menu. The advanced property form is similar to that seen earlier for the Time History Display; how-ever, you’ll notice some features specific to 3D plots located on the Styles—Plots option shown below.

Viewing Options: Isometric, top, left & right

View as block histogram or surface plot

Full Plot

Scale all plots to the same axis

limits

Change font size

Select or de-select all data points

Next / previous channel

Channels can be incremented / decre-mented in order by one channel at a time or by groups of channels. So if channels 1-4 are currently showing, the next group would be 5-8. The mode of increment / decrement is cho-sen by holding the button down and then picking from the menu shown.

Top view of a Surface plot

The number of displays shown on each plot can be changed from this option form. The rotation angle and zoom can also be selected here. This is changed interactively by dragging the mouse over the plot; however, more control is offered by allowing you to type in the numerical angle required. The graph can be shown using histogram towers or as a surface plot while colours can be used in different ways to enhance the display.


To interactively Rotate the Graph, simply left click on the plot and drag the mouse in the direction required.

3D Histogram Display

The rainflow results are shown using the 3D histogram display glyph. This can display up to 8 interactive histograms in one display. The glyph automatically configures the display to show up to 4 plots for multi-channel data. The number of plots shown can be changed using the Advanced Properties options (right click). Maximize the plot by clicking on the button in the top right hand cor-ner of the glyph. A typical plot is shown below. It can be rotated by clicking and dragging the mouse. The most common options are pro-vided on the Tool bar shown at the bottom of the page. If you get a little lost after dragging, press the Isometric button to return to normal. Another useful option is the Top view option. This makes for a particu-larly pleasing graph when used in conjunction with the Surface option.

Hint: If you want to clear the input files and look at another set of data then simply right click on the TSInput glyph and select Remove Tests from the menu. You can now drag another set of data from the Available data tree and drop them on the TSInput glyph.

You can also drop several tests on the TSInput glyph at once; however, you can only view one test at a time.

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Topic 5 In this topic you will export Data to both Binary and ASCII output files.

• Using the GlyphWorks process created

in topic 3, add the Histogram Output glyph and rerun the process.

• Edit the GlyphWorks process and add a Data Values Display glyph. Export the histogram to a CSV file and look

at this file in MS Excel.

• Edit the GlyphWorks process and view

the numerical values of all channels in

the input time series using the Data Values Display glyph.

Binary Data Output

Output data from GlyphWorks can be written in a file for later use by other programs. This might be report generation using ICE-flow Studio, test rig drive signals, input to FE-Fatigue analysis or Multi-body simula-tion, etc. Binary data is output using the appropriate output glyphs; these being Time Series, Multi-column or Histogram data. This GlyphWorks process carries out a Rainflow Cycle Analysis on the data and stores the rain-flow histograms to file.

By default, the Output glyph uses the same filename as the input but appends ‘_out’ to the end of the filename. You can change the output name via the glyph Properties. Right click on the glyph and select Properties to get the options below.

Select type of Binary format to use: either DAC or S3 format. Metadata can also be stored with the plot. Metadata

contains information on the settings used in the analy-sis as well as statistical quantities, titles and units.

GlyphWorks can be instructed to auto-matically overwrite existing files or ask for user confirmation first.

The new data can be added automati-cally to the Data tree if required; other-wise you must press the data refresh button to load the new data.

The output filename is automatically assigned based on the following options: 1. Use existing filename with a given suffix 2. Use existing filename with a given prefix 3. Use a new filename 4. Use the existing filename but write to another directory


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ASCII Data Output

Data can also be written to ASCII files for use by programs that cannot read Binary files; these include FE analysis packages, Spreadsheets, Mathcad, Matlab, etc. ASCII data is output using the Data Values Display glyph. This can be used to give a numerical view of the data on screen but is also able to output to CSV (Comma Separated Values) files by clicking the Export button on the glyph. You will then be prompted for a filename.

Enter the channels to export. Channel numbers can be written as a comma sepa-rated list or using a range of channels expressed in the form; ‘1-3’ , etc...

Select the preferred bin labels for histo-gram data. Cells can be labelled using the center value or the minimum value covered by the bin.

Enter the number of significant figures to use for each value. In-ternally GlyphWorks maintains a precision of approximately 12 sig-nificant figures but this is probably excessive and can result in large file sizes.

This only applies to Time Se-ries and Multi-column data. You can page through the data by the number of points defined by the increment value.

Remember you can view help on any of these options by right-clicking on the option and se-lecting Help on Property… from the menu.


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There are many different ASCII based file formats around and it’s difficult to create a generic translator that is able to automatically discern one format from another. Typical formats include: ASC, TXT,

CSV (Comma Separated Values), XML, etc... ASCII Translate is an interactive Wizard that guides you through the translation and allows you to see the data before you commit to the translation. If you have a lot of data in the same format you can save your translation settings and run them automatically. The CSV file shown here contains measured Time Series strain data from three legs of a strain gage rosette. Time Series data is recorded at a constant sample rate, in this case 409.6Hz as given in the file header. You’ll notice that Time Se-ries data contains no separate time column as the time of any point can be evaluated using the formula below:

ASCII Translate can also be used to translate data with a non-constant sample rate. Obviously you would need a time column for this type of data. This is referred to as multi-column data as apposed to Time Series data. ASCII Trans-late is able to read time and date stamped data, this is very common for long term measurements such as extreme environmental loading, etc.

Topic 6 In this topic you will import data from a typical ASCII file using the ASCII Translation tool.

• Click the ASCII Translate application

from the Processing tool box on the left hand side of the ICE-flow desktop.

• Browse for the file StrainRosette.csv and select the

Convert to Time Series option. Press Next to continue to the next wizard form.

• Stretch the form so you can see a number of lines in

the data preview window. Select Number of header lines = 12, Line number for channel titles = 5, Num-ber of channels = 3, Line number for units = 7, Fields = Comma Separated. Press Next to continue to the next wizard form.

• Enter Sample Rate = 409.6 and press Translate But-

ton to complete the translation.

• View the data in GlyphWorks as Tutorial 1.3

nftt n ⋅∆+= 0Where: tn = time of the nth data point t0 = Time Base (starting time usually 0sec) ∆f = Sample rate

Enter ASCII filename for translation. This can con-tain any format of ASCII data including CSV (Comma Separated Val-ues), Space separated, tab separated, etc.

Time Series files have no separate time axis. The time for each point is calcu-lated knowing the starting time (Base Time) and the constant Sample Rate. Multi-column files have a separate time axis that doesn’t have to be meas-ured at a constant sample rate and may also contain a date column for long term measured data.

Previously saved Setup Files can be loaded here to preset the Wizard values and automate the translation. The option to save a setup file is offered on the final wizard form.

Header lines are included at the top of most ASCII files to describe the data present and provide other information like the sample rate, units, etc. ASCII Translate needs to know how many header lines are present so it doesn’t confuse these with the actual measured data. It can also use some of this data to automatically assign units and titles to the plots.

There are many ways of formatting ASCII files and it is not feasible to detect the format automatically. ASCII Translate provides a data pre-view window so you can choose the formatting options from the Fields and see whether they are satisfactory.


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Binary vs. ASCII Files

Time Series data does not contain a separate time column so you must enter the Sample rate and time base (starting time). The actual time values for each point can then be calculated.

You can then change the label and units used for the X-axis. By default these are as-sumed to be Time measured in Seconds.

ASCII Translate produces a binary output file that can be used directly by GlyphWorks. GlyphWorks can read a number of binary formats and you can select the preferred format and filename. The filename de-faults to the original ASCII filename while the format defaults to the GlyphWorks standard format. The choice of format largely depends on your requirements and is discussed in the information box opposite.

ASCII files are very popular because they can be read using a simple text editor, like MS Notepad®, and are universal between Operating Systems. Binary files, in contrast, are very specific to the computer platform they were created on. Not withstanding these benefits, binary files are generally pre-ferred when measuring and analyzing engineering data. Binary files are typically half the size of equivalent ASCII files. This is because numbers are stored in ASCII using 1 byte per digit, therefore requiring 8 bytes for 8 significant figures. Binary files, however, use a more precise storage mechanism enabling the same degree of numerical precision to be achieved using only half the memory.

Common Types of Binary Files DAC files are compact, versatile and provide accuracy to ap-proximately 8 significant figures. All channels are stored in sepa-rate files which can cause problems managing filenames. RPC files can contain multiple channels within the same file; however, this is stored in a multiplexed format making access rather slow and preventing direct manipulation of the length of the data. RPC files store data as integer numbers and although this reduces the file size it also reduces the numerical precision to approximately 5-6 significant figures. S3 files can also contain multiple channels and these are stored in a non-multiplexed format allowing versatility and speed. Data is usually accurate to approximately 8 significant figures; how-ever, multi-channel data is stored with a precision of approxi-mately 12 significant figures. This is the preferred format.

At the end of the translation a summary log is produced so you can make sure everything went as expected. You also have the option to Save the Setup file so you can re-run the conversion quickly on other similar files.

Translating Multi-column data

Multi-column data always requires a time column and optionally a date column where data is measured over a long period of time. The translation process is the same as for Time Series data except a Wiz-ard form is offered for you to enter whether the data contains date as well as time, and the date formatting used. You must also enter which col-umns pertain to the time and date.


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Basic Signal Analysis

Learning Objectives This tutorial introduces you to signal analysis in GlyphWorks with a particular emphasis on detect-ing anomalies (or errors) in the data. The Rainflow glyph will be used, along with the Amplitude Probability Distribution, the Frequency Analysis glyph and the Statistical Analysis glyph. These glyphs will be used to identify anomalies such as spikes, electrical line interference, clipped data, signal drift, inadequate signal length, aliasing, etc.


Topic 1 – Eyeballing your data. Look at the time signals

Topic 2 – Time at Level analysis. Use GlyphWorks to identify amplitude anomalies in a signal

Topic 3 – Carry out a Rainflow Analysis. Use GlyphWorks to identify spikes in a signal and deter-mine whether the duration of data is statistically sufficient

Topic 4 – Frequency Analysis. Use GlyphWorks to identify electrical line interference in a data file

Topic 5 – Statistical Analysis

Pre-requisites You must have completed tutorial 1. The following tutorial files must be installed in your working directory:

• Strain.dac

• Clipped.dac

• Drifting.dac

• Spiked.dac

• Mains.dac

Tutorial 2—Basic signal analysis in GlyphWorks

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Strain.dac

If you zoom-in on strain.dac you’ll notice that the data seems poorly resolved. There are too few data points recorded to confi-dently determine whether you have all the required peak values. This is a good example of under-sampled data. You will need to remeasure this data at a higher sample rate. Later you can see how this information is obtained more readily using the Frequency Spec-trum glyph.

