introduction final report
TRANSCRIPT
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CHAPTER 1
INTRODUCTION
In engineering field, metal components are required to have high dimensional precisionand accuracy. After fabrication, they require further machining to facilitate dimensionalcontrol.Thus proper machining of metal parts plays a very important role in industrial
production.
Surface roughness plays an important role in evaluating quality of machined products.The quality of surface is of utmost important for the correct functioning of machine parts which
directly affect the attributes of product such as friction, fatigue, wear resistance, coating,
reflection and lubricant . There are many factors that affect surface roughness of any machinedparts, these factors among others includes: machining parameters, tool geometry, work piece
material, nature of chip produced, machine rigidity and cutting fluids used . In other to achieve
the specified roughness, a tradeoff between the factors that affect the surface roughness is alwaysmade.
Carbon Steel is widely used in various fields of engineering and industries.. Nevertheless,
because of carbon steels wider area of applications coupled with its low cost and availability,machining characteristics of its surface roughness need to be optimized to further increase its
area of application as well as to achieve high quality products .
Steel alloys such as EN-8, EN-45, EN-31 etc. are widely used in automobile industries,
aeronautical industries, construction industries etc. Hence the effect of cutting parameters on
surface roughness is evaluated and the optimum cutting condition for minimizing the surface
roughness is determined.
The cutting parameters used for machining in this project are cutting speed, feed and depth of
cut. The experiments are done on three grades of steel and using the surface finish measuringdevice, Ra value of the specimen is calculated. Smaller the Ra value, better the surface finish.
Surface grinding is used to carry out the surface finish in the specimens due to its better removal
rate.
Surface grinding is the most common of the grinding operations. It is a finishing process that
uses a rotating abrasive wheel to smooth the flat surface of metallic or nonmetallic materials to
give them a more refined look or to attain a desired surface for a functional purpose. Formachining of iron and steel , grinding process is preffered. In steel components, grinding wheel
gives a better material removal rate and hence the makes the surface smooth.
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1.1 Objective
To determine the effect of different parameters in machining processes of steel alloys andhenceforth to obtain the maximum affecting and least affecting parameter usingTaguchis method of Design Of Experiments.
To obtain an optimum surface roughness and the value of the level of respective factorsattributing to get the optimum value.
To create a mathematical model to find out surface finish using parameters like feed,depth of cut and different types of material used.
Ra = C0+ C1x+ C2 y+ C3z + C4 x2+ C5 y2 + C6 z2 +C7xy+ C8 xz + C9xy
Where C0,C1,C2 are the constant and x,y z are the different parameters which are varied.In our project we consider three parameters material with different carbon composition,
depth of cut and feed.
1.2 Motivation
Design of experiments is a widely used technique in todays industries. This branch of
applied statistics deals with planning, conducting, analyzing and interpreting controlled tests to
evaluate the factors that control the value of a parameter or group of parameters.A strategically planned and executed experiment may provide a great deal of information about
the effect on a response variable due to one or more factors.
Taguchis method of design of experiments is useful for studying the interactions between the
parameters, and also it is a powerful design of experiments tool, which provides a simple,
efficient and systematic approach to determine optimal cutting parameters. Compared to theconventional approach of experimentation, this method reduces drastically the number of
experiments that are required to model the response functions . It is proposed for the purpose to
improve the quality of products based on the concepts of statistics and engineering.
The important applications of design of experiments in manufacturing industry includes
improved process yield and stability
improved profits and return on investment improved process capability
reduced process variability and hence better product performance consistency
reduced manufacturing costs
reduced process design and development time
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heightened morale of engineers with success in chronic-problem solving
increased understanding of the relationship between key process inputs and output(s)
increased business profitability by reducing scrap rate, defect rate, rework, retest etc.
1.3 Organisation of the Report
The report is divided into five chapters. The first chapter gives an introduction and
outline of the project . The second chapter discusses the literature review. All the theory and
related work done in this area is briefly explained in the chapter. The third chapter reveals themethodology followed to achieve the objective of the project. The method and experimental
work,is discussed in detail in this chapter. The fourth chapter is about the results and its
optimization. All the values and their detailed analysis that are obtained from MINITAB 15software to design and optimize the experiments are given in this chapter. The final chapter
indicates the conclusions drawn from the work done and also suggests its future scope .
CHAPTER 2
BACKGROUND THEORY
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In this chapter, the theory behind Taguchis method of Design Of Experiments and the
calculations involved in it are discussed. The project was started with literature review. Thefollowing topics were covered by referring various books and papers.
2.1. Taguchis Method of Design of Experiments
The Taguchi method involves reducing the variation in a process through robust design ofexperiments. The overall objective of the method is to produce high quality product at low cost
to the manufacturer. The Taguchi method was developed by Dr. Genichi Taguchi of Japan who
maintained that variation.
