Analysis on Surface Roughness

7/28/2019 Analysis on Surface Roughness

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CHAPTER 1

1 INTRODUCTION

1.1 Roughness

Roughness consists of surface irregularities which result from the various

machining process. These irregularities combine to form surface texture.

Roughness height is the height of the irregularities with respect to a re ference line.

Roughness width is the distance parallel to the nominal surface between

successive peaks.

1.2 Surface roughness

Fig 1.1 roughness and waviness profiles.

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Surface roughness, often shortened to roughness, is a measure of the texture of a

surface. It is quantified by the vertical deviations of a real surface from its ideal form. If these

deviations are large, the surface is rough; if they are small the surface is smooth. Roughness

is typically considered to be the high frequency, short wavelength component of a measured

surface.

Roughness plays an important role in determining how a real object will interact with

its environment. Rough surfaces usually wear more quickly and have higher friction

coefficients than smooth surfaces.

Roughness is often a good predictor of the performance of a mechanical component,

since irregularities in the surface may form nucleation sites for cracks or corrosion. On the

other hand, roughness may promote adhesion.

Although roughness is often undesirable, it is difficult and expensive to control in

manufacturing. Decreasing the roughness of a surface will usually increase exponentially its

manufacturing costs. This often results in a trade-off between the manufacturing cost of a

component and its performance in application.

A roughness value can either be calculated on a profile or on a surface. The profile

roughness parameter (Ra, Rq,...) are more common. The area roughness parameters (Sa,

Sq,...) give more significant values.

1.2.1 Profile roughness parameters

Each of the roughness parameters is calculated using a formula for describing the

surface. There are many different roughness parameters in use, but is by far the most

common. Other common parameters include , , and . Some parameters are used only in

certain industries or within certain countries.

Since these parameters reduce all of the information in a profile to a single number,

great care must be taken in applying and interpreting them. Small changes in how the raw

profile data is filtered, how the mean line is ca lculated, and the p hysics of the measurement

can greatly affect the calculated parameter.

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By convention every 2D roughness parameter is a capital R followed by additional

characters in the subscript. The subscript identifies the formula that was used, and the R

means that the formula was applied to a 2D roughness profile. Different capital letters imply

that the formula was applied to a different profile. For example, Ra is the arithmetic average

of the roughness profile, Pa is the arithmetic average of the unfiltered raw profile, and Sa is

the arithmetic average of the 3D roughness.

1.2.2 Amplitude parameters.

Amplitude parameters characterize the surface based on the vertical deviations of the

roughness profile from the mean line. Many of them are closely related to the parameters

found in statistics for characterizing population samples. For example, Ra is the arithmetic

average of the ab solute values and Rt is the range of th e collected roughness data points.

The average roughness, Ra, is expressed in units of height. In the Imperial (English)

system, 1 Ra is typically expressed in "millionths" of an inch. This is also referred to as

"microinches" or sometimes just as "micro" (however the latter is just s lang).

The amplitude parameters are by far the most common surface roughness parameters

found in the United States on mechanical engineering drawings and in technical literature.

Part of the reason for their popularity is that they are straightforward to calculate using a

computer.

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Table 1.1 Surface Roughness description and formula

Parameter Descriptio n Formula

Ra, Raa, Ryni arithmetic average of absolute values[1]

Rq, RRMS root mean squared[1]

Rv maximum valley depth

Rp maximum peak height

Rt Maximum Height of the Profile

Rsk skewness

Rku Kurtosis


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1.2.3 Slope, spacing, and counting parameters.

Slope parameters describe characteristics of the slope of the roughness profile. Spacing

and counting parameters describe how often the profile crosses certain thresholds. These

parameters are often used to describe repetitive roughness profiles, such as those produced by

turning on a lathe.

Table 1.2 slope of surface profile.

Parameter Description Formula

Rdq, R q

the RMS slope

of the profile

within the

sampling length

Rda, R a

the average

absolute slope

of the profile

within the

sampling length

i

where delta i is

calculated

according to

ASME B46.1

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1.3 Surface roughness in Manufacturing.

Many factors contribute to the surface finish in manufacturing. In forming processes,

such as molding or metal forming, surface finish of the die determines the surface finish of

the work piece. In mach ining the interaction of the cutting edges and the microstructure of th e

material being cut both contribute to the final surface finish. In general, the cost of

manufacturing a surface increases as the surface finish improves.

Just as different manufacturing processes produce parts at various tolerances, they are

also capable of different roughnesses. Generally these two characteristics are linked:

manufacturing processes that are dimensionally precise create surfaces with low roughness.

In other words, if a process can manufacture parts to a narrow dimensional tolerance, the

parts will not be very rough.

Due to the abstractness of surface finish parameters, engineers usually use a tool that

has a variety of surface roughnesss created using different manufacturing methods.

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Table 1.3 surface finesh in

manufacturing

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1.3 SURFACEROUGHNESSTESTER

Model SJ-201PR is comprised of two components: detachable detector/drive

and display unit which can house detector drive. Unit provides functions to communicate

Statistical Process Control (SPC) data with external devices via an RS-232C interface.

Product is compatible with ISO, DIN, ANSI and JIS standards and supports optional printer

for retrieving hard copies of profiles and parameters.

When it comes to surface roughness testing, at the process is where to measure. "The

Mitutoyo SJ-201PR has the added feature of gliding softly to the measuring surface, with

optional nosepiece or user specified fixture the SJ-201PR proves to be almost indestructible

on the shop floor," said a company spokesperson. "Virtual elimination of detector repairs

saves hundreds of dollars and head aches in the first year alone."

To operate the SJ-201PR, the display unit is placed on the surface to be measured;

pressing the start/stop key initiates detector traverse. The detecto r, which is positioned 3mm

above the surface, glides to the part and commences the measurement. The resulting

parameter values are then displayed on a large LCD screen.

The SJ-201PR comprises two components: a fully detachable detector/drive (about the size of

a pocket knife), and a display unit (about the size of a cordless phone) that can house the

detector drive.