Spiked.dac

As its filename suggests, this data contains elec-trical spikes. These are usually characterized by a single freak point of great amplitude. Spikes are more clearly dis-cerned by the Rainflow histogram that will be discussed later in this tutorial.

Drifting.dac

This file contains a steady drifting of the mean value of the sig-nal. In this case it arises because the strain gage is expanding at a differ-ent rate to the compo-nent as the temperature increases. You will see this later in the Ampli-tude Distribution as well.

All these anomalies and many more can be quickly detected using the techniques discussed in the next few pages. Read on to see

how GlyphWorks can help!


Topic 1 – Eyeballing your data In this topic you will use the TSInput glyph to browse

through the data files you will be considering through-

out this tutorial chapter. You will learn how to carry out

a methodical scan of the data and look for problems like data drop-out, drifting, clipping and poor numerical

resolution.

• Start a new GlyphWorks worksheet by clicking on

the menu item File/New Process

• From the available data tree, drag the time sig-

nal file strain.dac on to the workspace and notice

how GlyphWorks automatically identifies the

type of data and provides the appropriate input glyph.

• Click on the Display option and maximize the

plot using the button

• Zoom-in on the data and scan through it using

the slider bar at the top of the plot. What do you

think about the quality?

• When you have finished, shrink the plot back

again using button and then right click on

the TSInput glyph and select Remove Tests to

clear the selection. You’re now free to pick up another file and drop it on the glyph. You could

of course create lots of separate glyphs by drag-

ging and dropping each file on to the workspace

but it can get a bit messy that way.

• Repeat the exercise by looking at clipped.dac

spiked.dac, drifting.dac and mains.dac. Remem-

ber to zoom-in at different levels because some

anomalies can only be seen at certain scales.

Check here to display the time signal

Maximize Display

Is this plateau real or have the peak value been missed here? Are there sufficient data points to accu-rately record this strain cycle?

22

Example of Good Data

The above illustration is typical of GOOD data. In this particu-lar case a symmetric ‘Bell’ shaped curve following the Gaus-sian Normal distribution is noticeable. It is expected to see this shaped curve for random vibration signals.

Example of ‘Clipped’ Data

Clipped data is where the real data values exceed the full scale limits of the calibrated acquisition unit. For example, suppose you configure your data acquisition unit to measure strain in the range –1000me to +1000µε. Any values out-side this range are ‘clipped’, so instead of recording the ac-tual values the acquisition unit gives a false reading. For val-ues greater than 1000µε the unit will always read exactly 1000µε, and similarly for values less than –1000µε the unit will return values of exactly –1000µε. This causes a sudden

Topic 2 –Time at level analysis In this topic, the Time at Level and Amplitude Prob-ability Distributions (PDF) with particular emphasis on using them to quickly identify signs of ‘clipping’,

‘spikes’ and signal drift in the recorded data will be discussed.

To run the analysis you will create a simple calcula-

tion process similar to the one illustrated below.

• From the available data tree, drag the time

signal file strain.dac on to the workspace.

• From the Signal palette, drag the Amplitude Distribution glyph on to the workspace and

attach it to the input file glyph.

• You now need some way to plot the data, so

drag the XYDisplay glyph from the Display

palette and attach it to the Amplitude glyph output as shown in the illustration below.

• Run the analysis and look at the shape of plot

produced. This is an example of GOOD data.

Clipped Data • From the available data tree, drag the time

signal file clipped.dac on to the existing TSInput glyph and rerun the analysis.

• Click on the XYDisplay glyph then click the

Full Plot and Zoom-out buttons

to reset the axes and better see the clipped data.

Drifting Data • From the available data tree, drag the time se-

ries file drifting.dac on the TSInput glyph and

rerun the analysis.

• Click the XYDisplay glyph and then click the Full Plot and Zoom-out buttons.

Save your Workflow • You can now save the workflow you have just

created to a process file by using File/Save Process, and giving it a name. You can then use

this process again without having to set it up from scratch.


23


increase in the number of 1000µε and –1000µε records logged which are easily seen in the Amplitude Distribution. It is very common to miss clipped data if you only refer to the time signal plots.

Example of ‘Drifting’ Data

Long term drift in data is also a common problem. Drift can

occur in strain gage data through effects such as temperature changes during the measurement. If temperature variations are likely to be significant during the test then it is advisable to use strain gages that have the same coefficient of thermal expansion as that of the component being tested. Otherwise you’ll have to correct for the drift later in GlyphWorks.

Accelerometers data can also exhibit drift and this again can be corrected using GlyphWorks.

Of course, some drift might actually be due to real artefacts of the data so it’s always important to assess what’s real from what’s anomalous. You can compare the measured data with other data measured previously or try to think what hap-pened during the test that might contribute to the drifting result that you can see.

What are ‘Time at Level’ and ’Amplitude Probability’ Analysis?

Time at Level analysis is illustrated in the diagram be-low. The amplitude of the time signal is split up into a number of ranges called bins. The duration over which the time signal occupies each bin is calculated and then presented in the form of a bar chart. Time at level analy-sis is useful for determining the statistical amplitude content of a signal and can be used to detect anomalies such as signal drift, spikes, clipping, etc.

Although Time at Level analysis is useful it is more com-

mon to represent the data as an Amplitude Probability Distribution Function (PDF). This is simply a way of ‘normalising’ the time at level plot so it does not change shape when the length of time history changes or the number of bins is changed. To do this you can plot the values as a histogram instead of a bar chart. In histo-gram format the area of a column represents the time at level instead of the height. This means that the plot does not change shape as you increase the number of bins. Next you can divide the time at level by the total time of the signal, therefore it always remains the same height irrespective of signal length.

You are now able to compare the Amplitude PDFs for any signal irrespective of signal length or bin resolution.

Time spent at level

Time at level Seconds0.04

0.06

0.08

0.1

0.12

0.14

Time Seconds0.2 0.4 0.6 0.8 1

The time spent at each amplitude level is calculated and presented in a bar chart.

24

The simple Rainflow analysis process is illustrated above. The 3D rainflow histogram shows cycle range on the x-axis, cycle mean on the y-axis and number of cycles on the z-axis.

You can familiarize yourself with the characteristics of a rain-flow histogram by maximizing the histogram with the button

and changing the viewing angle with the other but-

tons as follows:

Example of Short Data

Look at the Rainflow histogram of the data in strain.dac. Have a close look at the tip of the plot. These high range cycles create exponentially more damage than the lower range cycles and effectively dominate the fatigue damage. Look at the Top view and the Left view and notice how sparse the data is in this region. This plot would suggest that most of the damage would be attributed to only a few cycles. You then need to confirm whether this is really representative. If you were calculating the fatigue damage on an anti-roll bar for example, then these data could be representative as most of the damage could be attributed to a few high load events like pot hole or curb strikes; however, if this data is from a vibration source, like a component on the engine for exam-

Topic 3 –Rainflow analysis In this topic Rainflow Cycle Extraction is discussed

and shown how it can be used to identify ‘spiked’ data and ‘short data’ where the sample length is in-

sufficient for high confidence results.

To run the analysis you will need to create a simple

calculation process similar to the one illustrated be-

low. You can create a new process if you wish, or you can drag the new glyphs on to your existing work-

sheet and build up a more complex analysis that con-siders both Time at Level and Rainflow.


signal file strain.dac on to the workspace or alternatively drop it on your existing TSInput glyph.

• From the Signal palette, drag the Rainflow

glyph on to the workspace and attach it to the

input file glyph.


drag the Histogram Display glyph from the Display palette and attach it to the Rainflow

glyph output as shown below.


produced. This is an example of SHORT data.

Other tasks • Repeat the analysis using spiked.dac,

clipped.dac and drifting.dac.


Isometric

XY axes (Top)

XZ axes (Left)

YZ axes (Right)

Surface plot

Tower plot

25


ple, then the sample length is probably too short to calculate any result with confidence.

Example of Spiked Data

Using the data file spiked.dac you can see a slightly different shaped plot. Please notice that most data is concentrated in the low range area and a few points are scattered in the ex-treme ranges. If you considered a fatigue analysis on this data you would observe nearly all the damage arose through only one very large amplitude cycle. You would then have to confirm whether this is really representative and statistically significant.

A more likely explanation is the presence of ‘spikes’ in the data. These are very common in strain gage signals where the data, usually measured in milli-volts, can easily be corrupted by external electrical noise. In this case it is very important that you effectively identify the high amplitude noise and remove it before proceeding with the analysis.

Example of Clipped Data

Clipping will artificially curtail the data to a lower range of values than is really present and so clipped data often ap-pears as a concentration of cycles at the extreme range as shown below. However, this is not always the case because some signals, for example might be clipped at only the maxi-mum values and therefore yield varying cycle ranges that might be overlooked. The ‘Time at Level; or ‘Amplitude Prob-ability Distribution’ is still the preferred identifier.

Example of Drifting Data

Drifting data is usually identified by a skewness in the mean distribution (Right view) in a similar fashion to that seen in the Time at Level plot. Compare the Rainflow and Time at Level distributions for the data in drifting.dac and notice the similarity.

What is Rainflow analysis?

Rainflow cycle counting is the cornerstone of fatigue analysis. A quantity of fatigue damaging energy is re-leased when the stress is cycled into tension and back again. Rainflow cycle counting is the method used to ex-tract these cycles from a time signal. As the amplitude of a cycle increases, its fatigue damage content raises expo-nentially.

Rainflow Cycle counting will be covered in more detail in

in later Fatigue Tutorials. The subject is covered in detail in nCode’s training courses.

In addition to fatigue analysis, Rainflow cycle counting can also prove valuable as a quick validity check on time signal data. It produces a 3D histogram that resembles the shape of an arrowhead for good data. The diagram can be used to spot spikes, check that the sample length was suf-ficiently long, and examine the amplitude PDF all from one diagram!

26

Topic 4–Frequency analysis This topic discusses frequency analysis and shows

how it can be used to identify electrical line interfer-ence and the likelihood of aliasing.

To run the analysis you need to create a simple calcu-

lation process similar to the one illustrated below. You can create a new process if you wish, or you can

drag the new glyphs on to your existing worksheet


signal file mains.dac on to the workspace or

alternatively drop it on your existing TSInput glyph.

• From the Signal or Frequency palette, drag the

Frequency Spectrum glyph on to the work-

sheet and attach it to the TSInput glyph.


drag the XY Display glyph from the Display palette and attach it to the Frequency Spec-trum glyph output as shown below.


produced. This is an example of electrical line

interference data.

