Pradeep Krishnan- Project Report[1]

8/6/2019 Pradeep Krishnan- Project Report[1]

1/36

1

CHAPTER-1

INTRODUCTION

1.1 DESIGN OF EXPERIMENTS:

Design of Experiments (DOE) is a complex but powerful method of validating

product and process designs. Design of Experiment enables designers to determine

simultaneously the individual and interactive effects of many factors that could affect the

output results in any design. DOE also provides a full insight of interaction between design

elements; therefore, it helps turn any standard design into a robust one. Simply put, DOE

helps to pin point the sensitive parts and sensitive areas in designs that cause problems in

Yield. Designers are then able to fix these problems and produce robust and higher yield

designs prior to going into production. DOE provides a powerful means to achieve

breakthrough improvements in product quality and process efficiency. By using Fractional

factorial design or Taguchi technique available in DOE it is possible to carry out the fewest

number of experiments while maintaining the most important information. From the view

point of manufacturing fields, this can reduce the number of required experiments when

taking into account the numerous factors affecting experimental results. Experimental design

techniques are preferred over the traditional one-factor-at-a-time approach to experimentation

for determining the optimal condition of any manufacturing process. DOE guides

experimenters through making several simultaneous changes or testing more than one factor

at a time. Overall, less t ime and resources are spent doing fewer experiments, and the result is

a better, more reproducible solution.

Some of the important applications of experimental design in manufacturing environment

are:

Rapid understanding of the process or system behaviour over a specified region of

interest to the experimenters.

Determining which sets of input variables are most important in a process so that they

can be separated from the unimportant ones.

Identifying the key process variables which cause variation in the functional

performance of products and processes and thereby minimising performance variation

for better product quality.


2/36

2

Building quality and reliability into products and processes from the initial design

stages.

Determining the optimal operating condition of manufacturing processes. Setting up tolerances on the critical process variables and thereby reducing process

variability.

Reducing the product and process design and development costs. Reducing scrap rate, rework and other quality related costs.

1.2 GOAL OF EXPERIMENTS:

Experiments help us in understanding the behaviour of a (mechanical) system. Data collected by systematic variation of influencing factors helps us to quantitatively

describe the underlying phenomenon or phenomena.

The goal of any experimental activity is to get the maximum information about a

system with the minimum number of well designed experiments. An experimental program

recognizes the major factors that affect the outcome of the experiment. The factors may be

identified by looking at all the quantities that may affect the outcome of the experiment. Themost important among these may be identified using a few exploratory experiments or from

past experience or based on some underlying theory or hypothesis. The next thing one has to

do is to choose the number of levels for each of the factors. The data will be gathered for

these values of the factors by performing the experiments by maintaining the levels at these

values. Experiments repeated with a particular set of levels for all the factors constitute

replicate experiments. Statistical validation and repeatability concerns are answered by such

replicate data.

1.3 ISSUES IN EXPERIMENTAL DESIGN:

Identification of the objectives of the experiment in the form of specific questions.

The selection of treatments to provide answers to the questions. Statistical concepts

of treatment structure are used to provide maximum information relating to these

questions.


3/36

3

Choice of experimental units and amount of replication.

Control of the variability between sets of units through systems of blocking and/or by

using ancillary information (i.e. covariate information ) collected on the units.

Allocation of treatments to particular units within the overall structure of units,

involving, where possible, an element of randomisation .

Collecting data that are appropriate for the objectives of the research . These may be

on the whole experimental unit, or may involve sampling within the unit.

1.4 BASIC PRINCIPLES:

1.4.1 Statistical design of experiments:

Statistical design of experiments refers to the process of planning the experiment so

that appropriate data that can be analyzed by statistical methods will be collected, resulting in

valid and objective conclusions. The statistical approach to experimental design is necessary

to draw meaningful conclusions from the data.

When the problem involves data that are subject to experimental errors, statistical

methodology is the only objective approach to analysis. Thus, there are two aspects to any

experimental problem: the design of the experiment and the statistical analysis of the data.

These two subjects are closely related because the method of analysis depends directly on the

design employed.

The three basic principles of experimental design are

Replication,

Randomization and

Blocking


4/36

4

1.4.2 Replication:

Replication is a repetition of the basic experiment. There is an important distinction

between replication and repeated measurements. For example, suppose that a silicon wafer isetched in a single-wafer plasma etching process, and a critical dimension on this wafer is

measured three times. These measurements are not replicates; they are a form of repeated

measurements, and in this case, the observed variability in the three repeated measurements is

a direct reflection of the inherent variability in the measurement system or gauge.