The SJ-201PR is able to measure 19 surface roughness parameters: Ra, Ry, Rz, Rq, S, Sm,

Pc, R3z, mr, Rt, Rp, Rk, Rpk, Rvk, Mr1, Mr2, A1, A2, Vo. It is compatible with ISO, DIN,

ANSI and JIS standards.

An optional printer can be added for a hard copy of both profile and parameters. SurfPak

software is also available.

The SJ-201PR provides functions to communicate Statistical Process Control (SPC) data with

external devices including PCs and printers via an RS-232C interface.


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Fig 1. Photographic view of the surface roughness tester.

1.4 Surface roughness in CNC machining process.

Nowadays, due to the increasing demand of higher precision components for its

functional aspect, surface roughness of a machined part plays an important role in the modern

manufacturing process. Turning is a machining operation, which is carried out on lathe. The

quality of the surface plays a very important role in the performance of turning as a good

quality turned surface significantly improves fatigue strength, corrosion resistance, or creep

life. Surface roughness also affects several functional attributes of parts, such as, contact

causing surface friction, wearing, light reflection, heat transmission, ability of distributing

and holding a lubricant, load bearing capacity, coating, or resisting fatigue. To achieve the

desired surface finish, a good predictive model is required for stable machining. Generally,

these models have a complex relationship between surface roughness and operational

parameters, work materials, and chip breaker types

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Fig 1.1 CNC Machine.

1.5 Programmable logic Controller:-

A PLC matches the NC to the mach ine. PLCs were basically as replacement for hard

wired relay control panels. They were basically introduced as replacement for hard wired

relay panels. They developed to be re-programmed without hardware changes when

requirements were altered and thus are re-usable. PLCs are now available with increased

functions, more memory and larger input/output capabilities. In the CPU; all the decisions are

made relative to controlling a machine or a process. The CPU receives input data, performs

logical decisions based upon stored programs and drives the output s. connection to a

computer for hierarchical control are done via the CPU

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1.6 PROCEDURE OF PART PROGRAMMING.

Study the relevant component drawing thoroughly.

Identify the type of material to be machined.

Determine the specifications and functions of machine to be u sed.

Decide the dimension and mode i.e. Metric or inch.

Decide the co-ordinate system i.e. Absolute or incremental

Identify the plane of cutting.

Determine the cutting parameters for the job / tool combination.

Decide the feed rate programming.

Check the tooling required.

Establish the sequence of machining operations.

Identify whether use of special features like subroutines, mirror imaging etc. is

required or not.

Decide the mode of storing the part p rogram once it is completed.

1.7 STRUCTURE OF A PART PROGRAMMING

Part program defines a sequence of NC machining operations. The information

contained in the program can be dimensional or non-dimensional like speed, feed, auxiliary

functions, etc... The basic unit of a part program input to the control is called a block. Each

block contains adequate information for the machine to perform a movement and or

functions. Block in turn is made up of words and each word consists of a number of

characters. All blocks are terminated by the block end character. The maximum block length

for each CNC is f ixed. A block may contain any or all the fo llowing:-

Optional block skip (/).

Sequence or block number (N).

Preparatory functions (G).

Dimensional information (X, Y,Z etc. )

Decimal point (.).

Feed rate (F).

Spindle speed (S).

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Tool number (T).

Tool offset function (D).

Miscellaneous functions (M,H, etc. )

End of block (EOB / *)

1.8 CNC CUTTING PARAMETERS

Different parameters in CNC Machining to obtain surface finish.

1.8.1 Depth of cut,

The Thickness of material removed by one pass of cutting tool. Cutting speed and

feed rate come together with depth o f cut to determine the material removal rate, which is the

volume of work piece material that can be removed per unit time.

1.8.2 Cutting speed,

The rate at which the cutting edge of the tool moves past the work piece surface at the

point of contact. Cutting speed (also calledsurface speedor simply speed) may be defined as

the rate (or speed) that the material moves past the cutting edge of the tool, irrespective of the

machining operation used. A cutting speed for mild steel, of 100 ft/min (or approx. 30

meters/min) is the same whether it is the speed of the (stationary) cutter passing over the

(moving) workpiece, such as in a turning operation, or the speed of the (rotating) cutter

moving past a (stationary) workpiece, such as in a milling operation. What will affect the

value of this surface speed for mild steel are the cutting conditions:

For a given material there will be an optimum cutting speed for a certain set of machining

conditions, and from this speed the spindle speed (RPM) can be calculated. Factors affecting

the calculation of cutting speed are:

The material being machined (steel, brass, tool steel, plastic, wood) (see table

below)

The material the cutter is made from (Carbon steel, high speed

steel (HSS), carbide, ceramics)


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The economical life of the cutter (the cost to regrind or purchase new, compared

to the quantity of parts produced)

Cutting speeds are calculated on the assumption that optimum cutting conditions exist, these

include:

Metal removal rate (finishing cuts that remove a small amount of material may be

run at increased speeds)

Full and constant flow of cutting fluid (adequate cooling and chip flushing)

Rigidity of the machine and tooling setup (reduction in vibration or chatter)

Continuity of cut (as compared to an interrupted cut, such as machining square

section material in a lathe)

Condition of material (mill scale, hard spots due to white cast iron forming in

castings)

The cutting speedis given as a set of constants that are available from the material

manufacturer or supplier, the most common materials are available in reference books, or

charts but will always be subject to adjustment depending on the cutting conditions. The

following table gives the cutting speeds for a selection of common materials under one set of

conditions. The conditions are a tool life of 1 hour, dry cutting (no coolant) and at medium

feeds so they may appear to be incorrect depending on circumstances. These cutting speeds

may change if, for instance, adequate coolant is available or an improved grade of HSS is

used (such as one that includes cobalt).

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Cutting speeds for various materials using a plain high speed steel cutter

Material typeMeters per min

(MPM)

Surface feet per min

(SFM)

Steel (tough) 1518 5060

Mild steel 3038 100125

Cast iron (medium) 1824 6080

Alloy steels (13209262) 20-37 65120

Carbon steels (C1008-C1095) 21-40 70130

Free cutting steels (B1111-B1113 &

C1108-C1213)35-69 115225

Stainless steels (300 & 400 series) 23-40 75130

Bronzes 2445 80150

Leaded steel (Leadloy 12L14) 91 300

Aluminium 75105 250350

Brass 90-210300-700 (Max. spindle

speed)

.