This is a method for designing experiments to investigate how different parameters affect the
mean and variance of a process performance characteristic that defines how well the process is
functioning. The experimental design proposed by Taguchi involves using orthogonal arrays to
organize the parameters affecting the process and the levels at which they should be varied; it
allows for the collection of the necessary data to determine which factors most affect productquality with a minimum amount of experimentation, thus saving time and resources. Analysis of
variance on the collected data from the Taguchi design of experiments can be used to select newparameter values to optimize the performance characteristic.
2.1.1 Steps Involved in Taguchi Method
The general steps involved in the Taguchi Method are as follows:
Determine the design parameters affecting the process. Parameters are variables withinthe process that affect the performance measure such as temperatures, pressures, etc. thatcan be easily controlled. The number of levels that the parameters should be varied at
must be specified. Increasing the number of levels to vary a parameter at increases the
number of experiments to be conducted.
Create orthogonal arrays for the parameter design indicating the number of andconditions for each experiment.
Conduct the experiments indicated in the completed array to collect data on the effect onthe performance measure.
Complete data analysis to determine the effect of the different parameters on theperformance measure.
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2.1.2 Taguchi Loss Function
The goal of the Taguchi method is to reduce costs to the manufacturer and to society from
variability in manufacturing processes. Taguchi defines the difference between the target valueof the performance characteristic of a process, , and the measured value, y, as a loss function as
shown below.
( ) ( ) 2= ykyl c
The constant, kc, in the loss function can be determined by considering the specification limits orthe acceptable interval, delta.
2=
Ckc
The difficulty in determining kc is that and C are sometimes difficult to define.
If the goal is for the performance characteristic value to be minimized, the loss function is
defined as follows:
( ) 2ykyl c= where 0=
If the goal is for the performance characteristic value to maximized, the loss function is defined
as follows:
( ) 2ykyl c=
The loss functions described here are the loss to a customer from one product. By computingthese loss functions, the overall loss to society can also be calculated.
2.1.3 Determining Parameter Design Orthogonal Array
The effect of many different parameters on the performance characteristic in a condensed set of
experiments can be examined by using the orthogonal array experimental design proposed by
Taguchi. Once the parameters affecting a process that can be controlled have been determined,the levels at which these parameters should be varied must be determined. Determining what
levels of a variable to test requires an in-depth understanding of the process, including the
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minimum, maximum, and current value of the parameter. If the difference between the minimum
and maximum value of a parameter is large, the values being tested can be further apart or more
values can be tested. If the range of a parameter is small, then less values can be tested or thevalues tested can be closer together.
Knowing the number of parameters and the number of levels, the proper orthogonal array can be
selected. Using the array selector table shown below, the name of the appropriate array can be
found by looking at the column and row corresponding to the number of parameters and numberof levels. Once the name has been determined (the subscript represents the number of
experiments that must be completed), the predefined array can be looked up. Links are provided
to many of the predefined arrays given in the array selector table. These arrays were createdusing an algorithm Taguchi developed, and allows for each variable and setting to be tested
equally.
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Table 2.1 Array Selector Matrix
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The above table shows the array selector for the Taguchis method of Design OfExperiments. This table corresponds to the minimum number of experiments which should be
performed according to the number of parameters and number of levels of each parameterinvolved.
For e.g. for 2 parameters and 2 levels minimum number of experiment performed should be 4.
Similarly for 6 parameters and three levels, the minimum number of experiments to beperformed is 18.
In this table L4, L8, L9, L12..represents different arrays.
In our experiment, we have three parameters (feed, carbon composition, depth of cut) and threelevels of each factor, by reffering array selector we will get L9 array. The levels designated as 1,
2, 3 etc. should be replaced in the array with the actual level values to be varied and P1, P2, P3
should be replaced with the actual parameters .
Table 2.2 L9 array
Note : In our experiment though we have 3 factors and 3 levels of experiment, instead of L9
array, L27 array is chosen .This is done to consider the interactions between the factors and thusto get more precise and accurate analysis.
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Table 2.3 L27 array
Here:
P1 = carbon composition factor
P2 = Feed rate factor
P3 = interaction between P1 and P2
P5 = Depth of cut factor
P6 = interaction between P1 and P5
P7 = interaction between P2 and P5
P8 = interaction between P1, P2, P3
According to a rule, the unwanted parameters in array table can be neglected. Thus P9 to P13 areneglected.
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2.1.4 S/N Ratio:
Taguchi has used signal-noise (S\N) ratio as the quality characteristics of choice. S\N ratio is
used as measurable value instead of standard deviation due to the fact that as the mean decreases,the standard deviation also decreases and vice versa. In practice, the target mean value may
change during the process development. Two of the applications in which the concepts of S\N
ratio is useful are the improvement of quality through variability reduction and the improvementof measurement.