• Repeat the analysis using strain.dac and con-

sider whether this data could be aliased.


Example of Electrical Line Interference

The simple Frequency Analysis process is shown above. Fre-quency in Hz is given along the x-axis while the mean square amplitude content in each frequency band is given on the y-axis. You can observe a spike located at 50Hz which inciden-tally coincides with the electrical line frequency used throughout the UK. It is quite common to see electrical line interference in lab based data acquisition units arising through an earthing problem. This type of anomaly can be easily rectified by GlyphWorks provided the maximum real frequency is well below the electrical line frequency. If the real frequencies encroaches on the line frequency then it’s hard to distinguish the real data from the anomalous. This topic will be discussed further in a later chapter.

Example of Aliased Date

Aliasing occurs if you select a sampling frequency that is very low compared with the frequency range of the signal you are

measuring. This is illustrated in the figure below.

In this example the measured signal is that of a unit ampli-tude sinusoidal wave of frequency of 10Hz. Sampled at 100Hz in the red plot, a good frequency representation is seen and only a slight loss of amplitude resolution with the maximum amplitude reads 0.95 instead of 1.0. As a general rule students are taught to over sample by at least a factor of 10x the maximum frequency in the signal to ensure satisfac-tory amplitude resolution. The slight reduction in amplitude arises because you didn’t sample at the exact moment the

27

What is Frequency analysis?

Frequency analysis is founded on the principles postulated by the French mathematician J. Fourier. He reasoned that all peri-odic time signals could be broken down into a number of sinusoidal waves of various frequency, amplitude and phase. When all the waves were later added together they would recreate the original time signal. Today the Fast Fourier Transform (FFT) algorithm is employed to carry out this frequency decomposition , and this is provided in GlyphWorks.

Frequency analysis data is typically presented in graphical form as a Power Spectral Density Function (PSD). Essentially a PSD displays the amplitude of each sinusoidal wave of a particular frequency. Frequency is given on the x-axis. The mean squared amplitude of a sinusoidal wave at any frequency can be determined by finding the area under the PSD over that frequency range. So, if you want to find the mean square amplitude of a 4Hz harmonic for example, you simply calculate the area un-der the PSD between say 3.5-4.5Hz.

The approximate amplitude of a sinusoidal component can be found from the equation:

This figure shows examples of four different PSDs. The PSD of a sine wave is simply a spike centred at the frequency of the sine wave. The area under the spike represents the mean square amplitude of the sine wave.

A ‘Narrow band’ process is one that covers only a narrow range of fre-quencies. This is easily seen in the PSD.

A ‘Broad band’ process is one that covers a wide range of frequencies. This might consist of a single, wide spike or a number of distinct spikes as shown in the diagram.

A ‘White noise’ process is an ideal signal with equal amplitude content

for all frequencies. It is commonly used when preparing drive signals for tests.

PSDs are useful for detecting resonance in components, aliasing in the data, frequency interference, etc. This subject is cov-ered in more detail in nCode’s training courses.

amplitudesquaremeanAmplitude ⋅≈ 2

Time history PSD

0 5

0.5

0.5

0 5 10

frequency Hz

Sine wave

0 5

2

2

0 5 10

0.5

1

frequency Hz

Narrow band process

Time history PSD

0 5

5

5

0 5 10

0.5

1

Broad band process

frequency Hz

0 5

10

10

0 5 10

1

2

frequency Hz

White noise process

∞


sine wave reached its zenith.

The blue plot shows what happens when you sample at only 25Hz (2.5x maximum). You can still see frequency and phase information but the amplitude is now lost. Reducing the sam-ple rate to only 12.5Hz results in the green plot showing now an incorrect frequency response too. This frequency ’Aliasing’ error will occur when the sample rate is reduced below a fac-tor of 2x the maximum frequency of the signal; this is known as the Nyquist limit.

You can now use the Frequency Analysis plot to check whether aliasing is likely to be a problem with your data. Switch the y-axis to ‘log-scale’ and look for the highest fre-quency of the recorded data before it disappears into very low level noise. Now compare this with the maximum fre-

quency on the x-axis (the Nyquist limit) and make sure there’s a factor of 5x between the two. If there is then you’ll probably be fine, if there isn’t then make sure an ‘anti-aliasing’ filter was used during the original data acqui-sition, otherwise your data could be seriously compromised!

This data could be unde r - samp led and might show aliasing errors!

28

Topic 5–Statistical Analysis

This topic discusses statistical analysis and shows how it can be used to very quickly ascertain whether data is good or bad and whether it is comparable to that measured before. To run the analysis you will need to create a simple calculation process similar to the one illustrated be-

low. You can create a new process if you wish, or you can drag the new glyphs on to your existing work-

sheet


signal file strain.dac on to the workspace or

alternatively drop it on your existing TSInput glyph.

• From the Function palette, drag the Statistics glyph on to the worksheet and attach it to the input file glyph.


drag the Metadata Display glyph from the Display palette and attach it to the Statistics glyph output as shown below.

• Run the process and in the Metadata Display glyph expand the branch ‘Channe1 Metadata’ and ‘Statistics1_Results’


Statistical analysis is a very revealing way with which you can compare measured data with measurements taken previously during other studies. Most engineers already know what the measured data should look like based on previous experi-ence. Next, you typically look at the overall range and mean and also compare the amplitude (Time at Level) and fre-quency content of the data and look for spikes. This compari-son is usually done visually by the engineer after the data has been downloaded to his desktop computer and it is often too late to re-measure anything that might have gone wrong. Ideally you will need some very simple numerical values that you can quickly compare while performing the test. Basic statistical analysis is ideally suited for this role.

GlyphWorks will calculate all the most commonly used statis-tical properties of the data. The information panels on the next two pages have been prepared to remind you what these properties refer to and how you can use them.

29

Statistical analysis

Statistical analysis is concerned with reducing a long time signal into a few numerical values that describe its characteristics. These are ideal when you need to quickly assess whether data is good or bad. Statistical properties can reveal anomalies like spikes, drift, clip-ping and an inadequate sample length.

The most common statistical quantities are based on the amplitude PDF discussed earlier in this tutorial. They describe the shape of the PDF in terms of its central tendency, spread, symmetry and area profile. These are illustrated in the diagrams below.

0

10

20

30

y

Amplitude PDF

Measures of Central Tendency: Give an indication of the mid value in the PDF

Median: the mid value with an equal number of points above and below

Mean: the average value or the center of area of the PDF about the x axis

Mode: the value that occurs most often, or the location of the peak of the PDF

0

10

y

Amplitude PDF

Measures of Spread: Give an indication of the width or range of values in the PDF. Used to quickly identify problematic data

Range = ymax - ymin Shows whole range of data, useful for assessing calibration of data acquisition equip-ment

Mean deviation Average deviation from the mean value, or the center of area about the mean ∑

=

−⋅=N

nnN yys

1

1

∑=

⋅=N

nnN yy

1

1

Variance Similar to mean deviation but take square of deviation from the mean rather than using the modulus operator. This is a smooth mathematical function and is preferable to the modulus although it is less intuitive.

( )∑=

−⋅=N

nnN yy

1

212σ

Standard deviation (σ) Take square root of variance to make dimensions consistent with the input units. Again this is less intuitive than the mean deviation but is the most commonly used measure of spread.


20

30

30


Combined measures of central tendency and spread: used to quickly identify problematic data

Mean Square (MS) Defined as the 2nd moment of area of the PDF about the x axis this measure is also known as the ‘intensity’ of the signal. It represents both central tendency and spread and is a very useful parameter for quickly checking a measured signal to ensure it is good. Any change in mean or range will be reflected in this parameter.

∑=

⋅=N

nnN yy

1

212

Root Mean Square (RMS) Take the square root of the MS to make the dimensions consis-tent with the input units. This measure is also used as a simple quality check on the measured data as any change in mean or range will be reflected in this parameter.

Measure of Symmetry

Skewness Defined as the 3rd moment of area about the mean, this measure is useful for assessing the degree of asymmetry of the PDF about the mean. This is useful for identifying signal drift, cyclic hardening, etc.

Mean

-veSkewness Zero +ve

Measures of Area Profile

Kurtosis Defined as the 4th moment of area about the mean, this measure is useful for assessing the likelihood of extreme values (outliers). A high Kurtosis value shows significant area in the upper and lower tails of the PDF at the expense of the mid portion, indicating a likelihood of extreme outliers. The normal distribution has a Kurtosis value of 3, distributions with a larger Kurtosis are more prone to extreme outliers.

Low Kurtosis High Kurtosis

Crest Factor Defined as the ratio between the absolute maximum value and the standard deviation, this measure is use-ful for assessing how ‘peaky’ a signal is. It is very similar to the Kurtosis value but is better for detecting a single extreme and possibly anomalous value, that might otherwise be averaged out with the Kurtosis method. This approach is useful for spike detection.

( )∑=

−⋅⋅

=N

nn yy

Nskewness

1

33

1σ

( )∑=

−⋅⋅

=N

nn yy

NKurtosis

1

44

1σ

( )σy

CFmax

=

31

Tutorial 2—What you have learned

In this tutorial you have learned how to view data signals in GlyphWorks and how to use various engineering analysis glyphs to perform the most common signal processing func-tions. You have paid particular attention to anomalies and have introduced some of the most revealing analysis tech-niques. You have considered:

• Time at Level and Amplitude Probability Distribution

• Rainflow Analysis

• Frequency Spectrum Analysis

• Statistical Analysis

Building up glyphs and saving your workflows

During tutorial 2 you have used three analysis processes. A strength of GlyphWorks is that you can concatenate virtually any number of glyphs to tailor your own analysis procedure. You are now able to combine all the analyses discussed here into one GlyphWorks process that you can use to rapidly check a measured time signal for anomalies. You can then save this process for reuse at any time.

If you have time, build a full anomaly detection process using the glyphs discussed here. You can add other functions if you need to. Have a look at the online manual for more informa-tion on all the available glyphs.

You can create automated reports using the Studio Display glyph. These can be exported to Microsoft® Word or HTML web pages as well as being printed on paper and being available for interac-tive viewing on the screen.

For more information on Studio report-ing please refer to the manual and also the Worked Example, ‘Creating a Studio Report in GlyphWorks’.


See glyph_wex.pdf page 236

See Studio.pdf

32

Data Manipulation

Learning Objectives In this tutorial you will take a real time signal that contains many of the anomalies previously dis-cussed and cleanse this so it’s suitable for use in a fatigue life analysis. The data was collected un-der actual working conditions from a strain gage attached to a cooling fin rotating on a shaft. The fatigue problem will be discussed, and then you will look at how to clean the data.