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1.8.3 Feed rate,

The rate that the cutting tool travels along the surface of the work piece. 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 bo ring (typically inches per revolution [ipr]

or millimetres 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

millimetres per minute), with considerations of how many teeth (or flutes) the cutter has then

determining what that means for each too th.

Feed rate is dependent on the:

Type of tool (a small drill or a large drill, high speed or carbide, a boxtool

or recess, a thin 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 w ill collapse thin wall tubing)

Characteristics of the material being cut, chip flow depends on material type and feed

rate. The ideal chip shape is small and breaks free ear ly, carrying heat away from the tool and

work.

Threads per inch (TPI) fo r taps, die heads and threading tools.

When deciding what feed rate to use for a certain cutting operation, the calculation is fairly

straightforward for single-point cutting tools, because all of the cutting work is done at one

point (done by "one tooth", as it were). With a milling mach ine or jointer, where multi-

tipped/multi-fluted cutting tools are involved, then the desirable feed rate becomes d ependent

on the number of teeth on the cutter, as well as the desired amount of material per tooth to cut

(expressed as chip load). The greater the number of cutting edges, the higher the feed rate

permissible: for a cutting edge to work efficiently it must remo ve sufficient material to cut

rather than rub; it also must d o its fair share of work.

The ratio of the spindle speed and the feed rate controls how aggressive the cut is, and the

nature of the swarf formed.

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Formula to determine feed rate

This formula can be used to figure out the feed rate that the cutter travels into or

around the work. This would apply to cutters on a milling machine, drill press and a number

of other machine tools. This is not to be used on the lathe for turning operations, as the feed

rate on a lathe is given as feed per revolution.

Where:

FR = the calculated feed rate in inches per minute or mm per m inute.

RPM = is the calculated speed for the cutter.

T = Number of teeth on the cutter.

CL = The chip load or feed per tooth. This is the size of chip that each tooth of the cutter

takes.

1.8.4 Other CNC Cutting Parameters

1. Tool nose radius,

2. Tool overhang,

3. Approach angle,

4. Work piece length,

5. Work piece diameter,

6. Work piece material.


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CHAPTER 2

LITERATURE SURVEY

To study the Analysis of Surface Roughness on Different Materials in CNC Turning

Operation. This literature study guides my project to undergo in same procedure and

principles.

Ahilana.C, et al (2012),Decision-making process in manufacturing environment is

increasingly difficult due to the rapid changes in design and demand of quality products. To

make decision making process (selection of machining parameters) online, effective and

efficient artificial intelligent tools like neural networks are being attempted. This paper

proposes the d evelopment of neural network models for prediction of m achining parameters

in CNC turning process. Experiments are designed based on Taguchis Design of

Experiments (DoE) and conducted with cutting speed, feed rate, depth of cut and nose radius

as the process parameters and surface roughness and power consumption as objectives.

Developed models are validated and reported. Signal-to-noise (S/N) ratios of responses are

calculated to identify the influences of process parameters using analysis of variance

(ANOVA) analysis. Results obtained in this work are intended for use by numerical control

or manually operated machines.

AmanAggarwal, et al (2008), this paper optimizes multiple characteristics (tool life,

cutting force, surface roughness and power consumption) in CNC turning of AISI P-20 tool

steel using liquid nitrogen as a coolant. Four controllable factors of the turning process viz.

cutting speed, feed, depth of cut and nose radius, were studied. Face centred central

composite design was used for experimentation. Response surface methodology was used for

modelling the responses. Desirability function was used for single and multiple responseoptimization. Models developed were adequate in explaining the effect of independent

parameters on responses.3D plots for overall desirability function revealed the desirability

range when responses are given equal weight age. As clear from the plots low level of cutting

speed, feed and depth of cut are desirable for getting high value of desirability. Likewise high

value of nose radius is also desirable forgetting high value of desirability. Confirmation

experiments were done as output in Table and the % variation between actual experimental

data and predicted data was in between 6.25 and 2.6% which validates the results drawn

from desirability plot.

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AmanAggarwal, et al(2008). An experimental investigation into the effects of cutting

speed, feed rate, depth of cut, nose radius and cutting environment in CNC turning of AISIP-

20 tool steel.response surface methodology (RSM) and Taguchis technique, have been used

to accomplish the objective of the experimental study. L27 orthogonal array and face centred

central composite design have been used for conducting the experiments. The effects of feed

rate and nose radius were found to be insignificant compared to other factors. Time required

for conducting experiments using RSM technique was almost twice as that needed through

Taguchi technique. It is attributed to the fact that 180 (302, 302, 302) were performed

using face centred central composite design for three (dry, wet and cryogenic) cutting

environments whereas only 81 (273) experiments were performed using L27 orthogonal

array. Also ramp function graphs tell the exact level of parameters for desired level of

response. Thus RSM can better predict the effect of parameters on response and is a better

tool for optimization.

Anil Gupta, et al(2011), This paper presents the application of Taguchi method with

logical fuzzy reasoning for multiple output optimization of high speed CNC turning of AISI

P-20 tool steel using TiN coated tungsten carbide coatings. The machining parameters

(cutting speed, feed rate, depth of cut, nose radius and cutting environment) are optimized

with considerations of the multiple performance measures (surface roughness, tool life,

cutting force and power consumption).Taguchis concepts of orthogonal arrays, signal to

noise (S/N) ratio, ANOVA have been fuzzified to optimize the high speed CNC turning

process parameters through a single comprehensive output measure (COM).The result

analysis shows that cutting speed of 160 m/min, nose radius of 0.8 mm, feed of 0.1 mm/rev,

depth of cut of 0.2 mm and the cryogenic environment are the most favourable cutting

parameters for high speed CNC turning of AISI P-20 tool steel. In the multi-response

problem, all the four responses tool life, power consumption, cutting force and surfaceroughness were simultaneously considered. It can be concluded that middle level of cutting

speed(160 m/min) and nose radius (0.8 mm) and lower level of feed (0.1 mm/rev) and depth

of cut (0.2 mm) yield the optimal result. Both single response and multi-response

optimization analysis proved that cryogenic machining environment E3 is favourable in

increasing tool life and reducing surface roughness, cutting force and power consumption

compared to wet and dry m achining.