To determine the effect each variable has on the output, the signal-to-noise ratio, or the SN
number, needs to be calculated for each experiment conducted. After calculating the SN ratio for
each experiment, the average SN value is calculated for each factor and level.
In the equations below, yi is the mean value and si is the variance. yi is the value of the
performance characteristic for a given experiment.
For the case of maximizing the performance characteristic, the following definition of the SN
ratio should be calculated:
Nominal is the best characteristics
2
2
log10i
i
is
ySN =
Smaller is the best characteristics
=
=
iN
u i
u
iN
ySN
1
2
log10
Larger the better characteristics
=
=iN
u ui
iyN
SN1
2
11
Where
=
=IN
u
ui
i
yN
y1
,
1
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( )=
=iN
u
iui
i
i yyN
s1
,
2
1
1
i = Experiment Number
u = Trial Number
Ni= Number of experiments for trial i
2.1.5 Analysis Of Variance
The analysis of variance (ANOVA) establishes the relative significance of factors in terms of
their percentage contribution to the response. For the analysis of variance ,given equations are
used. (For 9 experiments)
[ ]
9
2
=
i
m
YS
= miT SYS 2
[ ]
m
Ai
A SN
YS =
2
= ATE SSS
A
AA
f
SV =
E
AA
V
VF =0
where,
ST = sum of squares due to the total variation,
Sm = sum of squares due to the mean,
SA = sum of squares due to parameterA
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SE = sum of squares due to error,
Yi = output value of each experiment (i = 1,2,.,9),
YAi = sum of the i level of parameterA (i = 1,2,3),
N = repeating number of each level of parameterA,
fA = degree of freedom of parameterA,
VA = variance of parameterA
FA0 = F-test value of parameter
2.1.6 F test
When the data have been collected from more than one sample, there exists two independent methods of
estimating the population parameter , called respectively the between and the within method.
Since each of the sample variances may be considered an independent estimate of the
parameter , finding the mean of the variances provides a method of combining the separate
estimates of into a single value. The resulting statistic is called theMean Squares Within,often represented by MSW. It is called the within method because it computes the estimate by
combining the variances within each sample.
2sMSW =
The parameter may also be estimated by comparing the means of the different samples, but
the logic is slightly less straightforward and employs both the concept of the sampling
distribution and the Central Limit Theorem.
First, the standard error of the mean squared ( ) is the population variance of a distribution ofsample means. In real life, in the situation where there is more than one sample, the variance of
the sample means may be used as an estimate of the standard error of the mean squared ( ).
This is analogous to the situation where the variance of the sample (s2) is used as an estimate of
.
In this case the Sampling Distribution consists of an infinite number of means and the
real life data consists of A (in this case 5) means. The computed statistic is thus an estimate ofthe theoretical parameter.
The relationship expressed in the Central Limit Theorem may now be used to obtain an estimate
of .
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N
X
X
22
=
22* XXN =
Thus the variance of the population may be found by multiplying the standard error of the mean
squared ( ) by N, the size of each sample.
Since the variance of the means, , is an estimate of the standard error of the mean squared, ,the variance of the population, , may be estimated by multiplying the size of each sample, N,by the variance of the sample means. This value is called the Mean Squares Between and is often
symbolized by MSB. The computational procedure for MSB is presented below:
2*
XBsNMS =
The expressed value is called the Mean Squares Between, because it uses the variance between
the sample means to compute the estimate. Using the above procedure on the example data
yields:
21.283*6=BMS
28.1699=BMS
At this point it has been established that there are two methods of estimating , Mean
Squares Within and Mean Squares Between. It could also be demonstrated that these estimates
are independent. Because of this independence, when both mean squares are computed using thesame data set, different estimates will result. For example, in the presented data MSW=89.78
while MSB=1699.28. This difference provides the theoretical background for the F-ratio
A new statistic, called the F-ratio is computed by dividing the MSB by MSW
W
Bobs
MS
MSF =
The F-ratio can be thought of as a measure of how different the means are relative to the
variability within each sample. The larger this value, the greater the likelihood that the
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differences between the means are due to something other than chance alone, namely real effects.
How big this F-ratio needs to be in order to make a decision about the reality of effects is the
next topic of discussion.
If the difference between the means is due only to chance, that is, there are no real effects, then
the expected value of the F-ratio would be one (1.00). This is true because both the numeratorand the denominator of the F-ratio are estimates of the same parameter, . Seldom will the F-ratio be exactly equal to 1.00, however, because the numerator and the denominator are estimates
rather than exact values. Therefore, when there are no effects the F-ratio will sometimes be
greater than one, and other times less than one.
