ME 388 – Applied Instrumentation Lab

Spring 2012

Dave Bayless, PhD, PE, FASMELoehr Professor of Mechanical Engineering

248 Stocker Centere-mail: bayless@ohio.edu

Office Hours: M,T,W,Th 15:30 – 16:30

Text• ME 388 Laboratory Manual can be found at

http://www.ohio.edu/people/bayless/seniorlab

• Experimental Methods for Engineers (Holman) may be useful, but is not required

GradingSubject Unit Weight Total

Five Experimental Lab Exercises @ 10% 50%

Mastery level formal report @ 25% 25%

CIM laboratory Project @  5% 5%

DOE Project @   5% 5%

Formal Lab Introductory material @  5% 5%

Final Exam @ 10% 10%

Total   100%

Purpose

• Enhance fundamental engineering learning with lab experiments

• Gain experience and improve experimental techniques

• Improve data reduction and analysis skills• Improve written communication skills

Outcomes

• Mastery• Competence• Awareness

Course Mastery Outcomes• Ability to perform curve-fitting of multivariate

data sets• Ability to calculate the error/uncertainty

propagation for calculations that include multiple terms with uncertainties.

• Writing and editing clear and effective laboratory reports, including the creation of “professional quality” graphics for figures, tables, plots and charts.

Course Competence Outcomes• Ability to use common measurement equipment• Ability to apply previously-learned engineering

concepts to compare theoretical predictions with actual experimental results in diverse, practical mechanical engineering experiments.

• Ability to program and use CNC machines to manufacture simple parts

• An ability to interpret tensile test data

Course Awareness Outcome

• Awareness of Design of Experiments (DOE) statistical techniques

• DOE Exercise will give you a chance to interpret a test matrix

Spelling and Grammar

• Write in the 3rd person• Use spelling and grammar checker in Word• Adopt the style of a textbook or journal article• See formal report guidelines in lab notes• “Write smart”

– “Outlying data were rejected.” instead of “Bad data was thrown out.”

– Edit your work to be concise!!

Figure 1. Burst strength as a function of time

Figure Example

0

100

200

300

400

500

600

0 100 200 300 400 500

Date of Sampling

Mul

len

Bur

st S

treng

th (p

si)

Teflon - Reactive 275Ryton B - Reactive 430Ryton-Sulfuric 605Omnisil-Sulfuric 550

Table 4. Dependent process variables as a function of the DOE number.

DOE No.

Dependent variablesRam pressure

(MPa)Specific pressure

(MPa)

1 14.71 459.22 15.09 471.1

3 14.30 446.64 13.69 427.35 14.77 461.16 14.37 448.37 13.30 415.0

Table Example

vt

wt

1

3p iip

Figure 5: Schematic of stressed multi-void tube due to pressure

Use computer generated schematics

Equations

• Use MS Word Equation editor• Number equations sequentially, right justified• See Lab notes

Statistical Analysis Review

• Mean

• Standard Deviation

• Sample Size

n

in x

nnxxxxx

1

321 1...

Mean

Standard Deviation

xxi Simple variance

ni

i xxnn

xxxxxxxx

1

222

32

22

121

11

...

Sample variance

2

1

1

222

32

22

11

11

...

n

ii xx

nnxxxxxxxx

Standard deviation of a sample

Histogram and normal distribution

Standard deviation and data

How many samples are enough?

n xi xave 1 90 --- ---2 89 89.50 0.7073 91 90.00 1.0004 87 89.25 1.7085 80 87.40 4.3936 90 87.83 4.0707 92 88.43 4.036

Can “outlying” data be ignored?

• Determine if there is a physical basis for the suspect data (i.e., the TC broke, etc.)

• Chauvenet’s criteria for data rejection

Chauvenet’s criteria

1. Calculate xave and for data set2. Get dmax/ for the specific sample size from a

table3. Calculate dmax = (dmax/) × 4. Determine if the most “reject-able” data is

larger than this value, d = |xave – xi|5. Reject outlying data and then recalculate xave

and for data set

Chauvenet’s Example

Chauvenet’s Example4 (5 data points, n=5)23 Average = 6.2175 Standard Deviation = 6.14

Which one to reject? Technically, examine at them all Realistically, focus on “17”

For n = 5, reject at

65.1dmax

76.114.6

2.617d

← REJECT 17

How sure are you of your data?• All measurement instruments have a degree of

uncertainty when taking a reading• Uncertainty values for a particular instrument

is usually given or can be determined• For calculated parameters, the uncertainty is a

function of the uncertainties of the measured parameters.

Uncertainty calculation

• Report uncertainty as a % of calculated value

21

222

),,(Let

zxW

yxW

vxWW

zyvfx

zyvx

%100X

WyintUncerta x

Regression Analysis• Pertains to reporting of a “least-square” or

other type of curve fit to your data

• You must report the equation and the correlation coefficient (R) or the coefficient of determination (R2)

• R-values should be presented with the equation and a graph of the data

Formal Report

• Abstract• Introduction• Experimental Apparatus• Results and Discussion• Conclusions and Recommendations• Appendices

– Uncertainty analysis– Data

Abstract (250 words)

• Purpose• What was done?• Significant parameters measured or set• Measurement results (summarized)• Quantitative comparisons (i.e., to published

values)

Introduction

• Provide background information• Establish significance of work• Introduce work and motivation for experiment• Introduce equations that are pertinent to data

analysis and purpose of the lab

Experimental Apparatus

• Describe experimental equipment configuration using schematic diagrams

• Explain test procedure• Present any calibration data• Show (tabulate) all uncertainty values

measurements that were taken

Results and Discussion

• Present results (analyzed data) in graphical form

• Discuss results and sources of error (explain why the data did what it did)

• Develop logical and reasonable explanations with regard to data behavior

• Make quantitative comparisons• Discuss uncertainty and if it accounts for any

known or obvious discrepancies

Conclusions and Recommendations

• Summarize results quantitatively• Summarize any comparisons• Start with results of most importance or

significance• Address all significant points• Make sound recommendations

– Things that could be improved– Additional work that could be done