1.4.3 Randomization:

Randomization is the cornerstone underlying the use of statistical methods in

experimental design. Randomization means both the allocation of the experimental material

and the order in which the individual runs or trials of the experiment are to be performed are

randomly determined. Statistical methods require that the observations (or errors) be

independently distributed random variables. Randomization usually makes this assumption

valid. Properly randomizing the experiment also assists in "averaging out" the effects of

extraneous factors that may be present.

1.4.3 Blocking:

Blocking is a design technique used to improve the precision with which comparisons

among the factors of interest are made. Often blocking is used to reduce or eliminate the

variability transmitted from nuisance factors; that is, factors that may influence the

experimental response but in which we are not directly interested. Generally, a block is a set

of relatively homogeneous experimental conditions.

The three basic principles of experimental design: randomization, replication, and blockingare part of every experiment.

1.5 GUIDELINES FOR DESIGNING EXPERIMENTS:

To use the statistical approach in designing and analyzing an experiment, it is

necessary to have a clear idea in advance of exactly what is to be studied, how the data are to

be collected, and a qualitative understanding of how these data are to be analyzed.


5/36

5

Guidelines for Designing an Experiment

1. Recognition of and statement of the problem.

2. Choice of factors, levels, and ranges.

3. Selection of the response variable.

4. Choice of experimental design.

5. Performing the experiment.

6. Statistical analysis of the data.

7. Conclusions and recommendations.

In practice, steps 2 and 3 are often done simultaneously or in reverse order.

1.6 FACTORIAL DESIGN:

Many experiments involve the study of the effects of two or more factors. Factorial

design means that in each complete trial or replication of the experiment all possible

combinations of the levels of the factors are investigated. Among the Factorial designs themost commonly used is the 2 k design with k factors, each at two levels.

The 2 k design is particularly useful in the early stages of experimental work, when

there are likely to be many factors to be investigated. It provides the smallest number of runs

with k factors can be studied in a complete factorial design. Consequently these designs are

widely used in factor screening experiments.

1.6.1 Two-Level Fractional Factorial Design:

As the number of factors in a 2 k factorial design increases, the number of runs

required for a complete replicate of the design rapidly outgrows the resources of most

experimenters. For example, a complete replicate of the 2 6 design requires 64 runs. In this

design only 6 of the 63 degrees of freedom correspond to main effects, and only 15 degrees

of freedom correspond to two-factor interactions. The remaining 42 degrees of freedom are

associated with three-factor and higher interactions.


6/36

6

If the experimenter can reasonably assume that certain high-order interactions are

negligible, information on the main effects and low-order interactions may be obtained by

running only a fraction of the complete factorial experiment. These fractional factorial

designs are among the most widely used types of designs for product and process design and

for process improvement.

A major use of fractional factorials is in screening experiments. These are

experiments in which many factors are considered and the objective is to identify those

factors (if any) that have large effects. Screening experiments are usually performed in the

early stages of a project when it is likely that many of the factors initially considered have

little or no effect on the response. The factors that are identified as important are then

investigated more thoroughly in subsequent experiments.

The successful use of fractional factorial designs is based on three key ideas:

1. The sparsity of effects principle : When there are several variables, the system or process

is likely to be driven primarily by some of the main effects and low order interactions.

2. The projection property : Fractional factorial designs can be projected into stronger

(larger) designs in the subset of significant factors.

3. Sequential experimentation : It is possible to combine the runs of two (or more) fractional

factorials to assemble sequentially a larger design to estimate the factor effects and

interactions of interest.

1.6.2 One-Half Fraction of the 2 k Design:

One half fraction factorial design can reduce the number of experiments and generates

the same result it can be achieved by full factorial design. The number of experiments is

reduced by eliminating the three-factor and higher interactions. For example 2 4 factorial

design can be reduced to 2 4-1 = 2 3 factorial design.

1.7 TAGUCHI METHOD:

Taguchi method is a statistical method developed largely by Genichi Taguchi to

improve quality of manufactured goods. The Taguchi method is a standardized approach for

determining the best combination of inputs to produce a product or service. This isaccomplished through design of experiments (DOE).


7/36

7

1.7.1 Robust Design:

The Taguchi concept of robust design states that products and services should be

designed so that they are inherently defect free and of high quality. Taguchis approach for creating robust design is through a three-step method consisting of concept design, parameter

design, and tolerance design.

1.7.1.1 Concept design:

Concept design is the process of examining competing technologies to produce a

product.

1.7.1.2 Parameter design:

Parameter design refers to the selection of control factors and the determination of

optimal levels for each of the factors.

1.7.1.3 Tolerance design:

Tolerance design deals with developing specification limits.

1.7.2 Taguchi Process steps:

Step 1: Problem Identification:

First, the production problem must be identified. The problem may have to do with

the product process or the service itself.