To review, the basic procedure used in hypothesis testing is that a model is created inwhich the experiment is repeated an infinite number of times when there are no effects. A
sampling distribution of a statistic is used as the model of what the world would look like if there
were no effects. The results of the experiment, a statistic, is compared with what would be
expected given the model of no effects was true. If the computed statistic is unlikely given the
model, then the model is rejected, along with the hypothesis that there were no effects.
In an ANOVA, the F-ratio is the statistic used to test the hypothesis that the effects are
real: in other words, that the means are significantly different from one another. Before thedetails of the hypothesis test may be presented, the sampling distribution of the F-ratio must be
discussed.
If the experiment were repeated an infinite number of times, each time computing the F-
ratio, and there were no effects, the resulting distribution could be described by the F-distribution. The F-distribution is a theoretical probability distribution characterized by two
parameters, df1 and df2, both of which affect the shape of the distribution. Since the F-ratio must
always be positive, the F-distribution is non-symmetrical, skewed in the positive direction.
The F-ratio which cuts off various proportions of the distributions may be computed fordifferent values of df1 and df2. These F-ratios are called Fcrit values and may be found by entering
the appropriate values for degrees of freedom in the F-distribution program.
2.1.7 P test
Determines the appropriateness of rejecting the null hypothesis in a hypothesis test. P-
values range from 0 to 1. The smaller the p-value, the smaller the probability that rejecting the
null hypothesis is a mistake. Before conducting any analyses, determine your alpha () level. Acommonly used value is 0.05. If the p-value of a test statistic is less than your alpha, you rejectthe null hypothesis.
Because of their indispensable role in hypothesis testing, p-values are used in many areasof statistics including basic statistics, linear models, reliability, and multivariate analysis among
many others. The key is to understand what the null and alternate hypotheses represent in each
test and then use the p-value to aid in your decision to reject the null.
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For example, consider a 2-sample t-test where you are testing the difference between the mean
strength of steel from two mills based on random samples from each. In this case, the null
hypothesis states that the two population means are equal while the alternate hypothesis statesthat they are not equal. A p-value below your cutoff level suggests that the population means are
different.
Suppose you are also conducting regression analyses on steel strength where temperature
is one of the explanatory variables. You will see a p-value for each regression coefficient. Here,the default test is to determine if the estimated coefficient for temperature is different from zero.
Therefore, the null hypothesis states that the coefficient equals zero while the alternate
hypothesis states that it is not equal to zero. A p-value below your cutoff level suggests that thecoefficient for temperature is significantly different from zero and likely to be a meaningful
addition to your model.
The p-value is calculated from the observed sample and represents the probability of
incorrectly rejecting the null hypothesis when it is actually true . In other words, it is the
probability of obtaining a difference at least as large as the one between the observed value andthe hypothesized value through random error alone.
CHAPTER 3
METHODOLOGY
3.1 Equipments used and their operations
In this, cylindrical grinding is carried on three different steel alloys i.e EN8,EN31 and EN45 .
Before grinding ,alloys were drilled , faced and turned in the lathe machine with approximatelength of 125mm and diameter of 12mm.
3.1.1 Lathe
A lathe is a machine tool used principally for shaping pieces of metal, wood, or other materials
by causing the workpiece to be held and rotated by the lathe while a tool bit is advanced into the
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work causing the cutting action. Lathes can be divided into three types for easy identification:
engine lathe, turret lathe, and special purpose lathes. Some smaller ones are bench mounted and
semi-portable. The larger lathes are floor mounted and may require special transportation if they
must be moved. Field and maintenance shops generally use a lathe that can be adapted to many
operations and that is not too large to be moved from one work site to another. The engine latheis ideally suited for this purpose. A trained operator can accomplish more machining jobs with
the engine lathe than with any other machine tool. Turret lathes and special purpose lathes are
usually used in production or job shops for mass production or specialized parts, while basic
engine lathes are usually used for any type of lathe work.
3.1.1.1 Workholding methods
Chuck
Chucks are a very common workholding method. There are many types, some for round and
square stock, and other for irregular shapes.
Collet
Primarily used for small round workpieces.
Faceplate
A faceplate, drive dog, and mandrel may be used to turn workpieces such as gear blanks.
Drive center
Use hydraulic or spring-loaded teeth that "bite" into the end of workpieces and can be used when
the entire length of the workpiece must be machined.
3.1.2 Grinding Machine
The grinding machine is used to shape the outside of an object. It can work on a variety of
shapes, however the object must have a central axis of rotation. The grinding machine consists ofa power driven grinding wheel spinning at the required speed and a bed with a fixture to guide
and hold the work-piece. The grinding head can be controlled to travel across a fixed work piece
or the workpiece can be moved with the grind head stays in a fixed position. Very fine control of
the grinding head or tables position is possible using a vernier calibrated hand wheel, or using
the features ofnumerical controls.