Topic 1 – Description of the data and how it was collected. This topic discusses the origin of the data and describe the problems experienced by the engineers during its collection.

Topic 2 – Extracting usable data from a time series file. This topic looks at methods for extracting sections of data from a much larger file.

Topic 3 – Graphical data editing. This topic uses the Graphical Editor glyph to manipulate the erroneous data and correct for a change in calibration.

Topic 4 – Detecting and removing spikes. This topic teaches how to use the Amplitude Distribu-tion and Spike Detection glyphs to detect and remove spikes from the data file.

Topic 5 – Removing signal drift and electrical interference from a signal. This topic teaches how to use the Butterworth Filter glyph to effectively remove unwanted frequencies from the data. These relate to a 50Hz electrical line interference and a low frequency signal drift.

Topic 6 – Calculating stress from gage results in strain. This final topic teaches how to use the Calculator glyph to convert the measured data from strain in me to stress in MPa.

Pre-requisites You must have completed tutorials 1 and 2. You’ll also need the file Sg.dac in your working direc-tory.

Tutorial 3—Data manipulation in GlyphWorks

Gly

phW

ork

s 3.

0—Tu

tori

al 3

33

Introduction to the design problem

This tutorial is based on a real engineering problem. It involves a failure investigation on a ducted shaft. The shaft is used to drive a particular machine and also transports high-pressure hot gasses through its core. Cooling fins are mounted along the length of the shaft as shown in the figure below. A steel ducting surrounds the shaft and cooled water is passed through it. The rotating fins circu-late the water through the pipe. The shaft rotates at a steady 1.48Hz.

The data sample was taken from a strain gage located adjacent to the root of the fin and measures the vibration loading at the root. The vibration load arises through the turbulent flow of the cooling water over the fin.

The data acquisition was fairly traumatic and the resulting strain gage data has known problems. The strain gages were not ther-mally matched to the fin material and therefore the strain readings change with temperature. This causes drift in the signal.

As the shaft operates at a steady temperature this is not a signifi-cant problem. The intention was to bring the shaft up to working temperature and then calibrate the gages. The calibration was com-pleted after 900 seconds but unfortunately the strain gage signal was lost after 1200 seconds and it would be imprudent to conduct an analysis on only 300 seconds of data. The cost of repeating the

test is high so the engineer wants to use the data already collected. You will therefore have to analyse the data and cleanse the signal so it can be used for fatigue analysis.

Following the test, it was noticed that an electric arc welder was also in use in the next room. This has introduced spikes in the data.

In this tutorial you will take the anomalous time signal and ‘cleanse’ it to recreate what you think the data should have been had the anomalies not been present.

To cleanse this data, please carry out the following manipulation steps:

1. Extract the intact signal from 0 to 1200 seconds and dis-card the region containing dropout

2. Edit the signal to remove the recalibration step

3. Identify and remove the spikes

4. Frequency filter to remove the low frequency drift and high frequency electrical line interference

5. Verify that the signal is long enough for a statistically repre-sentative fatigue analysis

When you have finished you will be able to use this signal in the fatigue analyses in the next tutorials.

Topic 1 –Description of the data and how it was collected

Before you start to analyze the data, it is good to know where it came from and why it was necessary in the first place. This topic discusses the origin of the data.

Before you move on to the next topic, you might want to run this data through your anomaly analysis proc-ess, developed in the previous tutorial, to see if you can identify all the problems.

Tutorial 3—Data manipulation in GlyphWorks

Measured Data

Cleansed Data

34

Tutorial 3— Data manipulation in GlyphWorks

Topic 2— Extracting usable data from a longer time signal file This topic looks at how to extract the good

data from the signal and discard the bad

data.

• Create a New GlyphWorks Process using

the menu File/New Process

• From the available data tree drag the

time series file sg.dac onto the analysis workspace.

• Click the Display box shown on the

bottom of the glyph to see a miniature

plot of the data.

• Enlarge the time signal plot by

dragging the bottom corner of the

glyph or pressing the maximize button at the top right hand corner.

• Select the good data between 0—1200

seconds, (see the adjacent information

panel).

• To see the results so far, drag the

XYDisplay glyph from the glyph palette

and link it to the output pad. You’ll notice that it contains only the good

data.

The Time Series Input glyph allows you to pick regions of data to analyse. This is useful in this example because the data after 1200 seconds contains erroneous drop-outs, so you can use this facility to highlight only the good data for analysis. The above flow shows how you can select the good data in the Input glyph and then demonstrates how this is passed on to following glyphs with the bad data being dis-carded.

This facility is also useful for separating a single event from a long data file. For example, you can switch on the data acqui-sition unit at the start of the day and then perform a number of tests one after the other without stopping the unit in-between. When you come to analyze the data, you might want to separate the long record into a number of shorter distinct events. This way you can compare the damage cre-ated by each event and quickly see where problems might arise with your product.

The Graphical Editor glyph is also used to select and manipu-late data. This has more advanced features than those seen here which you’ll see in action in the next task.

35


Selecting data in GlyphWorks—a few hints

There are several ways in GlyphWorks to select a section of data from a time history file.

• Hold down the control key and click and drag the section over the data. You will see something like the plot shown below on the left. You can zoom in on the 1200 second mark to refine the selection by dragging the orange selection square, as shown on the right. Hint: Don’t move the mouse cursor too quickly—let your computer ‘catch up’ with its movement.

• If you know the exact times at which the good data starts and finishes then you might prefer to enter these values numeri-cally rather than graphically. Right click on the TSInput glyph and select Properties from the menu. Now click on the Ad-vanced tab and notice the property called MarkedSections. This gives the time coordinates you specified in the graphical selection above. You can edit these values now for the exact time, 0—1200 seconds.

The syntax is always the same: enclose the range in curly brackets and separate the start and end points with a comma. You can enter any number of such ranges in the Marked Sections field as shown below. You can combine these methods by first of all mak-ing a graphical selection and then refining the coordinates by editing the numeric values in the Advanced Properties.

Hint: To ensure that you save the marked sections for re-use later, click Store Marked Sections = True. This is a real timesaver if all the input files have the same range, for example if multi channel input files all have spikes at the same point in time.

36

The GlyphWorks process for this task is shown above. The difficulty with this analysis is in selecting the exact start time for the recalibration. At full scale the width of the cursor can cover several seconds of data. The trick is to select the ap-proximate location and then zoom in on the start point (click either side) and drag the orange selection square until you have the exact point. This is shown in the plot opposite.

The only remaining task now is to change the properties so the Graphical Editor will undo the calibration offset. Change

the EditMethod to Scale&Offset and then select an offset of –5000µε. How do you know the offset was –5000µε? It was assessed visually by changing the Graphical Editor XY Graph property ‘Labels’ to YAxis=Label major, and the Styles prop-erty had the Grid box checked.

Topic 3—Graphical data editing This topics uses the Graphical Editor glyph to

remove the recalibration error in the data. You will be able to select the area of data to

be corrected and then tell the glyph to auto-matically rescale it.

• Disconnect the XYDisplay from the

TSInput glyph used in the previous task. (Right click on the pipe and select

‘Disconnect’ from the menu)

• From the glyph palette’s Signal menu

drag the Graphical Editor glyph onto the workspace and link it to the TSInput glyph.

• Re-connect the XYDisplay glyph to the

output of the Graphical Editor glyph so

you can see the result of the editing.

• Now press the Run button to pass the

input data through the Graphical Editor ready for processing.

• In the Graphical Editor, zoom-in to the

step and select all of the data from the step to the end of the signal.

• You must now tell the Graphical Editor glyph what to do with the selected data, in this case offset it by –5000me

in order to line it up with the rest of the signal. Right click on the glyph and set

the properties to: Edit Method = Scale&Offset Offset =-5000

• From the Advanced properties form

select ‘StoreMarkedSections’ = True, this

will remember these coordinates for future use.

• It is always a good idea to save your

flow regularly so do it now.


37


Topic 4 – Detecting and removing spikes

This topic looks at spike identification and re-moval. Three methods of spike detection are intro-duced and results compared. You then can see how to use the Graphical Editor glyph to display the spikes and automatically remove them.

This topic is split into the following parts:

• Using the Amplitude Distribution glyph to detect spikes

• Using the Spike Detection glyph and the Graphical Editor glyph to display and re-move spikes

• Using the Differential (or gradient) method

• Using the Statistical method

About spikes

Spikes are a common problem with strain gage data. You could manually edit the spikes using the graphical editor if you wanted. Simply zoom in on the spike and overwrite it with a ramp, for example. However this can become tedious where many spikes are present or where many channels of data have been recorded. For this rea-son, engineers have been looking for methods of auto-matically detecting and correcting spikes.

You can identify and remove spikes with a lot of help from GlyphWorks though, as you will see.

To date, no one has invented a completely reliable method to accurately detect all types of spike. Most methods seem over-sensitive and engineers themselves have differing opinions on what really is a spike. Many times, either too many or too few spikes can get re-moved from real data – each with consequences. The main detection methods available in GlyphWorks are:

• Amplitude threshold detection

• Differential (or gradient) threshold detection

• Statistical threshold detection

• Crest factor threshold detection

In order to use these you need to specify the appropri-ate threshold parameters. Each method is suited to a particular type of spike and you might have to use two methods to completely remove all your spikes.

Spike identification is very subjective and it is not rec-ommended that you rely on a purely automatic re-moval without first validating it. In the interactive mode shown in this tutorial, you choose the method and the threshold values and the program searches for all the spikes. A graphical view of the spikes is then presented so you can check whether they agree with the choice. Spikes can be removed graphically using this mode. The procedure is outlined below.

In automatic mode you enter the method and thresh-old values and GlyphWorks automatically removes any spikes from any number of time histories entered. The recommended approach is to process a few files inter-actively until you gain confidence in the method and threshold values. When you are confident then you can process the remaining files automatically.

It should be remembered that time histories taken from different locations or using different types of transducers, e.g. strain gages or accelerometers, will all exhibit different spike characteristics and will there-fore require different methods and threshold values.

In the following topics you will look at the theory of each method in turn and see how well they work with your data. You will then use the interactive mode to remove the spikes using the most appropriate method.

Choose a method and determine the threshold

values

Run spike identification and confirm the choice using the interactive

Graphical Editor

Agree?

NO Choose new

method / threshold value

YES

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The method is illustrated in the diagram below. The spikes are clearly seen to have an amplitude much larger than the rest of the data. Threshold values are then established that clearly tell the spike removal glyph how to discriminate spikes from real data. These thresholds are best obtained from the Amplitude Distribution. The high amplitude spikes are seen as individual points that lie outside of the main statistical distribution; the log axis emphasizes this.