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Daniel Kirby.E, et al (2007), this paper discusses the development of a surface

roughness prediction system for a turning operation, using a fuzzy nets modelling technique.

The goal is to develop and train a fuzzy-nets-based surface roughness prediction (FN-SRP)

system that will predict the surface roughness of a turned work piece using accelerometer

measurements of turning parameters and vibration data. The FN-SRP system has been

developed using a computer numerical control (CNC) slant-bed lathe with a carbide cutting

tool. The system was trained using feed rate, spindle speed, and tangential vibration data

collected during experimental runs. A series of validation runs indicate that this system has a

mean accuracy of 95%.Providing machine tools with the capability to monitor quality

characteristics such as surface roughness is an essential component for the ability to create

reliable unmanned machining cells. This study explored the use of vibrations measured at the

cutting tool in a turning operation to predict surface roughness. A viable FN prediction

system was developed, using feed rate, spindle speed, and vibrations in the Y-axis, which

yielded excellent results. Test data was used to validate the model, which yielded an average

error rate of 95%.

Future research into varying the FN techniques, applying these techniques to different

experimental setups, and exploring ways to increase prediction accuracy

Exploration of the effect of various conditions such as different materials, material

variations, coolant, internal and external vibration sources, and tool wear

Development of a true in-process system, to determine the capability to predict

surface roughness deviations under various conditions.

Development of an adaptive control system which utilizes this predictive capab ility.

DurmusKarayel (2009). In this study, a neural network approach is presented for the

prediction and control of surface roughness in a computer numerically controlled (CNC)

lathe. Experiments have been performed on the CNC lathe to obtain the data used for the

training and testing of a neural network. The parameters used in th e experiment were reduced

to three cutting parameters which consisted of depth of cutting, cutting speed, and feed rate.

Each of the other parameters such as tool nose radius, tool overhang, approach angle,

workpiece length, work piece diameter and workpiece material was taken as constant. The

number of iterations was 8000 and no smoothing factor was used. Ra, Rz and Rmax were

modelled and were evaluated individually. One hidden layer was used for all models while

the numbers of neurons in the hidden layer of the Ra model were five and the numbers of

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neurons in the hidden layers of the Rz and Rmax models were ten. A new surface roughness

value was determined by sending the cutting parameters to the observer (ANN block).The

feed rate is a dominant parameter and the surface roughness increases rapidly with the

increase in feed rate. If the developed control algorithm is used, cutting parameters

corresponding to any surface roughness can be obtained to produce the desired surface

roughness. If the research is repeated with different p arameters for other machine tools, it can

be generalized and so can be applied to other machining types.

IlhanAsiltrk and HarunAkkus (2011), this study focuses on optimizing turning

parameters based on the Taguchi method to minimize surface roughness (Ra and Rz).

Experiments have been conducted using the L9 orthogonal array in a CNC turning machine.

Dry turning tests are carried out on hardened AISI 4140 (51 HRC) with coated carbide

cutting tools. Each experiment is repeated three times and each test uses a new cutting insert

to ensure accurate readings of the surface roughness. The statistical methods of signal tonoise ratio (SNR) and the analysis of variance (ANOVA) are applied to investigate effects of

cutting speed, feed rate and depth of cut on surface roughness. As a result, nine experiments

were conducted instead of the full factorial 27 experiments. Ra and Rz S/N ratios were found

as a result of exp eriments conducted according to the L9 orthogonal array.

Joseph Davidson. M, et al(2008), Design of experiments has been used to study the

effects of the main flow-forming parameters such as the speed of the mandrel, the

longitudinal feed, and the amount of coolant used on the surface roughness of flow-formed

AA6061 tube. A mathematical prediction model of the surface roughness has been d eveloped

in terms of the above parameters. The effect of these parameters on the surface roughness has

been investigated using response surface m ethodology (RSM). Response surface contours

were constructed for determining the optimum forming conditions for a required surface

roughness. The surface roughness was found to increase with increase in the longitudinal feed

and it decreased with decrease in the amount of the coolant used. The verification experiment

carried out to check the validity of the developed model predicted surface roughness within

6% error. RSM has been used to determine the surface roughness attained by the flow-formed

tubes for various input parameters namely the feed, speed and the coolant. A RSM model can

successfully relate the above process parameters with the response, surface roughness. The

verifying experiment has shown that the predicted value agrees with the experimental

evidence.


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Muammer Nalbant, et al(2009), In this study the machining of AISI1030 steel (i.e.

orthogonal cutting) uncoated, PVD- and CVD- coated cemented carbide insert with different

feed rates of 0.25, 0.30, 0.35, 0.40 and 0.45mm/rev with the cutting speeds of 100, 200 and

300m/min by keeping depth of cuts constant (i.e.2mm), without using cooling liquids has

been accomplished. The surface roughness effects of coating method, coating material,

cutting speed and feed rate on the work piece have been investigated. Among the cutting

tools with 200mm/min cutting speed and 0.25mm/rev feed rate the TiN coated with PVD

method has provided 2.16 mm. While the uncoated cutting tool with the cutting speed of

100m/min and 0.25mm/rev feed rate has yielded the surface roughness value of 2.45 mm.

The training and test data of the ANNs have been prepared using experimental patterns for

the surface roughness. Decreasing the friction of coefficient and thermal conductivity of the

cutting tool decrease the average surface roughness of the machining material. There is a

positive linear relationship between the average surface roughness of u ncoated and coated

cemented carbide cutting tools and the feed rate used at cutting operation. The

bestaveragesurfaceroughnessvaluesat200m/min cutting speed with the feed rate of

0.25mm/rev are the following:

TiN - coated cutting tool with CVD method 2.16 mm,

TiAlN- coated cutting tool with PVD method2.3 mm,

AlTiN coated cutting tool with PVD method 2.46 mm,

In the case of uncoated cutting tool, at the cutting speed of100m/min and feed rate of

0.25mm/rev the result is2.45 mm.