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Grinding machines remove material from the workpiece by abrasion, which can generate
substantial amounts of heat; they therefore incorporate a coolant to cool the workpiece so that it
does not overheat and go outside its tolerance. The coolant also benefits the machinist as the heat
generated may cause burns in some cases. In very high-precision grinding machines (most
cylindrical and surface grinders) the final grinding stages are usually set up so that they removeabout 200 nm (less than 1/100000 in) per pass - this generates so little heat that even with no
coolant, the temperature rise is negligible.
We generally use two types of grinding machine and they are cylindrical grinder and surface
grinder. In our project cylindrical grinding machine is used.
3.1.2.1 Cylindrical Grinding Machine
Cylindrical grinder includes both the types that use centers and the centerless types. A
cylindrical grinder may have multiple grinding wheels. The workpiece is rotated and fed past the
wheels to form a cylinder. It is used to make precision rods, tubes, bearing races, bushings, and
many other parts.
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Figure 3.1 Cylindrical Grinding Machine
This machine is required for the precision grinding of various governing component.For e.g.
valve seat, valve cone and mandrels etc to meet accuracy requirements.Ferrous material like
carbon steels, alloy steels, tool steels etc can be grinded using this machine.
Table 3.1: Specification of cylindrical grinding machine
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3.1.3 Surtronic 3+
Surtronic 3+ combines advanced technology with high precision and value to give effective
measurement of surface finish in the workshop, inspection room or laboratory.
With Surtronic 3+, users across a wide range of skills can become proficient within minutes.
Operating functions are minimal, measurement cycles are short and output is available from a
built-in LCD display or various printer options.
The measurement process and operation is simplicity itself, the entire cycle being controlled
from a wipe-clean membrane touch key panel, via walk through menu selections.
The instrument is usable on horizontal, vertical or inclined surfaces, or with selected accessories
as a bench mounted system for laboratory or batch measurement applications. The pick-up
holder is mounted on a slide for vertical adjustment and can be rotated to different measuring
positions, including right-angled measurement.
Surtronic 3+ is powered by NiCAD batteries or through an optional mains adaptor. The portable
format of the Surtronic 3+ renders it particularly useful for measuring surface texture on bores
and inaccessible parts which are unsuitable for conventional measuring instruments.
Table 3.2 Specifications of surtronic 3+
19
Maximum Length of workpiece : 3000mm
Steady rest capacity : 100 to 400mm
Max. grinding length : 2600mm
Centre height : 410mm
Grinding wheel Dia x Width : 750 x 70 mm approx.
Max. table traverse : 3500mm
Table swivel either side : 40
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20
Gauge range -0.006 to +0.006 in.
Traverse length max 25.4 mm
Pick up type variable reluctance
Stylus diamond tip radius 5 micrometer
Cut off values 0.25,0.8,2.5,8 mm
Parameters Ra, Rq, Rz, Ry, Sm, Rt
Resolution 0.01 micrometer
Traverse length min 0.25 mm
Traverse speed 1 mm/sec
Power battery or mains
Overall dimension 130 x 80 x 65 mm
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Figure 3.2 : Surtronic 3+
Advantages
Total portability for workshop or laboratory
Fast, simple operation21
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Accurate and versatile
Selectable range of parameters, cut-off lengths and filtering
Software analysis options
Data processing options
Multi-language selection
Battery powered and completely self-contained
3.1.3.1 Surtronic accessories
Replica kit
Contains prepared quantities of materials for producing replica of surfaces inaccessiblefor direct measurement.
Detachable skid
Clamped to the pick-up body, this accessory enables the Datum Support Stand to be used
with standard, recess, right angle and chisel edge pick-ups.
Precision Vice
High quality precision vice ideal for holding finished components.
Dimensions: Jaw Width 70mm
Jaw Opening 80mm
Mains Adaptor
Enables instrument to be powered from the mains supply.
Datum Support Stand
Provides an independent straight datum where the surface is too short to accommodate
the stylus and skid of the small bore pick-up. May also be used with other pick-ups fitted
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with the detachable skid.
Ball joint vice
Comprising a surface mounted swivel base and a wide jaw vice. Suitable for holdingirregular shaped components.
Vice Dimensions:
Overall length 280mm
Jaw Width 54mm
Jaw Opening 160mm
Pick-up Lift
Used to lift the stylus clear of the workpiece after measurement and to retain the pick-up
arm in a raised position, preventing the arm falling onto the workpiece and damaging the
surface or stylus tip.
Roll and Bore Fixture
This fixture allows the Surtronic 3+ to be mounted onto cylindrical components.