Step 2: Brainstorming Session:

Second, a brainstorming session to identify variables that have a critical affect on

service or product quality takes place. The critical variables identified in the brainstorming

sessions are referred to as factors. These may be identified as either control factors (variables

that are under the control of management) or signal factors (uncontrollable variation).

Step 3: Experimental Design:

Using the factors, factor levels, and objectives from the brainstorming session, the

experiment is designed. The Taguchi method uses off-line experimentation as a means of

improving quality. This contrasts with traditional on-line (in process) quality measurement.


8/36

8

Step 4: Experimentation:

There are different Taguchi analysis approaches that use quantitatively rigorous

techniques such as analysis of variance (ANOVA), signal-to-noise ratios (S/N), and responsecharts. These approaches, although not always theoretically sound, are useful in engineering

related projects involving engineered specifications, torques, and tolerances.

Step 5: Analysis:

Experimentation is used to identify the factors that result in closest-to-target

performance. If interactions between factors are evident, two alternatives are possible.

Either ignore the interactions (there is inherent risk to this approach) or, provided the cost is

not prohibitive, run a full factorial experiment to detect interactions. The full factorial

experiment tests all possible interactions among variables

Step 6: Confirming Experiment:

Once the optimal levels for each of the factors have been determined, a confirming

experiment with factors set at the optimal levels should be conducted to validate the earlier

results. If earlier results are not validated, the experiment may have somehow been

significantly flawed. If results vary from those expected, interactions also may be present,

and the experiment should, therefore, be repeated.

1.7.3 Orthogonal Array(OA):

Orthogonal Arrays (often referred to Taguchi Methods) are often employed in

industrial experiments to study the effect of several control factors. An orthogonal array is a

type of experiment where the columns for the independent variables are orthogonal to one

another.

1.7.3.1 Benefits:

1. Conclusions valid over the entire region spanned by the control factors and their

settings.

2. Large saving in the experimental effort.

3. Analysis is easy.


9/36

9

1.8 DISC BRAKE:

The motor vehicles are being now fitted with disc brakes instead of the conventional

type drum brakes. In these brakes, a circular plate replaces the drum while flat pieces of friction material grip the disc instead of shoes. A disc brake usually consists of a rotating disc

and two friction pads actuated by four hydraulic wheel pistons. A caliper which is an

assembly of two halves contains the wheel pistons.

The caliper assembly is secured to the steering knuckle in a front wheel brake and to

the axle housing in a rear wheel brake. It is cast into two parts, each part containing a piston.

In between each piston and the disc, there is a friction pad held in position by retaining pins,

spring plates etc. passages are drilled in the caliper for the fluid to enter or leave each

housing.

Fig 1.1: Schematic sketch of disc brake

When the brake pedal is applied the fluid will be displaced from the master cylinder

to the wheel cylinder pistons. This will exert equal and opposite pressure on the friction pads

by forcing them against the rotating disc to grip it. More force is needed to apply the disc

brakes for same brake requirement because they are not self-energizing. The pads will still

maintain slight pressure on the disc although pressure on the disc relieved when the pedal is

released


10/36

10

In India disc brakes were used for the first time in Maruti 800 cars at the front wheels.

These have now been employed in many other cars also.

1.9 BRAKE PAD:

The brake pads are designed for high friction with brake pad material embedded in the

disc in the process of bedding while wearing evenly. Although it is commonly thought that

the pad material contacts the metal of the disc to stop the vehicle, the pads work with a very

thin layer of their own material and generate a semi-liquid friction boundary that creates the

actual braking force. Friction can be divided into two parts: Adhesive and abrasive.

Depending on the properties of the material of both the pad and the disc and the configuration

and the usage, pad and disc wear rates will vary considerably. The properties that determine

material wear involve trade-offs between performance and longevity. The friction coefficient

for most standard pads will be in the region of .40 when used with cast iron discs. Racing

pads with high iron content designed for use with cast iron brake discs reach .55 to .60 which

gives a very significant increase in braking power and high temperature performance.

Fig 1.2: Disc pad

High iron content racing pads wear down discs very quickly and usually when the

pads are worn out so are the discs. The brake pads must usually be replaced regularly

(depending on pad material), and some are equipped with a mechanism that alerts drivers that

replacement is needed. Some have a thin piece of soft metal that rubs against the disc when

the pads are too thin, causing the brakes to squeal, while others have a soft metal tab

embedded in the pad material that closes an electric circuit and lights a warning light when

the brake pad gets thin. More expensive cars may use an electronic sensor.
http://en.wikipedia.org/wiki/Brake_padhttp://en.wikipedia.org/wiki/Frictionhttp://en.wikipedia.org/wiki/Sensorhttp://en.wikipedia.org/wiki/Sensorhttp://en.wikipedia.org/wiki/Frictionhttp://en.wikipedia.org/wiki/Brake_pad


11/36

11

Generally road-going vehicles have two brake pads per caliper, while up to six are

installed on each racing caliper, with varying frictional properties in a staggered pattern for

optimum performance.