In this example, some of the spikes are very easy to see as these have an amplitude that is much larger than the rest of the real data. You could then use the plot above to determine reason-able threshold values to use in the automatic Spike Detection glyph. A maximum value of 2000me is clearly appropriate and you could try a value of –6500me for the lower limit.

There are a few spikes you might be unsure about, however. These lie very close to the main statistical distribution and might therefore represent real data.

Topic 4a—using the Amplitude Distribution glyph to detect spikes

This is the simplest method for detecting spikes. It is suited to spikes that have large amplitudes compared to the rest of the data. If spikes have an amplitude comparable to the amplitude of the data, then this is not the method to use.

• Disconnect the XYDisplay glyph from the Graphical Editor

• From the glyph palette's Signal menu, drag the Amplitude Distribution glyph on to the output from the Graphical Editor

• Reconnect the XYDisplay glyph to the output of the Amplitude Distribution glyph

• Right click on the Amplitude Distribu-tion glyph and set the property: Analy-sis Type = PointCount

• Click on the XYDisplay glyph and change the Y axis to show log Y values.

On running the flow you will clearly see some spikes whose amplitudes lie outside the main statistical distribution.


Upper threshold

Lower threshold

Amplitude Probability PlotChoose appropriate threshold values by looking at the Amplitude Probability plot. Spikes lie outside of main statistical distribution.

Spikes Spikes

Spikes?

39

The Automatic Spike Detection glyph is designed to pick out spikes according to the method and threshold values chosen by you. It’s the job of the Graphical Editor to then remove these spikes automatically. The Spike Detection glyph there-fore has two output pads, the blue pad passes the unedited time signal through, while the orange pad passes on the lo-cations of all the spikes found.

The Graphical Editor has two input pads. In task 3 you only used the blue input pad to pass the time signal data. In this example you will also use the orange pad to pass the anom-aly data containing the spike locations. The Graphical Editor knows that it is connected to the Spike Detection glyph and therefore is required to ramp over the spikes. It clearly high-lights the spikes so you can see them and confirm whether or not you agree with the choices. You can edit the method and threshold values to refine the selection.

In this example you can see the amplitude method has cor-rectly spotted a number of spikes but has incorrectly marked some of the real data towards the end of the signal and has missed some of the spikes towards the beginning.

At this stage you could go into the Graphical Editor and manually select the spikes and then use the property Edit-Method = Ramp. Alternatively, you could try another ap-proach.


Topic 4b—using the Spike Detection glyph to automatically identify and filter spikes.

• From the glyph palette’s Anomaly menu, drag the Spike Detection glyph on to the output of the Graphi-cal Editor as shown.

• From the glyph palette’s Signal menu, drag another Graphical Editor glyph on to the output of the Spike Detection glyph.

• Ensure that both blue and orange pads are connected between the Spike Detection and the Graphical Editor glyphs. (The blue pad passes the time signal data, while the or-ange pad passes the spike informa-tion.)

• Right click on the Spike Detection glyph and select Properties. Use the following values: Method = Ampli-tude, YMax = 2000, YMin = -6500.

• Run the process to see the identified spikes. You could add another Dis-play glyph to the output of the sec-ond Graphical Editor to confirm these spikes have been removed.

40

Topic 4c—using the Differential (or Gradient) Method

• Delete the Amplitude Distribution and its XYDisplay glyph because you won’t need them in this exercise.

• Right click on the Spike Detection glyph and change the Method = Auto-Differential

• Run the flow and notice how few spikes have been identified by this method.

Rise/Gradient Probability Plot

Spikes

Choose threshold value by looking at the Rise Probability plot. Spikes lie outside of main statistical distribution.

Not all spikes have amplitudes significantly greater than the parent data. The signal below shows a time signal that con-tains some smaller amplitude spikes. You can identify these by comparing their gradient (point-point rise time) with that of the parent data. Spikes are typically characterized by 1 anoma-lous point and therefore have a very steep slope that can be seen on a Gradient Probability Plot.

GlyphWorks allows you to specify a threshold gradient above which the data is recognized as a spike. In many cases the threshold value can be obtained automatically so you do not need to do anything else.

This is one of the most reliable and simple spike detection methods. Unfortunately it is quite unsuitable for this analysis because there is a high frequency anomaly in the form of elec-trical line interference. If you zoomed into any part of the data you would see the very high frequency jagged profile. This gives rise to high gradients in the data and make it al-most impossible to distinguish the spikes from the electrical

interference. You could then choose to filter out the electrical interference first of course, but this would present an additional complication because it would also tend to smooth out the spikes making them even harder to dis-tinguish from the real data. You will have no option but to try an alternative method in this case.

At high magnification you can see the high frequency electrical interference that is confusing the spike detection analysis.


41

Topic 4d—using the Statistical Method

• Right click on the Spike Detection glyph, change the Method = Statisti-cal, and enter NumStanDevs = 4 and GateValue = 5

• Run the flow and notice how all the spikes have been correctly identified.

• Zoom-in to any of the spikes to get a good perspective and press the golden arrow buttons to toggle be-tween them. Do you agree with the choice?

The Statistical Method is slightly more sophisticated but is still straightforward and easy to use. It calculates a running stan-dard deviation (SD) of the data, and valid data is deemed to lie within a region defined by some multiple of the SD. This is illustrated in the diagram below.

The only problem with this method is choosing the threshold SD value to use. As a rule of thumb, a SD of 3 or 4 is usually a good starting point. GlyphWorks makes it easy to view the output so that you can quickly gage if the threshold is reason-able.

A problem might arise when several contiguous data points have the same value, as in the case of a horizontal line. In this case the running SD would tend towards zero and all points immediately after the line would register as spikes. A gating value is therefore assigned so that the minimum SD does not drop below this value. A gate of 5% - 7% is a usually a good starting point.

This method will find spikes with an amplitude comparable to the amplitude of the data. It is generally a very good method.

Calculate the running standard deviation. A spike is detected when a value passes through some multiple of this.

Zoom-in to any of the spikes to get a good perspective and press the golden arrow buttons to toggle between them. Do you agree with the choice?


42


Frequency Filtering

You now need to remove the very low frequency drift and the 50 Hz (UK) electrical line interference. There are a number of frequency filtering tools within GlyphWorks, and these are covered in detail in nCode’s training courses. The basic But-terworth Filter will only be looked at here because it is the most widely used.

There are two basic filter types available known as ‘Low Pass’ and ‘High Pass’. From these are derived the ‘Band Pass’ and ‘Band Reject’ filters. The graphic below illustrates the effect.

A frequency filter is used to remove frequencies from a time signal. In this example you will need to remove low frequency drift, below about 0.5Hz, and high frequency electrical line interference, roughly above 30Hz. It is alright to remove all frequencies above 30Hz as the PSD tells you that only electri-cal line interference is present above this value and no real data is affected. The lower limit is harder to choose because it is hard to see where the drift ends and the real data begins. Also filters do not cut in as sharply as the illustrations sug-gest. They tend to roll in gradually and therefore you will be quite likely to lose some real data. You can then conduct a quick check on this later to see if it is acceptable.

Frequency Hz

High frequencies get cutLow frequencies Pass through the filter

Frequency Hz

High frequencies Pass through the filter

Low frequencies get cut

Frequency Hz

This band of frequencies get cut all others pass through

Frequency Hz

This band of frequencies pass

through, all others get cut

Low Pass High Pass

Band Pass Band Reject

Topic 5a —Frequency filtering with a Butterworth Filter

This topic introduces the Butterworth Filter and shows how it can be used to remove unwanted frequencies from your data. Spe-cifically, this example shows how to remove the 50Hz electrical interference and the low frequency drift associated with the tempera-ture variation.

• From the Signal palette, drag a Butter-worth Filter glyph on to the output pad of the second Graphical Editor (see screen shot opposite)

• So that you can see the result, attach

an XYDisplay glyph to the Butterworth Filter’s output pad.

• On the Butterworth Filter glyph change the properties to: Type=BandPass Frequency1=0.5 Frequency2=20

• Run the analysis and notice the warn-ing in the Diagnostics panel.

• On the Butterworth Filter glyph change the property DCwarning = 0 to disengage the warning and rerun the analysis.

43

The Butterworth Filter glyph is renowned for its reliability, speed and ease of use. The glyph will issue a warning in the rare case where there might be a problem so you are aware of the fact and can perform the necessary checks.

In this example you must filter the very low frequency drift below approximately 0.5Hz. As this is below the glyph’s DC warning threshold of 2% of Nyquist, the process will error and force you to change the threshold limit. This is done to ensure you are aware of the possibilities of your actions.

You can then check the filter has performed properly by look-ing at the frequency content both before and after filtering and also by looking at the output time signal to make sure there are no strange amplitude modulations, etc. You can compare the PSDs by changing the XYDisplay properties to overlay and varying the line stiles as shown in the plot below.


See glyphref.pdf for a detailed de-scription of the Butterworth Filter.

Topic 5b—Comparing the Frequency Spectra

This topic compares the frequency content of the data both before and after the filtering. This will ensure that no spurious effects have been introduced while filtering below the warning threshold.

• Drag two Frequency Spectra glyphs from the glyph palette and connect them to the output of the second Graphical Editor and the Butterworth Filter glyphs respectively as shown be-low.

• Drag an XY Display glyph from the pal-let and attach a red input pad to each Frequency Spectrum glyph.

• Run the analysis to compare the two PSD plots.

• You can choose to overlay the two spec-tra by pressing the button.

44

GlyphWorks can carry out stress life (SN) fatigue analyses with stress based input files. As you will have noticed from the tutorials so far, the example time series file was collected from a strain gage, and is in microstrain.

So, in this topic you will convert it into stress units by apply-ing the formula:

(where 106 converts from microstrain to strain, E = 203400 MPa and represents the elastic modulus of the material RQC100 that is used in the later fa-tigue analyses.)

The Time Series Calculator is a very powerful scientific calcu-lator that can be used to edit data or create new data chan-nels by algebraically manipulating the existing channels.

Before using the calculator you should first connect it to the appropriate input glyph (the Butterworth Filter in this

case) and then run the flow. This step is necessary so the Calculator knows what data channels are available.

You can now right click on the glyph and select the Proper-ties option. This, unlike any of the other glyphs you have seen so far, is laid out like a proper scientific calculator. The top area lets you define the properties of your new data channel. Essentially you are taking one or more input channels and are manipulating these algebraically to create a new output channel. In this example you will choose to over-write the original input channel number 1 with the derived Stress data in units of MPa, as shown in the picture opposite. You could have chosen a new channel number if you’d wanted; in this case you’d then have both sets of data available for subse-quent analysis.