Impact Printer
Output includes all measuring conditions such as cut-off selected, traverses length, filter
and selected parameter results. The printer also outputs a range of horizontal and vertical
magnification settings for scaling of the graphical outputs.
Portable Base
Provides a support when used on machine tool applications and away from the measuring
room.
Support Stand
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Converts Surtronic 3+ into a bench mounted instrument, particularly useful when
checking small bores and for repetitive measurement. A swivel platform can be rotated
and angled to keep the pick-up traverse parallel to the measured surface.
3.2 Software Used
MINITAB 15
Minitab is a powerful, easy-to-use, statistical software package that provides a wide range
of basic and advanced data analysis capabilities. Minitabs straightforward command structure
makes it accessible to users with a great variety of background and experience.
Minitab software is used to identify effects which are most important to process
variability and is used to analyse and interpret the results of experiments using simple butpowerful graphical tools.It is a powerful tool to analyse the statistical data. Regression Analysis,
Multivariate Analysis factor analysis, cluster analysis, correspondence analysis, Analysis of
Variance etc can be done using Minitab.
3.3 Operations Preformed
The cylindrical grinding experiments is carried on three different steel alloys rods which have
different carbon percentage :
EN 8 - 0.40%
EN 45 - 0.55%
EN 31 - 1.00%
3.3.1 Lathe Operations
4 rods of length 125 mm of each material is taken .and then operation were carried on lathe
machine .
The following operations which are carried on lathe machines are :
3.3.1.1 Facing
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It is a turning operation that is carried out on a lathe on the side part of the rod. Facing is part of
the turning process. It involves moving the cutting tool at right angles to the axis of rotation of
the rotating workpiece.[1] This can be performed by the operation of the cross-slide, if one is
fitted, as distinct from the longitudinal feed (turning). It is frequently the first operation
performed in the production of the workpiece, and often the last- hence the phrase "ending up".
3.3.1.2 Drilling
It is a cutting process that uses a drill bit to cut or enlarge a hole in solid material. The drill bit is
a multipoint, end cutting tool.it cuts by applying pressure and rotating to the workpiece which
forms chips at cutting edge.
3.3.1.3 Turning
Turning is the process whereby a single point cutting tool is parallel to the surface. When
turning, a piece of material (wood, metal, plastic, or stone) is rotated and a cutting tool is
traversed along 2 axes of motion to produce precise diameters and depths. Turning can be either
on the outside of the cylinder or on the inside (also known as boring) to produce tubular
components to various geometries.
Dynamics of turning
The relative forces in a turning operation are important in the design of machine tools. The
machine tool and its components must be able to withstand these forces without causing
significant deflections, vibrations, or chatter during the operation. There are three principal
forces during a turning process:
The cutting or tangential force acts downward on the tool tip allowing deflection of theworkpiece upward. It supplies the energy required for the cutting operation.
The axial, thrust or feed force acts in the longitudinal direction. It is also called the feedforce because it is in the feed direction of the tool. This force tends to push the tool away
from the chuck.
The radial force acts in the radial direction and tends to push the tool away from the workpiece.
After all this lathe operations 4 rods of each materials are converted into 20 mm diameter and
125 mm length and then it is moved to another step which is cylindrical grinding operations
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3.3.2 Grinding Operations
Rods of EN 8, EN 31 and EN 45 of length 125mm and 12mm diameter are operated on
cylindrical grinding machine. 27 experiments are carried out with two parameters which are as
follows :
Depth of cut
The cutting depth of the tool affects to the processing speed and the roughness of surface. When
the cutting depth is big, the processing speed becomes quick, but the surface temperature
becomes high, and it has rough surface. Moreover, a life of byte also becomes short. If you do
not know a suitable cutting depth, it is better to set to small value.
In this experiment we take three different values is 0.02mm, 0.04mm and 0.06mm with the help
of design of experiments.
Feed rate
Feed rate is the velocity at which the cutter is fed, that is, advanced against the workpiece. It is
expressed in units of distance per revolution for turning and boring (typically inches per
revolution [ipr] or millimeters per revolution). It can be expressed thus for milling also, but it is
often expressed in units of distance per time for milling (typically inches per minute [ipm] or
millimeters per minute), with considerations of how many teeth (or flutes) the cutter has then
determining what that means for each tooth.
Feed rate depends on :
Type of tool (a small drill or a large drill, high speed or carbide, a boxtool or recess, athin form tool or wide form tool, a slide knurl or a turret straddle knurl).
Surface finish desired.
Power available at the spindle (to prevent stalling of the cutter or workpiece).
Rigidity of the machine and tooling setup (ability to withstand vibration or chatter).