12/36

12

CHAPTER - 2

LITERATURE REVIEW

Various journals related to design of experiments had been referred and inputs have been

taken from their articles. Some of the journals are listed below:

Dong-Woo Kim (2008) et al , have attempted to minimize the thrust forces in the step-feed

micro drilling process by application of the DOE (Design of Experiments) method. Taking

the drilling thrust into account, three cutting parameters, feed rate, step-feed, and cutting

speed, are optimized based on the DOE method. The objective is to ascertain factors

predominantly affecting drilling thrust in micro deep hole drilling processes. Forexperimental studies, an orthogonal array L27(3 13 ) is generated and ANOVA (Analysis of

Variance) is carried out. Through fractional experiments, optimal conditions can be

determined by analyzing the S/N ratio (Signal-to-Noise ratio) as a performance measure

Based on the results it is determined that the sequence of factors affecting drilling thrusts

corresponds to feed rate, step-feed, and spindle rpm. A combination of optimal drilling

conditions is also identified. In particular, it is found that the feed rate is the most important

factor for micro drilling thrust minimization.

Luc Pronzato (2008) traces the strong relations between experimental design and control,

such as the use of optimal inputs to obtain precise parameter estimation in dynamical systems

and the introduction of suitably designed perturbations in adaptive control. The mathematical

background of optimal experimental design is briefly presented, and the role of experimental

design in the asymptotic properties of estimators is emphasized. Although most of the paper

concerns parametric models, some results are also presented for statistical learning and

prediction with nonparametric models.

Roberto C.Dante (2003) et al , have used a fractional experimental design, at two levels and

four factors, to improve the power output of a commercial stack protonic exchange

membrane (PEM) fuel cell with seven graphite plates at about atmospheric hydrogen

pressure. The main objective of this study is the fast performance improvement for a fuel cell

specific application. In order to achieve the objective of this study, they utilized the

experimental design methodology, because the fuel cell system is a complex one; indeed


13/36

13

there are several factors too difficult to analyze. The factors considered were both hydrogen

and oxygen pressures and flow rates. An orthogonal and fractional matrix array was used in

order to reduce time execution with eight experiments instead of the full factorial design of

16 experiments. The experiments showed that not all the conditions have stable power output

with the considered hydrogen pressure, but that it is possible to stabilize it especially with

high oxygen flow rates. They have concluded that the experimental design methodology is a

suitable tool for the improvement of fuel cell systems in a specific situation. This

methodology allowed to quickly adopt a fuel cell system to a specific device, knowing the

principal factors and effects that affect the fuel cell performances.

Douglas C. Montgomery (2000) et al , have used a three-phase methodology for process

development and improvement based on statistically designed experiments, along with a

comprehensive example for a surface mount technology process in an electronics industry.

The objective of the experiment is to 1. Determine which variables are most influential on the

response(s), 2. Determine where to set the influential x's so that the response(s) are almost

always near their desired target values, 3. Determine where to set the influential x's so that the

variability in the response(s) is small, or 4. Determine where to set the influential x's so that

the effects of the uncontrollable variables on the response(s) are small. They have discussedsome of the aspects of how statistically designed experiments are used in developing new

processes or improving the performance of existing ones. They have identified the critical

factors influencing average solder volume and solder volume variance on a stencil printing

process via a statistically designed experiment. This process was subsequently optimized to

minimize solder volume variance while simultaneously achieving a target average solder

volume. Confirmation experiments at the optimal settings were subsequently carried out

proving that a significant improvement in the process has been obtained.

Jiju Antony (1999) has explained the potential benefits of SDE (Statistically Designed

Experiments) for product and process quality improvement in a manufacturing environment.

He has also described the application of SDE to a wire bonding process in order to identify

the key variables which affect the pull strength. Both analytical and graphical methods are

used for better and rapid understanding of the results. He has explained the importance of

SDE and they are used for identifying the critical variables associated with a process andthereby determining the optimal levels for these variables for enhanced performance.


14/36

14

S.Spuzic (1997) et al, have described a multidisciplinary approach to wear simulation

rolling-sliding abrasion. They have found that the statistical methods were highly suitable for

conceiving and analysing laboratory simulation of the wear process. They have presented a

Fractional factorial design showing a procedure for simultaneous evaluation of the influence

of force, temperature, material and sliding on abrasion of rolling mill tool materials. The

important factor was roll wear. Various experimental strategies have been developed and

applied to examine the process. A multifactorial experiemental design and complementary

statistical design (eg: multi regression analysis) were selected to rationalise the experiment to

detect the noise and the possible effects and interaction of force, temperature, material and

sliding velocity of the wear rate. They have concluded that it is significant that low contact

forces, typical for the deformation zone entry in rolling cause significant abrasion; an

increase in wear rate was detected with increase in normal force from 200 to 300N. The

increase of sliding velocity from 0.3 to 0.7 m s -1 decreases abrasion wear at elevated

temperature.