Oktem. H, et al(2005), This paper focuses on the development of an effective

methodology to determine the optimum cutting conditions leading to minimum surface

roughness in milling of mold surfaces by coupling response surface methodology (RSM) with

a developed genetic algorithm (GA).RSM is utilized to create an efficient analytical model

for surface roughness in terms of cutting parameters: feed, cutting speed, axial depth of cut,

radial depth of cut and machining tolerance. For this purpose, a number of machining

experiments based on statistical three-level full factorial design of experiments method are

carried out in order to collect surface roughness values. An effective fourth order response

surface (RS) model is developed u tilizing experimental measurements in the mold cavity. RS

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model is further interfaced w ith the GA to optimize the cutting conditions for desired surface

roughness. The GA reduces the surface roughness value in the mold cavity from 0.412m to

0.375mcorresponding to about 10% improvement. Optimum cutting condition produced from

GA is verified with the experimental measurement. In this study, a fourth order RS model for

predicting surface roughness values in milling mold su rfaces made of Aluminium (7075-T6)

material was developed. Surface roughness of th e mold surfaces, which was 0.412 _m before

optimization, was reduced to 0.375 _m after optimization. GA improved the surface

roughness by about 10%.Difference was found to be less than 1.4% . This indicates that the

optimization methodology proposed in this study by coupling the developed RS model and

the developed GA is effective and can be utilized in other machining problems such as tool

life, dimensional errors, etc.as well.

Ramesh S, et al (2012), the effect of cutting parameters on the surface roughness in

turning of titanium alloy has been investigated using response surface methodology. The

experimental studies were conducted under varying cutting speeds, feed and depths of cut.

The chip formation and SEM analysis are discussed to enhance the supportive surface q uality

achieved in turning. The work material used for the present investigation is commercial

aerospace titanium alloy (gr5) an d the tool used is RCMT 10T300 MT TT3500 round insert.

The equation developed using response surface methodology is used for predicting the

surface roughness in machining of titanium alloy. Using the Design of Experiments concept,

the experiments was designed using Taguchis orthogonal array principle titanium alloy was

machined using a RCMT 10T300 MT TT3500 round insert in dry machining

condition.Taguchi ANOVA analysis was performed. The most influencing parameter was

identified as the feed. The order of importance was feed, followed by depth of cut and cutting

speed.The surface damage owing to the interaction tool/work observed through SEM

analysis.

Satheesh Kumar.N, et al (2012), the effect of process parameters in turning of Carbon

Alloy Steels in a CNC lathe. The parameters namely the spindle speed and feed rate are

varied to study their effect on surface roughness. The five different carbon alloy steels used

for turning are SAE8620, EN8, EN19, EN24 and EN47.The study reveals that the surface

roughness is directly influenced by the spindle speed and feed rate. It is observed that the

surface roughness increases with increased feed rate and is higher at lower speeds and vice

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versa for all feed rates. The better surface finish may be achieved by turning carbon alloy

steels at low feed rate and high spindle speeds. It should also be noted that the turning

operation for all work pieces carried out sequentially.

SuleymanNeseli, et al (2011), This investigation focuses on the influence of tool

geometry on the surface finish obtained in turning of AISI 1040 steel. In order to find out the

effect of tool geometry parameters on the surface roughness during turning, response surface

methodology (RSM) was used and a prediction model was developed related to average

surface roughness (Ra) using experimental data. The results indicated that the tool nose

radius was the dominant factor on the surface roughness. In addition, a good agreement

between the predicted and measured surface roughness was observed. Therefore, the

developed model can be effectively used to predict the surface roughness on the machining of

AISI 1040 steel within 95% confidence intervals ranges of parameters studied.

The result of ANOVA proved that the quadratic mathematical models allow

prediction of surface roughness parameter with a 96% confident interval.

Tool nose radius is the most significant factor on surface roughness with 51.45%

contribution in the total variability of model. The quadratic effect of tool nose radius

little provides little contribution to the model.

Also, approach angle and rake angle are significant factors on surface roughness with

18.24% and17.74% contribution in the to tal variability of model, respectively.

It can be said that the interaction between all factors has no significant effect on

surface roughness.

Using response optimization show that the optimal combination of machining

parameters are (0.4 mm, 60, 3) for tool nose radius, approach an gle and rake angle,

respectively.

Suresh P.V.S, et al (2002), Due to the widespread use of highly automated machine

tools in the industry, manufacturing requires reliable models and methods for the predictionof output performance of machining processes. The prediction of optimal machining

conditions for good surface finish and dimensional accuracy plays a very important role in

process planning. The present work deals with the study and development of a surface

roughness prediction model for machining mild steel, using Response Surface Methodology

(RSM). The experimentation was carried out with TiN-coated tungsten carbide (CNMG)

cutting tools, for machining mild steel work-pieces covering a wide range of machining

24

conditions. This model gives the factor effects of the individual process parameters. An

attempt has also been m ade to optimize the surface roughness prediction model using Genetic

Algorithms (GA) to optimize the objective function. The GA program gives minimum and

maximum values of surface roughness and their respective optimal machining conditions.

The two-stage effort of obtaining a surface roughness model by surface response

methodology, and optimization of this model by Genetic Algorithms, has resulted in a fairly

useful method of obtaining process parameters in order to attain the required surface quality.

This has validated the trends available in the literature, and extended the data range to the

present operating conditions, apart from improving the accuracy and modelling by involving

the most recent modelling method.

VikasUpadhyay, et al (2011), in this work, an attempt has been made to use vibration

signals for in-process prediction of surface roughness during turning of Ti6Al4V alloy.

The investigation was carried out in two stages. In the first stage, only accelerat ion amplitudeof tool vibrations in axial, radial and tangential directions were used to develop multiple

regression models for prediction of surface roughness. The first and second order regression

models thus developed were not found accurate enough (maximum percentage error close to

24%). In the second stage, initially a correlation analysis was performed to determine the

degree of association of cutting speed, feed rate, and depth of cut and the acceleration

amplitude of vibrations in axial, radial, and tangential directions with surface roughness.