Strength of the workpiece (high feed rates will collapse thin wall tubing)
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Characteristics of the material being cut, chip flow depends on material type and feedrate. The ideal chip shape is small and breaks free early, carrying heat away from the tool
and work.
In our experiment we took three levels of feed rate :
Slow : 14mm/min
Medium : 28 mm/min
Large : 42 mm/min
On completing this process, the readings are noted on the factorial table and then to find
the values of surface rougnness with the help of surtronics 3+. The Ra value is also noted down
on the same table.
CHAPTER 4
RESULTS AND ANALYSIS
In this chapter, the results of surface finish are discussed and analyzed. The tool used here is
MINITAB.
4.1 Surface Roughness estimation:
Using Surtronic 3+ , following values of Ra for the experiments were obtained:
The Ra values are shown in the table 4.1 below :
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Table 4.1: Experimental Ra values
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Experiment No. C% Feed DOC Ra
1 0.4 17 0.02 1.49
2 0.4 17 0.04 1.56
3 0.4 17 0.06 1.77
4 0.4 28 0.02 1.53
5 0.4 28 0.04 1.66 0.4 28 0.06 1.78
7 0.4 42 0.02 1.59
8 0.4 42 0.04 1.62
9 0.4 42 0.06 1.82
10 0.6 17 0.02 1.36
11 0.6 17 0.04 1.46
12 0.6 17 0.06 1.58
13 0.6 28 0.02 1.42
14 0.6 28 0.04 1.56
15 0.6 28 0.06 1.64
16 0.6 42 0.02 1.48
17 0.6 42 0.04 1.62
18 0.6 42 0.06 1.72
19 1 17 0.02 1.08
20 1 17 0.04 1.19
21 1 17 0.06 1.53
22 1 28 0.02 1.19
23 1 28 0.04 1.26
24 1 28 0.06 1.56
25 1 42 0.02 1.39
26 1 42 0.04 1.54
27 1 42 0.06 1.59
Using MINITAB 15, analysis of the Ra values for the different parameters is done.The software
uses Taguchis Design Of Experiments approach to calculate the S/N ratios by analysis of
variance. Smaller the Ra value, better the surface finish we get. Thus we use smaller the betterformula while calculating S/N ratio for all the experiment results.
=
=
iN
u i
u
iN
ySN
1
2
log10
These SN ratio values are calculated by MINTAB for each factor and level, they are tabulated as
shown below and the range Delta (delta = high SN - low SN)of the SN for each parameter is
calculated and entered into the table. The larger the Delta value for a parameter, the larger theeffect the variable has on the process. This is because the same change in signal causes a larger
effect on the output variable being measured.
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Table 4.2 : Smaller the better S/N ratios and ranking of factors
Level doc feed C%
1 -2.816 -3.121 -4.277
2 -3.416 -3.486 -3.716
3 -4.415 -4.040 -2.655
Delta 1.599 0.919 1.622
Rank 2 3 1
According to the above results, carbon composition is the most significant parameter affectingthe surface finish obtained from the machining process (grinding) of the steel rods.
0.060.040.02
-2.5
-3.0
-3.5
-4.0
-4.5
422814
1.00.60.4
-2.5
-3.0
-3.5
-4.0
-4.5
doc feed
c%
Data Means
Signal-to-noise: Smaller is better
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Figure 4.1 : Main Effects Plots for SN ratios
The above graph is obtained by MINITAB 15 and it shows the dependence of surfacefinish to the affecting parameters . From the table above the rank of the parameters affecting
surface finish was obtained . Carbon composition was found to be the most affecting parameter
whereas feed is the least affecting parameters.The graph implies that mean of SN ratio increasesas the carbon composition in steel increases. That means harder the steel, better the surface
finish.
Similarly lower the feed rate and depth of cut , better the surface finish.Hence the optimum surface finish is obtained in high percentage carbon steel rods when
subjected to minimum depth of cut and minimum feed rate during grinding process.
4.2 Geometrical model for Ra values
By using the application of response surface design in MINITAB 15 we can create a
mathematical equation with different variables coefficient which can be used throughout for any
values.
Ra = C0+ C1x+ C2 y+ C3z + C4 x2+ C5 y
2 + C6 z2 +C7xy+ C8 xz + C9xy
Table 4.3: Values of Coefficients for Geometric Modeling of Ra values by MINITAB15
Term Coef
Constant 1.84672
c% -1.10675
feed -0.00286281
doc -1.34921
c%*c% 0.152778
feed*feed 8.78685E-05doc*doc 97.2222
c%*feed 0.00994898
c%*doc 4.10714
feed*doc -0.0833333
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From the above table we get the values as:
Table 4.4 : Ra values Geometric Modelling Coefficients
C0 1.84672
C1 -1.10675
C2 -0.00286281
C3 -1.34921
C4 0.152778
C5 8.78685E-05
C6 97.2222
C7 0.00994898C8 4.10714
C9 -0.0833333
Putting these values in the above equation we can obtain the mathematical model for surfaceroughness
Ra = 1.84672 - 1.10675x 0.00286281y 1.34921 z + 0.152778 x 2 + (8.78685 E -05) y2+
97.2222 z2
+ 0.00994898 xy + 4.10714 xz 0.083333 yz .