Design of Experiments could thus be a handy tool in finding out the optimal process

parameter and thereby improving a process.


15/36

15

2.1 SCOPE OF THE PROJECT:

There are various process stages in disc pad manufacturing. They are mixing, pre-

form, curing, baking and finishing process. These processes have been studied thoroughly. Itis found that certain percent of rejection is occurred in Ambassador Line. After thorough

investigation it is found the major defects are propagation of crack. These cracks are

influenced by many factors. They are flatness of the base plate, mixing ratios, pre-form

shape and curing factors. In the curing stage, all raw materials will be converted into finished

products and there are some factors that influence the crack from this process. They are

temperature, pressure, pressure raising time, vent opening time, vent closing time, vent gap,

number of vents and vent time. From these factors which has high contribution was taken assignificant factors. The significant factors were arrived by using Taguchi method. From

these the optimum process parameters were found.

2.2 OBJECTIVE OF THE PROJECT:

To find the root cause of the defects and analyze them.

Reduce the percentage defectives in disc pad production (Ambassador line).


16/36

16

CHAPTER-3

METHODOLOGY

Fig 3.1: Flow-chart for methodology

The above flow chart shows the step by step procedure for carrying out this research work.

3.1 STUDY OF THE OPERATIONS:

3.1.1 About the production schedule of Contessa:

Contessa disc pads will be produced on demand. Hence it will not be produced throughout

the month.

3.1.2 Process flow chart:

A process flow chart of the disc pad manufacturing plant is shown in Fig. 3.2.

Study the operations

Deciding response

Factors involved

Design of experiments

Conducting experimentsat different levels

Analysis

Determining optimumprocess parameter


17/36

17

Fig 3.2: Process flow chart


18/36

18

3.1.3 Process Stages of Disc Pad Manufacturing:

There are six main process stages in manufacturing disc pad. They are:

Mixing Pre-form Curing Baking Finishing

3.1.3.1 Mixing process:

There are two different types of mixes grouped together. They are:

Main mix Underlay mix

The main mix in contessa is R802 HUT. This R802 HUT has some friction particles and

these particles will be in contact with the disc plate. So they are called as the main mix.

The underlay mix in contessa is RB 32A. This RB 32A has some fibre particles. This

grade will be in between the main mix and base of the disc pad. The underlay mix is in

between the main mix and base plate. The underlay mix is used in order to absorb heat

dissipated from the main mix and disc plate which may otherwise damage the disc caliper

and to adhere the main mix firmly to the base plate.

Fig 3.3: Positions of main mix and underlay mix

3.1.3.2 Pre-Form Process:

The pre-form stage gives the shape to the abrasive particles. The shape is obtained by

pressing the abrasive particles in hydraulic press machine. Both the main mix and the


19/36

19

underlay mix are put into the cavity of the hydraulic press. First the main mix is put into the

cavity followed by the underlay mix. Then the machine presses it. There will be two cavities

in the machine so two pre-form moulds can be produced at a time. There are certain

specifications according to that the main mix and the underlay mix is put. They are given

below.

Pre-form process specification for contessa:

Press tonnage = 45 tonnes

Maximum pressure = 200 kg/cm 2

Main mix = 135 grams

Underlay mix = 25 grams

Fig 3.4: Shape of pre-form

3.1.3.3 Curing Process:

The curing process is the main stage in disc pad manufacturing. The curing machine

has two jaws, the upper jaw and the lower jaw where the dies will be fixed. In the lower jaw,

a number of cavities are there in to which the pre-form moulds are put and above that thebase plate is placed. Then the machine presses it. The base plate has some adhesive so that it

gets adhered to the pre-form moulds firmly. When the curing process is started, within a few

seconds the machine starts to breathe (vent). This is done to eliminate air gaps in the moulds.

Curing process specification for contessa:

Temperature = 143-153C

Press tonnage= 166 tonnes


20/36

20

Maximum pressure = 205 kg/cm 2

Minimum number of cavities in a machine = 12

Maximum number of cavities in a machine = 24

3.1.3.4 Baking Process:

The baking process is done to harden the moulds. The baking process is carried out

after the curing process is completed. The baking process is the longest process of all the

process stages in disc pad manufacturing. The baking process for contessa model takes only

one cycle. Many sets of semi-finished disc pads (after curing stage) are put into the baking

machine and it gets heated there for a certain period of time.