Subsequently, based on this analysis, feed rate and depth of cut were included as input

parameters aside from the acceleration amplitude of vibrations in radial and tangential

directions to develop a refined first order multiple regression model for surface roughness

prediction. This model provided good prediction accuracy (maximum percentage error

7.45%) of surface roughness. Finally, an artificial neural network model was developed as it

can be readily integrated into a computer integrated manufacturing environment. Pearson

correlation coefficient was used to determine the correlation between surface roughness and

cutting parameters and acceleration amplitude of vibrations. Pearson correlation coefficient

for feed rate was maximum followed by acceleration amplitude of vibration in radial

direction, depth of cut and acceleration amplitude of vibration in tangential direction. As this

model was found accurate enough, neural network model was developed using the same

combination of input parameters. To check the adequacy of developed models, the models

were validated with the data not used in development of models.


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25

CHAPTER 3

3.1 Material Preparation

Aluminum alloys may be strengthened by addition of copper, silicon and Tin.

3.1.1 Silicon (Si)

Some of aluminum based bearing alloys contain silicon. Silicon has very high

hardness and its inclusions distributed over the aluminum matrix serve as abrasive particles

polishing the mating journal surface.

3.1.2 Tin (Sn)

Aluminum based bearing alloys commonly contain tin as a soft component. Tin is

distributed in aluminum matrix as a separate phase in form of a reticular (network) structure

along the edges of aluminum grains. Tin imparts to the material anti-friction properties

(compatibility, conformability, embedability).

3.1.3Copper (Cu)

The aluminum-copper alloys typically contain 1.2% copper, with s maller additions of

other elements. The copper provides substantial increases in strength and facilitates

precipitation harden ing. The introduction of co pper to aluminum can also redu ce duc tility

and corrosion resistance.

3.1.4 Material to be selected.

o Phosphor-bronze(Cu (85%) , Sn(10-12%), P(0.001%), Ni(1%) with boron carbide

(8 micron size)

o Aluminum with (Sn(22%), Cu(1.2%))

o Aluminum with (Si(36%), Mg(1.5%)

26

3.2 EXPERIMENTAL DESIGN AND RESULTS

Experimental design involves variation of three factors (cutting speed, feed rate an d

depth of cut) at three levels as mentioned

Table 1.4 Machining parameters and levels

Symbol Control factor Unit Level 1 Level 2 Level 3

V Cutting speed rpm 1500 2000 2500

F Feed rate mm/rev 0.15 0.2 0.25

A Depth of cut mm 1 1.5 2

3.2.1 Surface Roughness Measurement.

Each of the roughness parameters is calculated using a formula for describing the

surface. There are many different roughness parameters in use, but is by far the most

common. Other common parameters include , , and . Some parameters are used only in

certain industries or within certain countries.

Since these parameters reduce all of the information in a profile to a single number,

great care must be taken in applying and interpreting them. Small changes in how the raw

profile data is filtered, how the mean line is calculated, an d the physics of the m easurement

can greatly affect the calculated parameter.

By convention every 2D roughness parameter is a capital R followed by additional

characters in the subscript. The subscript identifies the formula that was used, and the R

means that the formula was applied to a 2D roughness profile. Different capital letters imply

that the formula was applied to a different profile. For example, Ra is the arithmetic average

of the roughness profile, Pa is the arithmetic average of the unfiltered raw profile, and Sa is

the arithmetic average of the 3D roughness.

27

Table 1.5 Experimental results of training patterns and actual surface roughness values

Sample

number

Cutting speed (v)

(rpm)

Feed rate (f)

(mm/rev)

Depth of cut (d)

(mm)

Actual roughness

(Ra) (m)

12500 0.15 1 3.47

2 2000 0.15 1.5 3.87

3 1500 0.15 2 4.41

4 2500 0.2 1 4.94

5 2000 0.2 1.5 4.72

6 1500 0.2 2 5.46

7 2500 0.25 1 5.61

8 2000 0.25 1.5 5.72

9 1500 0.25 2 5.82

10 2500 0.15 1 1.68

11 2000 0.15 1.5 1.97

12 1500 0.15 2 2.17

13 2500 0.2 1 2.47

14 2000 0.2 1.5 2.97

15 1500 0.2 2 3.44

16 2500 0.25 1 3.57

17 2000 0.25 1.5 4.73

18 1500 0.25 2 4.81

19 2500 0.15 1 0.52

20 2000 0.15 1.5 0.68

21 1500 0.15 2 0.88

22 2500 0.2 1 1.68

23 2000 0.2 1.5 1.79

24 1500 0.2 2 1.91

25 2500 0.25 1 2.33

26 2000 0.25 1.5 2.36

27 1500 0.25 2 2.69

28

3.3 ARTIFICIAL NEURAL NETWORKS

Artificial neural networks (ANNs) are information processing systems, and since their

inception, they have been used in several areas of engineering applications. In experimental

studies, some of the operating conditions of a system can be investigated. For this type of

experimental work, experts and special equipment are needed. It also requires too much time

and high cost.

3.3.1 Development of neural network model

In the present work, a feed-forward back-propagation training algorithm is employed

for predicting the surface ro ughness in CNC turning process. Training begins with all weights

set to random numbers. For each data record, the predicted value is compared to the desired

(actual) value and the weights are adjusted to move the prediction closer to the desired value.

Many cycles are made through the entire set of training data with the weights being

continually adjusted to produce more accurate predictions.