4.2.1 Calculations
Ra values for different experiment conducted is calculated and verified with the geometrical
equation,
Ra = 1.84672 - 1.10675x 0.00286281y 1.34921 z + 0.152778 x 2 + (8.78685 E -05) y2+
97.2222 z2 + 0.00994898 xy + 4.10714 xz 0.083333 yz
Considering the experiment conducted on En 45 with carbon composition of 1% , feed of 42mm/min and depth of cut as 0.06 mm,
i.e x =1 y = 42 z= 0.06
Ra = 1.84672 (1.10675 * 1) (0.00286281 * 42) (1.34921 * 0.06 )+ (0.152778 x 1 * 1) +
(8.78685 E -05) (42 * 42) + (97.2222 * 0.06 * 0.06) + ( 0.00994898 *1 *42) +( 4.10714
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*1 * 0.06) ( 0.083333 * 42 * 0.06)
Ra = 1.84672 1.10675 0.120238 0.0809526 + 0.152778 + 0.155 + 0.34999 + 0.41785 +
0.246428 0.20999
Ra = 1.65
Error % = (1.65 1.59) / 1.65 x 100
= 3.68
Considering the experiment conducted on En 31 with carbon composition of 0.6 % , feed of 28
mm/min and depth of cut as 0.04
i.e x = 0.6 y = 28 z = 0.04
Ra = 1.84672 (1.10675 * 0.6) (0.00286281 * 28) (1.34921 * 0.04 )+ (0.152778 * 0.6* 0.6)
+ (8.78685 E -05) (28 * 28) + (97.2222 *0.04 *0.04) + ( 0.00994898 *0.6 *28)
+( 4.10714 * 0.6 * 0.04) ( 0.083333 * 28 *0.04)
Ra = 1.50
Error% = (1.56 - 1.5)/1.56 *100
= 3.84
Other than these experiments we also performed a experiment by varying the depth of cut and
feed to verify the geometrical model.
We used the En 31 steel rod with carbon composition of 0.6 % (x = 0.6) and grinding of the rod
is done by giving a depth of cut of 0.05 mm ( y =0.05) and feed as 30 mm/min (z=30) and by
checking out the surface finish with the help of surtronic 3+ we get the Ra value of 1.64.
Using geometrical equation,
Ra= 1.84672 - 1.10675x 0.00286281y 1.34921 z + 0.152778 x 2 + (8.78685 E -05) y2
+ 97.2222 z2 + 0.00994898 xy + 4.10714 xz 0.083333 yz
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Ra = 1.84672 (1.10675 * 0.6) (0.00286281 * 30) (1.34921 * 0.05 )+ (0.152778 * 0.6* 0.6)
+ (8.78685 E -05) (30 * 30) + (97.2222 * 0.05 * 0.05) + ( 0.00994898 * 0.6 * 30) +
( 4.10714 * 0.6 * 0.05) ( 0.083333 * 30 * 0.05)
Ra = 1.84672 0.66405 0.0858843 0.06746 + 0.055 + 0.079 + 0.243 + 0.17908 + 0.1232142
0.124995
Ra = 1.5836
Error % = (1.64 -1.5836) / 1.64 * 100
= 3.43
CHAPTER 5
CONCLUSIONS
In this project we derived the following conclusions:
The effect of machining parameters on the surface roughness has been evaluated with thehelp of Taguchi method and optimal machining conditions to minimize the surface
roughness have been determined.
Taguchis method of Design Of Experiments is indeed an excellent statistical tool. Itreduces the number of experiments and cost and helps in optimizing and analyzing result.
Hardness is the dominant parameter for surface roughness as followed by depth ofcut.Feed rate shows the minimal effect on the surface roughness compared to other
parameters.
For achieving good surface finish, workpieces with high hardness are preferred.
MINITAB is a great software to analyse and optimizing the results .It can provide a largeamount of information about the experiment in fraction of seconds.
The derived geometrical model for surface roughness is
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Ra= 1.84672 - 1.10675x 0.00286281y 1.34921 z + 0.152778 x 2 + (8.78685 E -05) y2+
97.2222 z2 + 0.00994898 xy + 4.10714 xz 0.083333 yz
Is verified having an error of 3.5% approx. The above geometrical equation can be
widely used in many industry where machining of carbon rods are necessary with different depthof cut and feed.