You can pickup the strain data from the Available Channels box by double clicking on it. The Available Channels box lists all the data channels that are available for use in your equa-tions, when you double click you’ll notice that that particular channel has now been added to the equation editor window ready for use in your equation.

You can now complete the equation just like any other calcu-lator. You can use the buttons provided or type the equation from the keyboard. When you have finished simply click on the button titled ‘Add to List’ and that equation has now been entered in the Currently Defined Equations list.

You can add any number of equations to the list and they are all evaluated in sequence from top to bottom. You can even use results in one calculation that were derived from an ear-lier one. To edit an equation at some later date simply select the equation from the Currently Defined Equations panel and press the ‘Edit’ button to move it into the editor panel.

E⋅= 610εσ

Topic 6—Calculating stress from strain using the Calculator glyph

This topic uses GlyphWorks’ Calculator glyph to convert the measured strain gage signal into stress for use in subsequent fatigue analyses.

• From the Function palette, drag the Time Series Calculator glyph onto the output pad of the Butterworth Filter.

• Drag a XYDisplay glyph onto the Cal-culator’s output pad so you can see the results.

• Run the process so the Calculator can see all the data first of all.

• Right click on the calculator and select properties, then edit the equation as

shown on the opposite page.

• Rerun the flow and see how the data has been converted to stress in MPa.

• Drag the Time Series Output glyph on to the Calculator output and change its properties so it saves a file called sg1_stress.dac for use in the SN fa-tigue calculation.

• Drag another Time Series Output glyph on to the Butterworth Filter out-put and change its properties so it saves a file called sg1_strain.dac for use in the EN fatigue calculation.

• Run the analysis and then press the

button to refresh the data tree.


45


Conclusion to tutorial 3

This topic has used many of the manipulation tools available in GlyphWorks. The highly imperfect input file you started with has been converted into 1200 seconds of viable data that can be used to run a fatigue analysis. The fact that it also exists in both stress and strain versions means that it can be used for EN and SN analyses.

You have already used many of the advanced features of GlyphWorks. In tutorials 4 an 5 you will carry out fatigue life predictions with what you have already created.

Channel number and descrip-tion of your new output data channel. You can overwrite an existing channel number if you want.

This box shows all the data channels currently available for use in your calculation. If you create a new channel then it will also popup here for you to use in subsequent equations.

This is the edit box where you’ll edit your equation

You can use these calculator buttons or type the equation on the keyboard

Your finished equation is added to the equation list displayed here. You can edit the equation at any time as well as change the order of calculation and remove the equation all together.

46

Stress Life (SN) Fatigue Analysis

Learning Objectives This tutorial introduces the SN Fatigue Analysis glyph in GlyphWorks 3.0. A brief introduction to fatigue theory is covered first, then you will learn how to apply it to the rotor blades that were ana-lyzed in tutorials 1 –3.

You’ll learn how GlyphWorks 3.0 can be used to predict the life of the blade, and to carry out:

• Sensitivity studies to ascertain how variations in inputs can affect predicted in-service life

• ‘What if’ analysis to see how varying the material influences predicted life

• ‘Back calculations’ in which the life is fixed and input parameters are calculated

You will also investigate the effect of notches and welds on fatigue life.


Topic 1 – An introduction to SN fatigue life prediction. After completing this tutorial you will understand the engineering principles behind SN fatigue

Topic 2 –SN fatigue life prediction with GlyphWorks 3.0. After completing this tutorial you will be able to enter all of the parameters for a fatigue life prediction

Topic 3 – Post processing fatigue. This tutorial examines the rainflow and damage histograms, and view time correlated damage plots and compare these with the original input file.

Topic 4 – Sensitivity studies, what ifs, and multiple calculations. After completing this tutorial you will know how to gage the expected variability in fatigue life.

Topic 5 – The effect of notches on fatigue life. After completing this tutorial you will understand how notches affect the fatigue life and how to use the stress concentration factor Kt to produce more accurate life predictions.

Topic 6 – Fatigue of welds. After completing this tutorial you will be able to perform fatigue analysis on welded joints.

Pre-requisites You must have completed tutorials 1 to 3. You’ll also need the file Sg1_stress.dac, created in tuto-rial 3, in your working directory.

Tutorial 4— Stress life (SN) fatigue analysis in GlyphWorks

Gly

phW

ork

s 3.

0—Tu

tori

al 4

47

In this exercise you will take the measured strain gage time signal from the cooling fin and use it to estimate the fatigue life of the component. The strain gage was posi-

tioned directly over the critical stress region on the cooling fin as shown below.

Location of strain gage

Quick overview of the SN method

The SN (or Nominal Stress) approach is the oldest method of fatigue estimation. The German railway engineer, August Wöhler, developed the method between 1852 and 1870. Wöhler studied the progressive failure of railway axles using the test rig shown to the right. He subjected two railway axles simultaneously to a rotating bending test. He then plotted the nominal stress versus the number of cycles to failure, which has become known as the SN diagram. Each curve is still re-ferred to as a Wöhler line. The SN method is the most widely used method today, and a typical example of the curve is shown to the right.

Several features are notable about the Wöhler line. First, note that below the transition point (approximately 1000 cycles) the SN curve is not valid because the nominal stresses are now elastic-plastic. This is sometimes known as ‘low cycle’ fatigue on account of there being a low number of cycles to failure. It has been shown that fatigue is driven by the release of plastic shear strain energy; therefore above yield, stress loses the lin-ear relationship with strain and cannot be used. This region is addressed by EN (or the Strain Life) method discussed later.

Wöhler's rotating bending fatigue test

Between the transition and the endurance limit (approximately 106 – 108 cycles), SN based analysis is valid. Above the endurance limit, the slope of the curve reduces dra-matically and, as such, is often referred to as the “infinite life” region. In practice, however, infinite life is seldom achievable. For example, Aluminium alloys do not exhibit infinite life, and even steel does not exhibit infinite life when subjected to vari-able amplitude loading.

The GlyphWorks analyser uses a “Tri-linear” curve to represent the Wöhler line. This is made up of three logarithmic segments relating to the low cycle (plastic) regime, the high cycle (elastic) regime and the ”infinite life” regime, respectively. Two typical SN curves are shown on the next page. These represent the low alloy steel, MANTEN by US Steel Corporation, and the high strength steel, RQC100 by Bethlehem Steel Corporation. The dotted line below 1000 cycles represents the low cycle regime. The change in gradient at 108 cycles shows the effect of the endurance limit.

Topic 1 – an introduction to SN fatigue life prediction

After completing this tutorial you will understand the engineering principles behind SN fatigue.


48

SN curves for MANTEN low alloy steel and RQC100 high strength steel

To calculate the fatigue life of a component, GlyphWorks needs the SN curve for the material and a time history repre-sentation of the varying stress field at the point of failure. This is commonly recorded using a strain gage signal similar to the one used in the previous tutorials. First of all GlyphWorks will carry out a rainflow analysis of the time signal to extract the fatigue damaging cycles. Then GlyphWorks will use the SN curve to determine the damage caused by each cycle and then sum the damage to calculate the total accumulated damage in the signal. This process is all automated as shown in the following exercises. For more information on fatigue theory refer to nCode’s Fatigue The-ory training course, and check the online manual.

Topic 2—SN Fatigue Life Estimation

This topic carries out a simple SN fatigue analysis to estimate the life of your compo-nent. The basic glyph process is easy to create; what is trickier is to establish the properties to use. To assemble the glyph flow:

• Start a new worksheet by selecting File/New from the menu.

• Drag the stress based time series file created in the previous tutorial sg1_stress.dac onto the workspace.

• Drag the Stress Life (SN) glyph on to the output pad of the Time Series Input glyph.

• Drag a Metadata Display glyph on to the Metadata output pad of the SN glyph (the green pad)

• Right click on the SN glyph and select Properties from the menu. Enter the calculation properties listed below.

• Run the process and expand the tree in the Metadata Display: Channel1 Meta-data / StressLife1_Results. Alterna-tively, to see the results in a table for-mat, right click on the Metadata Dis-play glyph and select Display Type = Results

S-N Data PlotMANTENSRI1: 1717 b1: -0.095 b2: 0 E: 2.034E5 UTS: 552 RQC100SRI1: 2199 b1: -0.075 b2: 0 E: 2.034E5 UTS: 863

1E2

1E3

1E4

1E5

Str

ess R

an

ge

(M

Pa

)

1E0 1E1 1E2 1E3 1E4 1E5 1E6 1E7 1E8 1E9Life (Cycles)


In the stress life glyph enter these properties:

Material Data

• Material Data Source = Properties

• Database Name = leave empty

• Materials Name = RQC100

• Surface Finish = Polished

• Surface Treatment = None

• Certainty of Survival = 50

Material

• Material Stress Units = MPA

• UTS = 863

• E = 210000

• SRI1 = 2199

• B1 = -0.075

• NC1 = 1E+8

• B2 = -0.039

• Standard Error = 0

Advanced

• Mean Stress Correction = Goodman

49

After you have run the process the Metadata Display glyph should be populated with fatigue analysis data.

The life should be 3.334E6 repeats of the input file. Given that the input file is 1200 seconds long that is a life of around 127 years!

Keep this glyph process on screen, or save it as it will be used again in the next topic.


Stress life in repeats of input file

50

Rainflow cycle counting has been discussed in earlier tutori-als. To review briefly, it is a process to extract fatigue-damaging cycles from a random time signal and when plot-ted in 3 dimensions can prove a valuable analysis tool in its own right. You will remember how you used this plot to iden-tify spikes and insufficient sample size.

Before any fatigue analysis is done, GlyphWorks will perform a rainflow extraction on the time signal. The plots for this are made available by connecting the output pads to the histo-gram display glyph, so that you can satisfy yourself of the adequacy of the input data.

You might wish to perform your own rainflow extraction us-ing the Rainflow glyph introduced in Chapter 2 ,and thereby by-pass the automatic routine in the Stress Life glyph. The Stress Life glyph has 3 input pads and data can be provided to any of these, although you cannot connect more than one at a time. The top (blue) pad accepts time signal data, whereas the middle (red) and bottom (brown) accept data from the Rainflow glyph thereby by-passing the internal rain-flow analysis in the Stress Life glyph. The difference between the middle (red) pad and bottom (brown) pad is the type of data expected. The middle pad expects the rainflow histo-gram whereas the bottom pad expects a list of cycles. The histogram format tends to ‘bin’ the data into a small number of columns thereby improving its plotting clarity at the ex-pense of numerical precision; whereas the listed data is more exact. Bypassing the internal routine is only necessary if you

want to edit the histogram before estimating the fatigue damage.