Baking process specification for contessa:

Temperature = 205+/-5c

Cycle time = 13 hours 30 minutes +/- 15 minutes.

3.1.3.5 Finishing Process:

In the finishing process, there are two main of operations. One is grinding and another

one is cha mfering. A wear mark is made on the disc pads abrasive surface in order to

identify that the main mix is worn out completely. It is advisable to use the disc pad until the

main mix or layer is worn out because it may cause scores in the disc plate. The grinding

operation is carried out in the top face of the disc pad i.e. on the abrasive surface.

Finishing process specification of contessa:

Total thickness of the disc pad = 17.2 mm +/- 1 mm.


21/36

21

Fig 3.5: Wear mark

3.1.3.6 Painting Process:

Painting process is the final stage of the disc pad manufacturing. The painting is

carried out as per the specification (preferably grey) at the back side of the disc pad i.e., base

plate. The abrasive surface will not be painted. After this the pads undergo baking for 30

minutes.

3.1.3.7 Technical Specification of Disc Pad:

Part name - Contessa

Grade - R802 hut

Underlay mix - RB 32A

Thickness of the plate = 4.9-5 mm

Overall thickness before finishing process = 19 mm

Overall thickness after finishing process = 16.9-17.1 mm

3.1.3.8 Cycle time to produce a disc pad

Base plate preparation = 1hr 15 mins

Mixing = 25 mins

Pre-form = 1 min


22/36

22

Curing = 11 mins

Baking = 14 hrs 30 mins

Painting = 1 hr 40 mins

Finishing = 30 mins 28 secs

Total cycle time = 17 hrs 51 mins 28 secs

3.2 DECIDING RESPONSE:

The main quality problem in the disc pad manufacturing is propagation of crack

mainly at the corners. When the crack size exceeds 20 mm, the disc pad is considered to bedefective.

3.2.1 Acceptance / Rejection Criteria:

When the crack size is very large, the pads are rejected. However if the crack is hairline

and if exceed 20mm, the pads are reworked. Since rejection or rework will result in loss of

productivity, the presence of hairline cracks above 20mm is taken as response variable.

Fig 3.6: Propagated crack

3.2.2 Identified Problem:

The disc pad production (Ambassador Line) unit has been facing a problem of

producing defective pads. It is found that all the defectives have crack propagation in the

walls of disc pad. To resolve this issue this project work is carried out.


23/36

23

3.3 PROCEDURE FOR TAKING READINGS:

The readings of vent opening time, vent closing time and pressure raising time are

taken from the dial gauge fitted to the machine using stop watch.

The temperature readings of each cavity are noted using digital pyrometer.

The length of vent gap is measured using the vertical dial gauge.

3.4 PILOT STUDY

A pilot study has been carried out to evaluate the present performance and rejection

rate. The experiments will be conducted on the curing process.

Table 3.1: Pilot study results

No. of loads

taken (A)

No. of disc pads

in a load (B)

Total no. of disc

pads (A*B)

No. of defectives % of defectives

18 18 324 19 5.84

These trials are taken without adjusting given specification i.e., they are taken as per

the normal conditions and are taken in the curing machine. From the observation it is found

that about 6 percent are defective. This pilot study is conducted only for 18 loads i.e., for 324

disc pads. Therefore about 19 pieces of disc pads are found to be defectives with cracks.

3.5 FACTORS INVOLVED IN CRACK PROPAGATION:

Flatness of the plate. Mixing ratios.

Pre-form shape.

Factors in curing machine:

Temperature.

Vent gap.

Vent opening time.


24/36

24

Vent closing time.

Pressure raising time.

Vent time.

Number of vents

Pressure.

3.6 CAUSE AND EFFECT DIAGRAM OF THE CRACK PROPAGATION:

Fig 3.7: Cause and effect diagram of the crack propagation

3.7 CURING MACHINE:

This machine has a total capacity to produce 24 disc pads in a single load, but due

some constrains only 18 disc pads can be produced.

The temperature for each cavity is noted and an average of all 18 cavities is the

temperature obtained for that load.


25/36

25

In the figure, D represents the dummy cavity i.e., because of certain affecting

parameters. Parameters like temperature and pressure may vary, so that cavity is made

dummy.

3.8 DESIGN OF EXPERIMENTS:

Among the 8 factors in the curing machine only 5 are varying. The three factors

pressure, vent time and no. of vents are held constant. But the pressure raising time cant be

set in the curing machine. And hence that factor is also taken off. So for these 4 factors each

at two levels the number of experiments needed is 2 4 = 16.

3.8.1 Reasons to reduce the number of experiments:

The time taken for taking a trial is 15 minutes and the cycle time of a curing process is

11 minutes. So about 4 minutes is taken for conducting the trial experiment for a single load.