3.3.2 Execution of experiments

The experimental set-up and the tests were performed on a CNC turning center. The

training data set was developed through experiments based on L27 Taguchi orthogonal array

[16]. The orthogonal array were assigned to cutting speed (v), feed rate (f), and depth of cut

(d), respectively, and accordingly 27 experiments were performed under different

combinations of the factor levels. The aluminium and bronze composite metal specimens

with dimensions each of diameter 30 mm and length of 150 mm was clamped onto to the

turret of the machine tab le. Surface roughness measurement was done in the off-line with the

usage of TIME TR100 surface roughness tester. The radius of the stylus point is 10.02.5

micron and the traversed length is 6 mm. The experimental set-up consists of a CNC

machine, battery unit for backup purpose, power supply, and the whole set-up is connected to

the computer interface. A computer numeric control (CNC) program was written to perform

the turning process. According to the acceptable ranges of cutting speed and feed rate when

cutting brass with a carbide insert with a tool holder PCLNR120408 and nose radius of 0.8,

and then an NC program was written to execute the cutting operations. Three levels of each


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29

factor were selected as shown in Table 1. Including test runs, there were totally 6 specimens

machined in this experiment. All the specimens in this experiment were machined without

coolant. At high cutting speeds, there will be no BUE. Furthermore, in CNC, sculptured tool

holders are used, and hence, the inserts are replaced in its position, depending on the wear

rate of the tool inserts. In addition, the following assumptions were made

(1)Cutting tools used are identical in property.

(2)The hardness of each work p iece is same throughout the length of the work piece.

(3)Surface roughness values are not affected by abnormal factors.

(4)Surface roughness values measured within the measuring area are sufficient to

represent

(5)The roughness of entire work p iece.

(6)The effect ofapproach angle is not considered.

(7)Vibration is negligible.

(8)Tool nose radius is constant.

After turning all the specimens, the surface rou ghness (Ra) was measured by using the

surface roughness tester and shown in Table 2. The measurement was done separately and the

measured Ra values are utilized for the purpose of training the developed neural networks.

The parameters which influence the surface roughness are taken into consideration. A

combined set of test values are also obtained with the values of surface ro ughness, which can

be compared after implied with the neural network procedure.

3.3.3 Architecture of the proposed artificial neural network

Figure 3 shows the developed architecture of artificial neural network and comprises

one input layer with 3 neurons, one output layer with 1 neuron and one hidden layer with 9

neurons in the layers, respectively. The links with synaptic weights are connected between

neurons and the back-propagation training algorithm is based on weight updates so as to

minimize the sum of squared error for K number of output neurons, given as

E= (1)

Where dk,p=desired output for the pth pattern. The weights of the links are upd ated as

w ji(n+1)= w ji(n)+ pj opi + wji(n) (2)

30

Where n is the learning step, is the learning rate and is the momentum constant. In

Eq. 4, pj is the error term, which is given as follows:

For output layer:

pk=(dkp - okp )(1 - okp ), k = 1,.K (3)

Fig. 3 ANN architecture with a sin gle hidden layer.

For hidden layer:

pj

= opj

(1 - opj

) , j = 1,.J (4)

Where J is the number of neurons in the hidden layer. The training process is initialized by

assigning small random weight values to all the links. The inputoutput patterns are presented

one by one and updating the weights each time. The mean square error (MSE) at the end of

each epoch due to all patterns is computed as

MSE = (5)

Where NP=number of training patterns. The training process will be terminated when the

specified goal of MSE or maximum number of epochs is achieved. The activation function

31

for the input and the two hidden layers is chosen as tansigmoidal function. The activation

function for the output layer is chosen as pure linear function. The network is then simulated

for the input values and a graph is plotted between the output and target (neural network

output) values. The network created is trained for the input and output values. The stopping

criterion for training was number of epochs and is given as 590. The network is again

simulated for the input values and the target values of the experiments conducted. The input

values for the test readings are then given and the network is trained. The target value is then

obtained and compared with actual output. The predicted Ra values are compared with the

actual Ra values and the predicted Ra values obtained from the present study show minimal

in variations. The parameters taken could be confidently used for the above method for

predicting the Ra values. The behaviors of the parameters are also noted. The predicted value

of Ra is compared with the respective measured average values of Ra and the absolute

percentage error is computed, which is given as

% Absolute error = (6)

Where Ra, actual is the measured value (average) and Ra, predicted is the ANN predicted

value of the response forith

trial.

32

Table 2.1 Experimental results and predicted reading and actual roughness:

Sample

number

Cutting

speed (v)

(rpm)

Feed rate

(f)

(mm/rev)

Depth

of cut

(d)

(mm)

Actual

roughness

(Ra) (m)

Predicted

roughness

(Ra)

Difference % Error

1 2500 0.15 1 3.47 3.9869 0.5169 17.5036402

2 2000 0.15 1.5 3.87 4.4269 0.5569 16.8090308

3 1500 0.15 2 4.41 4.8155 0.4055 10.1261081

4 2500 0.2 1 4.94 5.318 0.378 8.28583954

5 2000 0.2 1.5 4.72 5.3179 0.5979 14.5047427

6 1500 0.2 2 5.46 5.8918 0.4318 8.58756613

7 2500 0.25 1 5.61 6.1038 0.4938 9.65169462

8 2000 0.25 1.5 5.72 6.1336 0.4136 7.79436153

9 1500 0.25 2 5.82 6.1915 0.3715 6.81839038

10 2500 0.15 1 1.68 2.0335 0.3535 26.6490765

11 2000 0.15 1.5 1.97 2.3238 0.6538 49.6733019

12 1500 0.15 2 2.17 2.6168 0.4468 25.9285051

13 2500 0.2 1 2.47 2.9522 0.4822 24.2579736

14 2000 0.2 1.5 2.97 3.5105 0.5405 22.247376

15 1500 0.2 2 3.44 4.0122 0.5722 19.9525769

16 2500 0.25 1 3.57 4.0921 0.5221 17.1298271

17 2000 0.25 1.5 4.73 5.3122 0.5822 14.0363566


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18 1500 0.25 2 4.81 5.2972 0.4872 11.2704728

19 2500 0.15 1 0.52 0.6146 0.1946 59.803319

20 2000 0.15 1.5 0.68 0.7942 0.2642 63.5401635

21 1500 0.15 2 0.88 1.0022 0.1222 16.1256268

22 2500 0.2 1 1.68 1.9302 0.2502 17.4989509

23 2000 0.2 1.5 1.79 2.0211 0.2311 14.8245558

24 1500 0.2 2 1.91 2.0603 0.1503 8.54122862

25 2500 0.25 1 2.33 2.4611 0.1311 5.96207195

26 2000 0.25 1.5 2.36 2.4922 0.1322 5.9341054

27 1500 0.25 2 2.69 2.7504 0.0604 2.29692729

34

CHAPTER 4

4 RESULTS AND DISCUSSION

The actual roughness values have been calculated for each set of readings and the

same is compared with predicted roughness values. The behaviour of roughness with various

parameters is analyzed and the influence of each parameter over surface roughness is

identified from the experiments carried out. The percentage deviation between actual

roughness values and predicted roughness values have been obtained and shown in Table 3

and also it is calculated that the ave rage percentage of error is 12.93%.