The Stress Life glyph will output the Rainflow histogram and cycle list just like the Rainflow glyph does. It will also output a corresponding damage histogram that represents the pro-portion of damage attributable to each histogram bin.

For the example data used here, notice the well-defined ‘arrowhead’ shape to the rainflow cycle count, indicating that this is likely to be good clean data. As fatigue damage varies exponentially with cycle range, most of the damage will be carried out by the few cycles at the tip of the arrowhead. Compare the damage histogram with the rainflow histogram to see which cycles are the most damaging. Do you think sufficient sample length exists to derive a statistically repre-sentative life?

A cursory glance tells that nearly all of the damage can be attributed to approximately 15 cycles so you will be unlikely to expect good statistical confidence with these results!

Topic 3—Post Processing Fatigue Data

This topic examines the rainflow and damage matrices output by the Stress Life glyph to ensure that the data is sufficient for your purposes.

• Drag a Histogram Display glyph onto the worksheet.

• Connect the two histogram input pads

to the 2nd and 3rd output pads (damage and rainflow histogram) of the Stress Life glyph as shown oppo-site.

• Run the analysis process to see the histograms. What do you conclude about this data? Is the sample length sufficient to determine a statistically sound estimate of fatigue damage?


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About the Stress Life input and output pads

The SN glyph has a number of input and output pads; these are listed below.


Time signal Rainflow Histogram

Materials data (optional)

Time correlated damage Damage Histogram Rainflow Histogram MetaData Rainflow Cycle list

Input Output

52

Up to this point fatigue analysis has been treated as a simple deterministic process resulting in a single value for life. In real-ity, fatigue is very statistical in nature. A company producing aircraft, for instance, might produce two ‘identical’ planes and sell them both to the same customer for use on the same route. In theory they should last exactly the same length of time, but in practice you will realize that some components will fatigue more quickly on one aircraft than the other. The difference in fatigue life is due to small statistical variations in the materials used, the quality of production, the load spectra experienced, and abuse by the crew.

If you were to take a large enough sample of aircraft, you could plot the probability density function of life for a particu-lar component. A typical plot is shown below.

PDF of fatigue life

For this reason, it is usually wise to use the software to deter-mine the sensitivity of damage to all the various input pa-rameters, and to carry out multiple life predictions. The end objective being to determine the expected ‘spread’ of life and the most critical parameter affecting it. GlyphWorks has been specifically written to facilitate this type of study. Usually you will proceed by varying each parameter separately and noting its significance on life. After each exercise you can reset the parameter to its original value before proceeding to the next parameter thereby avoiding confusion.


Topic 4—Sensitivity Studies

This topic investigates the significance of mi-nor variations in input parameters on the fa-tigue life of your component. This type of sen-sitivity study is useful in determining which parameters are most influential and what probable range of life will be delivered in ser-vice.

To help record these results, first switch the Metadata display glyph into table view and tell it to collate the tests.

• Right click on the Metadata Display-glyph and select properties from the menu. Set Display Type = Results and Collate Tests = True.

Now proceed through the following tasks and read the accompanying notes to ascertain the sensitivity to:

• Overload

• Various surface finishes

• Various surface treatments

• Mean stress correction algorithm

• Residual stresses

• Back calculation for Factor of Safety analysis

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Topic 4a—Sensitivity to Overloads

This topic investigates the sensitivity to over-loads. You will compare the fatigue life after scaling the input loads by 10% and also after reducing them by 10%. This will enable you to assess the significance of loading and help you to determine what the likely spread of life will be in-service.

• Right click on the Stress Life glyph and select ‘Properties’ from the menu.

• Enter the property Scale = 1.1 and re-run the analysis. Notice how the result is appended to the end of the Metadata table.

• Repeat the above using a Scale = 0.9 and rerun the analysis.

• Note the new values and then reset the scale factor back to Scale = 1.0 ready for the next topic.

Sensitivity to Overloads

The Scale Factor property allows you to assess the sensitivity to possible calibration errors during data collection or possible overloads in-service. You might be frequently unsure how rep-resentative the measured time signal is of the real loads ex-pected by your component, so it is prudent to carry out this sensitivity study to quantify the effect.


Topic 4b—Sensitivity to surface finish

This topic investigates the sensitivity to surface finish and asses the extent to which this could affect your fatigue life.

• From the Stress Life properties, change the Surface Finish = Average Machined and run the analysis

• Now rerun the analysis using Surface Finish = Ground

• Note the results and reset the Surface finish = Polished

Sensitivity to surface condition

The original analysis was based on material data obtained from a specimen with a mirror polished finish. Such a high quality finish is expensive for production components so GlyphWorks allows you to run the analysis with 10 different surface finishes to estimate how less expensive surfaces affect the expected life.

Surface quality can significantly affect fatigue life under high cycle fatigue but has less significance in low cycle fatigue where the relatively high loads dominate the fatigue process.

On the Stress Life glyph change Surface Finish to Average Ma-chined. Run the analysis and note that the predicted life falls dramatically. Is the life too low? Re-run the test with a surface finish of Ground. The life should be about half that of pol-ished, but about three times longer than Average Machined.

Now re-set it to Polished prior to moving to the next exercise.

About Surface Finish Factors

The surface finish factors offered in GlyphWorks are only in-tended for reference. They are only suitable for steel compo-nents and there accuracy cannot be guaranteed. You should always attempt to obtain representative material samples if you intend to model the surface finish with accuracy.

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Sensitivity to mean stress correction algorithm The mean stress (residual stress) will affect the rate of fatigue dam-

age. Viewed conceptually, if a mean tensile stress is applied to a crack, then the crack is being forced open and any stress cycles applied will therefore have a more pronounced affect. Conversely, if a mean compres-sive stress is applied, then the crack will be forced shut and any stress cycle would first of all have to overcome the pre-compression before any growth could ensue. The graphic shows the effect of residual stress on the SN curve. The curve reduces for tensile residual stresses and rises slightly for compressive residual stresses.

The effect of mean stress on fatigue life

For a comprehensive fatigue analysis it would be desirable to use a num-ber of SN curves, each one for a different mean stress level. (The SN glyph can perform the interpolation required for this type of data if re-quired.) However, the time and cost of performing so many fatigue tests is usually too much, and so you can mainly rely on an empirical correc-tion to take account of residuals. The most popular corrections for SN analysis are the Goodman and Gerber corrections. In general, Goodman proves more conservative for tensile residuals whilst Gerber proves most conservative for compressive. From a pragmatic view you can usually cal-culate the results using all methods and use the most conservative.

The Effect of Surface Treatments

The Surface Treatments modelled in GlyphWorks can be used to improve the fatigue resistance of your component. These surface treatments effec-tively provide a residual compressive stress at the surface of the component that retards the develop-ment of fatigue cracks. Such treatments are only effective in the high cycle fatigue regime where the applied loads are relatively small. The large loads associated with low cycle fatigue are likely to reverse the residual compressive stresses thereby destroying the beneficial effect of the treatment.

Topic 4c—Sensitivity to sur-face treatments

This topic investigates the possible benefits by applying a particular surface treatment.

• From the Stress Life proper-ties, change the Surface Treatment = Shot Peened and run the analysis

• Note the results and reset the Surface Treatment = None

About Surface Treatment Factors

The surface treatment factors offered in GlyphWorks are only intended for reference. They are only suitable for steel components and their accuracy cannot be guaranteed. You should al-ways attempt to obtain representative material samples if you intend to model the surface treat-ment with accuracy.

Topic 4d—Sensitivity to mean stress cor-rection

GlyphWorks can allow for the effect of mean stresses using the Goodman and Gerber corrections.

• From the Stress Life properties, change Mean-StressCorrection = Gerber and run the analy-sis.

• Now rerun with MeanStressCorrection = None

• Note the results and reset the MeanStressCor-rection = Goodman

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Sensitivity to Residual Stress

Residual stresses are usually introduced during manufacture through processes such as cold forming or welding. Compres-sive residual stresses can be beneficial for the reasons dis-cussed earlier in the section on surface treatments; however, tensile residual stresses will actually increase the fatigue dam-age.

You can then assess the effect of residual stresses by varying the mean stress offset and seeing how this influences the com-ponent’s life. To see the effect set the Offset property to 100 (100 MPa of tensile residual). Run the analysis and note the effect on life. Now try an offset of 300 MPa and again note the effect. Reset the offset to 0 before the next exercise.

A Question for you…

After considering the sensitivity to Mean Stress correction and having understood residual stress analysis, do you think Good-man was a suitable choice of Mean Stress correction? After all, Gerber gave shorter lives in topic 4d!

Goodman tends to be non-conservative for compressive residu-als, so gives a higher life than either Gerber or No Correction in task 4d. It would therefore be prudent to choose Gerber over Goodman for this particular time signal. However, Good-man will always prove more conservative when assessing ten-sile residuals such as those being investigated here so it is better to switch to Goodman for sensitivity studies on tensile residual stresses.

Topic 4e—Sensitivity to resid-ual stresses

This topic assesses the sensitivity to residual stresses in the component.

• From the Stress Life properties, change Offset = 100 and run the analysis.

• Now rerun with Offset = 300

• Note the results and reset the Offset = 0

About Residual Stress Analysis

This sensitivity study can only be performed if you have used a mean stress correction like Goodman or Gerber. It will not work if the mean stress correction is switched to none.

Back calculation

Suppose that despite the analyses above, your component was to fail after 10000 repeats of the input signal. GlyphWorks can be used to investigate possible causes. Suppose that you suspect operator abuse, for example. What stress overload would be required to bring the life down to 10000 repeats? GlyphWorks will back-calculate to find the appropriate scale factor to achieve a specified life. In this case the calculated scale factor is about 1.5, so a 50% continuous overload would cause the shortening of life.

Results

So what results did you get? For the record here are your re-sults. Don’t be concerned if your numbers are slightly differ-ent, you may have a slightly different input file after all of that cropping, de-spiking and Butterworth filtering.

Topic 4f—Back Calculation

This topic carries out an iterative back calculation to determine an appropriate scale factor that yields a particular life.

• From the Stress Life properties, double click on Mode = Scale Factor. You should notice a couple of new properties appear on the form.

• Set the Target Life = 10000 and rerun the analysis

• Scroll through the Metadata results and ob-serve the Scale Factor result.

