introduction final report

7/30/2019 Introduction Final Report

1/35

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.

1


2/35

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

2


3/35

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

3


4/35

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.

4


5/35

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

5


6/35

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.

6


7/35

Table 2.1 Array Selector Matrix

7


8/35

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.

8


9/35

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



P8 = interaction between P1, P2, P3

According to a rule, the unwanted parameters in array table can be neglected. Thus P9 to P13 areneglected.

9


10/35

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

10


11/35

( )=

=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

11


12/35

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 .

12


13/35

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

13


14/35

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.

14


15/35

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

15


16/35

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.

16


17/35

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.

17


18/35

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

18


19/35

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


20/35

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


21/35

Figure 3.2 : Surtronic 3+

Advantages

Total portability for workshop or laboratory

Fast, simple operation21


22/35

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

22


23/35

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

23


24/35

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

24


25/35

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

25


26/35

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)

26


27/35

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 :

27


28/35

Table 4.1: Experimental Ra values

28


29/35

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.

29


30/35

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

30


31/35

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

31


32/35

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

32


33/35

*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

33


34/35

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

34


35/35

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.

introduction final report

Documents