Altogether for a shift 7 to 8 loads will be needed for conducting trials. Therefore it will incur

in loss of production, it is necessary to reduce the number of experiments. Hence one-half

Fractional factorial design and Taguchi method were used.

Fig 3.8: Mould Design in 2D line sketch


26/36


27/36

27

Table 4.3: Results from ANOVA table

Source DOF Sum of Squares

Mean Square F-ratio P-value

Main effect 4 28 7 1.58 0.226

2- wayinteractions

3 5.833 1.944 0.44 0.727

Residualerror

16 70.667 4.417

Pure error 16 70.667 4.417

Total 23 104.5

Confidence level = 95%

Table 4.4: Sum of Squares for Individual Factors

Factors Sum of Squares (SS)

A 0.667

B 10.667

C 10.667

D 6

4.1.1 Pareto Chart of the standardized effect:

Fig. 4.1: Pareto chart


28/36

28

4.1.2 Model adequacy check using regression analysis:

Regression equation = 4.75 + 0.667 B + 0.667 C

Put B & C = +1

= 6.084

Put B & C = - 1

= 3.416

Put B = +1 & C = -1

= 4.75

Put B = -1 & C = +1

= 4.75

Table 4.5: Shows the observed and calculated value of the regression analysis

Observedvalue, y

Calculatedvalue,

Residuale = y -

3.1667 3.416 -0.2493

5.000 4.75 0.25

5.000 4.75 0.25

5.833 6.084 -0.251

Fig.4.2: Shows the Normal Probability Plot

From the analysis it is concluded that points on the normal probability plot lie reasonably

close to a straight line, which is leading to the conclusion that B and C are significant.


29/36

29

4.1.3 Results from Experiments under Fractional Factorial Design:

Total no. of disc pads taken for trial = 432

Total no. of hair line cracks = 307

Total no. of disc pads with no crack = 113

Total no. of defective disc pads = 12

Total % of crack = 2.8

Total % of acceptable disc pad = 97.2

Crack % in PPM = 28000

Acceptable % of disc pads in PPM = 972000

Even though the model was found to be adequate, none of the factors were found to

be significant because the sample size is smaller and more number of noise factors

was present.

And factors other than curing machine were not considered to conduct experiment.

Hence, it is decided to conduct the experiments further using Taguchi method by

considering more factors.

4.2 EXPERIMENTS UNDER TAGUCHI METHOD:

Table 4.6: Shows the factors and their levels:

Variable Factors Level (1) Level (2) Unit

A Flatness of thebase plate

0-0.1 0.1-0.2 mm

B Pressure 191-208 208-225 Kg/cm 2

C Temperature 143-148 148-153 C

D Vent open time 3.5-4.0 4.0-4.5 sec

E Vent close time 3.5-4.0 4.0-4.5 sec

F Vent gap 3.0-3.5 3.5-4.0 Mm


30/36

30

Table 4.7: L8 orthogonal array (OA)

A B C D E F No. of defectives %defective

Replicate 1 Replicate 2

1 1 1 1 1 1 1 0 2.78

1 1 1 2 2 2 0 0 0

1 2 2 1 1 2 0 0 0

1 2 2 2 2 1 0 2 5.56

2 1 2 1 2 1 1 1 5.56

2 1 2 2 1 2 2 0 5.56

2 2 1 1 2 2 1 0 2.78

2 2 1 2 1 1 1 0 2.78

Total 6 3 3.125

Grand total 9

The above results are arrived by considering only defectives.

4.2.1 Results from Experiments under Taguchi Method:Table 4.8: Response table for Means

Level A B C D E F

1 3 5 3 4 4 3

2 6 4 6 5 5 6

Diff 3 1 3 1 1 3

Rank 1 2 1 2 2 1

Among the six factors, factors C-temperature, A-flatness and F-vent gap have the highest

contribution.

Total no. of disc pads taken for trial = 288

Total no. of hair line cracks = 53

Total no. of hair line cracks more than 20mm (re-work) = 9


31/36

31

Total no. of disc pads with no crack = 217

Total no. of defective disc pads = 9

Total % of open cracks (defectives) = 3.125

Total % of defectives and re-work = 6.25

Total % of acceptable disc pad = 96.875

Crack % in PPM = 31250

Acceptable % of disc pads in PPM = 968750

The same issue in Fractional Factorial Design is still prevailing i.e., none of the factors

were found to be significant because more number of noise factors were present. Since the

noise factors cant be controlled. And all the necessary factors were also taken into account.

Hence confirmation experiments were conducted by considering the factors which has high

contribution.