Number of repeats

Approx % change

Scale factor 1.1 7.97E5 -75

0.9 1.95E7 +600

Mean stress correction

Gerber 2.89E6 -13

None 2.87E6 -14

Surface Average ma-chined

1.67E5 -95

Ground 9.52E5 -71

300 8492 -99

800 5.36E5 -84

Residual

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The Effect of Notches

In many cases it is not possible to apply a strain gage directly over the point of failure. You may not know the exact location of failure, or the location maybe inaccessible. In many cases the stress gradient is too steep around a failure site and the strain gage its self would be too large to obtain a good physical result. In this case it is common practice to apply the strain gage at some remote location and use a stress concentration factor ‘Kt’ to effectively scale the nominal stress values to that encoun-tered at the critical location. An illustration of the principal is shown below. Several people have documented values of Kt for various types of notch, the most famous being the works of Peterson.

Where suitable values cannot be readily obtained, then a simple linear Finite Element analysis can be used to deter-mine the solution.

This graphic shows the effect of a circu-lar notch in an infinite plate.

GlyphWorks allows you to model the affect of notches using a fatigue reduc-tion factor Kf. Kf is a function of the stress concentration factor, Kt , and a materials sensitivity to notches. At its most conservative Kf = Kt but is gener-ally slightly less. You may wonder why the notch correction is not applied to

the Scale factor in nSoft. If the time history were scaled by this value it would most probably exceed yield strength and SN would no longer be valid. The Fatigue reduction approach considers the affect of local yielding. A very small region of local yielding next to a stress concentration is not as severe as when the nominal stress exceeds yield. For more information please refer to the Basic Fatigue Theory training course.

Consider how the fatigue life of your component would be compromised if the 5mm radius was reduced to 3mm. The FE de-partment has calculated an effective notch correction Kt = 1.7 for this case.


Topic 5—The Effect of Notches

This topic looks at notch correction in SN analysis. Notch correction is briefly discussed in the informa-tion panel below. Here, you might be interested in the impact of reducing the blade root radius from 5mm to 3mm. This reduction will give rise to an additional local stress concentration at the blade root that will reduce the fatigue life.

• Make sure you have reset all the Stress Life properties back to the original values. (Life = 3.334E6 repeats)

• Edit the Stress Life property Kf = 1.7 and re-run the analysis.

• Note the result and reset Kf = 1 when you’ve finished.

Running the notch correction (Kf) analysis

Enter the new properties as specified and run the analysis. What’s your result? It should be a predicted life of about 13000 repeats—a drop of over 99% caused by reducing the radius from 5mm to 3mm, and the conse-quent increase in the stress concentration.

Fatigue lives can be very sensitive to the presence of stress raisers such as sharp cor-ners and notches.

Strain gages are seldom positioned in the critical regions. They are therefore used to measure the nominal stress in a component.

Stresses in the Critical region here are 3 times larger than the nominal stresses for this example.

Stress concentration fac-tor Kt is defined as: StressNominal

StressCriticalMaxK t =

Kt for a circular hole in an infinite plate is 3.

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Topic 6—Weld Fatigue Analysis

This topic examines what would happen if a simple fillet weld were used to attach the fin. GlyphWorks comes complete with a database of commonly used materials and weld proper-ties so you don't have to type them in manu-ally.

• Make sure you have reset all the Stress Life properties back to the original val-ues. (Life = 3.334E6 repeats)

• Edit the Stress Life property Material-DataSource = MDM_Database

• Notice how the material tab at the top of the form is now un-greyed. Click on the Material tab to see a list of avail-able properties.

• Ignore the error box, this is merely in-forming you that your existing mate-rial, RQC100, is not present, so click on close to dismiss it.

• Select the weld ‘class F’ and either dou-ble click or press the ‘select’ button.

• Rerun the analysis and note the new life.

Running the Welded Analysis

Now you will examine what would happen if a simple fillet weld were used to attach the fin. From the Materials menu chose Class F (weld analysis). Welds are classified in accor-dance with BS7608. If you do not know what classification you have then you can use the British Standard and look at the illustrations. For this analysis it is appropriate to use a Class F weld for a component of 5mm thickness.

The new result is only about 28 repeats! This is a drop in life of over 99.9%, purely because it is now welded. In a real engineering exercise you would almost certainly have to change the design or methods of manufacture to get a more acceptable lifespan.

Note: The material database tab is only available if the property MaterialDataSource = MDM_Database.

Conclusion to tutorial 4

You have now learned how to use the (SN) Stress Life glyph to accomplish basic calculations and carry out detailed sensitivity studies and failure analysis. You’ll find more information on fatigue analysis in the online documentation and the nCode training courses.

In the next tutorial you will achieve similar things with the Strain Life (EN) fatigue glyph. It is comforting to recognize that both glyphs share the same sort of functionality, and now that you’re happy with SN analysis you should have no problem with EN.

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Strain Life (EN) Fatigue Analysis

Learning Objectives

This tutorial introduces the EN Fatigue Analysis glyph in GlyphWorks 3.0. You will start with a brief introduction to EN fatigue theory and learn how to apply it to predict the life of the rotor blades used in tutorials 1 –4.


Topic 1 – An introduction to EN fatigue life prediction. After completing this tutorial you will understand the engineering principles behind EN fatigue analysis and know when to use the EN approach over the SN approach.

Topic 2 – EN fatigue life prediction with GW3.0. After completing this tutorial you will:

• Use the Strain Life glyph to predict the life of the blade

• Understand the input and output from the Strain Life glyph

• Understand all of the parameters for a fatigue life prediction

Pre-requisites You must have completed tutorials 1 to 4 and will also need the file Sg1_cleaned.dac cre-ated in tutorial 3.

Tutorial 5— Strain life (SN) fatigue analysis in GlyphWorks

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Strain Life (EN) Analysis Introduction Quick overview of the EN method

With the advent of modern magnification techniques, fatigue cracks have been investigated in more detail. You now know that a fatigue crack initiates and grows in a two-stage process. In the early stages a crack is seen to grow at approximately 45° to the direction of applied load (following the line of maximum shear stress). After traversing two or three grain boundaries, its direction changes and then propagates at ap-proximately 90° to the direction of the applied load. These are known as Stage I and Stage II cracks and are illustrated below. Furthermore, you now know that fatigue cracks develop and grow as a result of very localized plastic shear strains on a mi-croscopic level.

The above graphic shows Stage I and Stage II crack growth

When August Wöhler pioneered the first fatigue analysis method (SN), he was unaware of this two-stage crack growth process. Therefore the SN method traditionally includes both stages.

In actual fact, each stage involves a different physical mecha-nism and today and you will usually use different analysis techniques for each. The EN (or local strain) method is used to calculate the time taken for Stage I crack growth, while you will usually employ a fracture mechanics approach to calculate Stage II growth.

For many components, Stage II growth may be so fast that engineers can safely ignore it. For more details on this and fracture mechanics, please refer to nCode’s Basic Fatigue The-ory training course.

The EN approach uses strain as an input as opposed to stress (which you used in the SN method in tutorial 4). Localized plastic shear strain is the property that drives fatigue and so strain represents a more suitable choice of input. The EN curve can be considered as a simple extension of the SN curve. Where the SN curve plots stress vs. life, the EN curve plots strain vs. life. When stresses are linear elastic (i.e. in the high cycle regime), the two curves would yield virtually the same life result.

However, where the SN curve is invalid, below 1000 cycles to failure, the EN curve can still be used.

The plot below shows a normalized comparison between the two curves.

Low Cycle Region

(EN Method)

High Cycle Region (SN or EN Method)

Infinite life region???

Transition

Endurance Limit

SN Curve

EN Curve

Topic 1 – an introduction to EN fatigue life prediction

After completing this tutorial you will understand the engineering principles behind EN fatigue.

Tutorial 5— Strain life (EN) fatigue analysis in GlyphWorks

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The Morrow mean stress correction is very similar in its imple-mentation to the Goodman approach used in the SN method. Notice the similarity between the EN based life result of 3.96E6 repeats to failure and that obtained with the SN method of 3.34E6. In terms of fatigue lives, this 15% dis-crepancy is negligible as the real scatter of lives is usually greater than this. The similarity between the two results arises here because the rainflow cycles all lie in the high cycle damage region where both SN and EN methods are valid.

Have you noticed how similar the SN and EN glyphs are in operation? Try rerunning some of the sensitivity studies you did earlier.

• Re-run the analysis with scale factors of 1.1 and 0.9

• Re-run the analysis with different surface finishes and treatments

• Re-run the analysis with mean stress corrections set to: SWT and NoCorrection respectively

• Re-run the analysis with a back calculation target life of 10,000 repeats

Close the worksheet when you have finished. You can save it if you want to re-use it later.

Topic 2—EN Fatigue Life Estimation

This topic carries out a simple EN fatigue analysis to estimate the life of your compo-nent.

• Start a new worksheet by selecting File /New from the menu

• Drag the strain based time series file created in tutorial 3, sg1_strain.dac onto the workspace.

• Drag the Strain Life (EN) glyph on to the output pad of the Time Series in-put glyph.

• Drag a Metadata Display glyph on to the output pad of the EN glyph. Change its properties to Display Type

= Results, Collate Tests = True.

• Right click on the Strain Life (EN) glyph and select properties from the menu. Set the properties Material-DataSource = MDM_Database, click on the Materials tab and select the material RQC100. Set the MeanStress-Correction = Morrow.

• Run the analysis and note the results. Now consider a sensitivity study on all the input parameters. You can follow the notes in Tutorial 4 if you need assistance. How do these results com-pare with the SN based analysis?

Tutorial 5— Strain life (SN) fatigue analysis in GlyphWorks

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Tutorial 5— Strain life (EN) fatigue analysis in GlyphWorks

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End Notes

Thank you for completing these tutorials. I hope you have enjoyed working through this guide and it has given you the confidence to use GlyphWorks with your own data.

The online documentation contains more technical information on the theory and use of each glyph. You can find information about this on page 6 of this guide.

If you require any further help or wish to talk with an nCode engineer, then please contact nCode directly. You can find your nearest contact on www.ncode.com

If you are interested in attending one or more training courses, then please do not hesitate to contact nCode. Training courses are offered in both engineering theory and software usage.

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Notes:

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Southfield, MI 48076

Tel: +1 248 350 8300 Fax: +1 248 350 1678 E-mail: [email protected]

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France

Tel: +33 14178 9080 Fax: +33 14178 9081 E-mail: [email protected]

Germany

nCode International Industriestrasse 29

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Tel: +49 (0)8142-44458-0 Fax: +49 (0)8142-44458-22

E-mail: [email protected]

Worldwide Sales and Support

Local sales and support agencies are available world-wide. Please refer to the nCode web site or contact our offices for more information.

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Sheffield S9 3LQ

Tel: +44 (0)114 275 5292 Fax: +44 (0)114 275 8272

E-mail: [email protected]

www.ncode.com

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Ncodedownloads GW30 Tutorial and Quick Start Guide