4.1 Comparison of graphical results

Figure 6 shows comparison between actual and predicted roughness values and it is

observed that there is a good agreement between experimental and predicted Ra values. The

test values obtained from the neural network after training the values are found to be closer to

the experimental values. The 3D plots were drawn using Minitab 5.0 software package to

identify the influence of the parameters over surface roughness for the experimental values.

Optimization of cutting parameters can be obtained for the usage of the same in machining

area in mass production. This will reduce the inspection of the product, which is a quality

check in any industry. The material is widely used in the pump industry where surface

roughness plays an important role. The methodology using ANN for predicting parameters

are utilized for the same actual roughness values. The test values obtained from the neural

network after training the values are found to be closer to the experimental values. The 3D

plots were drawn to identify the influence of the parameters over surface rou ghness for th e

experimental values. Optimization of cutting parameters can be obtained for the usage of the

same in machining area in mass production. This will reduce the inspection of the product,

which is a quality check in any industry. The material is widely used in the pump industry

where surface roughness plays an important role. The methodology using ANN for predicting

parameters are utilized for the same.

Figure 9.1 shows the interaction plot between surface roughness and speed with

respect to feed. If the feed rate increases gradually towards the speed, the surface roughness

value decreases proportionately. The surface roughness will be improved by increasing the

feed for higher speeds.

35

Figure 9.2 shows the interaction plot between surface roughness and f with respect to

speed. If the Speed is increased with respect to the feed, the surface roughness value

increases and decreases. The surface roughness will be improved for higher speeds by

increasing the feed rate.

Figure 10.1 shows the interaction plot between surface roughness and depth of cut

with respect to speed. Depth of cut (DOC) influences more on su rface roughness and if DOC

is increased, the surface roughness values will also increase for variable speeds. The

roughness values are decreased for m inimum DOC values with respect to speed.

Figure 10.2 shows the interaction plot between surface roughness and speed with

respect to DOC. If the speed is increased gradually, the surface roughness value increases

with respect to the DOC. The surface roughness will be improved by decreasing the speed for

depth of cut values. The cutting parameters play an important role in obtaining the surface

roughness of a machined part. These variables are independent and hence it is analyzed with

a methodology using ANN to obtain a model which will be useful for the industries.

Figure 11.1 shows the interaction plot between surface roughness and DOC with

respect to feed. If the feed rate is increased gradually towards the DOC, the surface roughness

value decreases .The surface roughness will be improved by increasing the feed for DOC

values. The surface roughness value is higher when the feed rate is not increased for higher

depth of cut values. The DOC plays an important role as a parameter in CNC turning. Hence,

the parameters are analyzed for obtaining improved surface roughness characteristics.

Optimization of the same may be done with the h elp of ANN.

Figure 11.2 shows the interaction plot between surface roughness and feed with

respect to DOC. If the feed rate is increased gradually towards the DOC, the surface

roughness value decreases.

36

Fig 6 Comparison of actual and predicted roughness


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37 38

Fig 9 The actual roughness Ra value o btained aluminium with silicon composite material.

0.15

0.2

0.25

3.00

3.50

4.00

4.50

5.00

5.50

6.00

15002000

2500

Feedrate

Actualroughness

Cutting speed

Surface Plot of Ra vs Speed, Feed

0.1

1.5

2

2

2.5

3

3.5

4

4.5

5

5.5

6

15002000

2500

De

pth

ofcutA

ctualroughness

Cutting speed

Surface Plot of Ra vs Speed, depth of

cut

Fig 10 The actual roughness Ra value obtained aluminium with tin composite material.

39

0.15

0.2

0.25

0.00

1.00

2.00

3.00

4.00

5.00

6.00

15002000

2500

Feed

rate

Actualroughness

Cutting speed


0.1

1.5

2

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

15002000

2500

Depth

ofcut

Actualroughness

Cutting speed

Surface Plot of Ra vs Speed, depth of

cut

40

Fig 11 The actual roughness Ra value obtained for bronze composite material

0.15

0.2

0.25

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

15002000

2500

Feed

rate

Actualroughness

Cutting speed


0.1

1.5

2

0

0.5

1

1.5

2

2.5

3

15002000

2500

Depth

ofcut

Actualroughness

Cutting speed

Surface Plot of Ra vs Speed, depth of cut


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41

CHAPTER 4

4. PROBLEM DEFINITION

Surface roughness was important role in machining process, it may varied to its

material, cutting parameters, type of cutting tools used. To analysis the surface roughness on

different machining parameters on CNC turning operation for the prediction of surface finish.

To bring out a finished work p iece with required surface quality in order to gives a perfect

mating with other parts. The parameters namely the sp indle speed, depth of cut and feed rate

are varied to study their effect on surface roughness.

4.1 OBJECTIVE

The objective of this project work is the prediction and analysis of surface roughness

on different materials for different machining parameters during turning operations in a

Computer Numerically Controlled (CNC) Lathe.

The main purpose of the project to bring out a finished work piece with required

surface quality in order to gives a perfect mating with other parts.

42

CHAPTER 5

METHODOLOGY

.

STUDY OF CNC TURNING PARAMETERS

STUDY OF SURFACE ROUGHNESS ANAL YSIS.

LITERATURE SURVEY

REPORT OF PHASE -1

SELECTION OF MATERIALS

OBJECTIVE

SELECTION OF TURNING PARAMETERS LIKE

TOOL, SPEED, FEED,. Etc.

TURNING OPERATION

SURFACE ROUGHNESS MEASUREMENT/ANALYSIS.

COMPARISION & TABLE PREPARATION

CONCLUSION/ REPORT PREPARATION

Phase-1

Phase-2

43

CHAPTER 6

REFERENCES

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