Table 4.9: Optimum Level

Variable Factors Level Range

A Flatness of the base plate

1 0 - 0.1 mm

C Temperature 1 143 - 148C

F Vent gap 2 3.5 4.0 mm

4.2.2 Predicting results using Omega transformation:

Factors C-temperature, A-flatness and F-vent gap have the highest contribution. Hence levels

of these factors are used for predicting the percentage defective.

Y Predicted = Y + ( A 1 - Y ) + ( C 1 - Y ) + ( F 2 - Y )

Y = 18/288 = 0.03125

A1 = 3/144 = 0.02083


32/36

32

C 1 = 3/144 = 0.02083

F 2 = 3/144 = 0.02083

Using omega transformation the predicted defective percentage is Y Predicted = 0.9159 %

4.2.3 Confirmation Experiments:

Table 4.10: Shows results from confirmation experiments

No. of loads

taken (A)

No. of discpads in aload (B)

Total no.of discpads

(A*B)

No. of defectives

No. of defectives& rework

% of defectives

% of defectives& rework

10 18 188 1 5 0.53 2.65

Therefore, the predicated value is very close to the obtained value. Hence, it proves that the

obtained optimum level is correct.

4.3 ECONOMIC BENEFITS:

Table 4.11: Production report

Year Total no. of discpads produced

(units)

Total no. of defective disc pads

produced (units)

Total no. of discpads re-worked

(units)

2010 1,64,490 9,606 10,075

Total cost per disc pad = Rs. 80

Total loss from defectives = Rs. 7, 68,480

Total loss from re-work [Considering only manpower] = Rs. 8,200

Total no. of man hours required to re-work = 41 hours for 10,075 disc pads

Total no. of manpower required to re-work = 7

Total cost spent on manpower to re-work = Rs. 800/4 hrs for 7 employees


33/36

33

4.3.1 Cost savings:

Total cost saved by reducing re-work [6.125% to 2.127%] = Rs. 3,800

Total cost saved by reducing defectives [5.84% to 0.53%] = Rs. 6, 98,800

Total cost saved from re-work and defectives = Rs. 7, 02,600


34/36

34

CHAPTER - 5

CONCLUSION

A pilot study has been conducted with the existing process settings. The results

obtained from that have a percentage defective of 5.84%.

Among the several stages involved in a disc pad manufacturing the curing stage is

most important.

It is found that in the curing stage factors like temperature, pressure, pressure raising

time, vent gap, vent opening time and vent closing time are the important factors

influencing the crack propagation among the 8 factors involved.

Based on the experiments conducted using Taguchi technique, half of the factors

which have the highest contribution were taken as significant.

Confirmation experiments were conducted by keeping those factors at the optimum

levels and by keeping the other factors within the current specification.

Rs. 7, 02,600 can be saved per year [as per 2010 production reports].


35/36

35

REFERENCES:

1. Dong-Woo Kim, Myeong-Woo Cho, Tae-Il Seo and Eung-Sug Lee Appl ication of

Design of Experiment Method for Thrust Force Minimization in Step-feed Micro

Drilling Sensors 8 (2008), 211-221.

2. Douglas C. Montgomery, J. Bert Keats, Leonard A. Perry, James R. Thompson,

William S. Messina Using statistically designed experim ents for process

development and improvement: an application in electronics manufacturing Robotics

and Computer Integrated Manufacturing 16 (2000), 55 63.

3. Jiju Antony Improving the wire bonding process quality using statistically designed

experiments Microelectronics Journal 30 (1999), 161 168.4. Luc Pronzato Optimal experimental design and some related control problems

Automatica 44 (2008), 303 325.

5. Roberto C. Dante, Jos-e L. Escamilla, Vicente Madrigal, Thomas Theuss, Juan de

Dios Calder-on, Omar Solorza, Rub-en Rivera Fractional factorial design of

experiments for PEM fuel cell performances improvement International Journal of

Hydrogen Energy 28 (2003), 343 348.

6.

S.Spuzic, M.Zec, K.Abhary, R.Ghomashchi and I.Reid Fractional factorial designof experiments applied to a wear simulation Wear 212 (1997), 131-139.

Bibliography:

1. Automobile technology by S.Selvaraj, S.Dhanushkodi and P.Jaya prakash.

2. Design and analysis of experiments by Douglas C.Montgomery.

3. Fluid power with applications by Anthony Esposito.

4. Fundamentals of quality control and improvement by Amitava Mitra .

5. Mechanical measurements by Prof. S.P. Venkateshan, IIT Madras.


36/36

36

LIST OF PUBLICATIONS:

1. Pradeep Krishnan G and Samuel Raj D Reduction of Percentage Defectives in Disc

Pad Manufac turing using Design of Experiments National Conference on Advances

in Mechanical Sciences, Volume-III (2011), 21-24.

Documents

Pradeep Krishnan- Project Report[1]