# Experimental investigation and optimization of surface roughness of AISI 52100 alloy steel material by using Taguchi method.

INTRODUCTIONHard turning is a lathe machining process where a AISI 52100 Alloy steel work piece is either turned or, faced , bored chamfered or grooved by using a much harder cutting tool medium namely tungsten carbide and. This process can be performed on relatively inexpensive lathes as long as the machines are sturdy and highly consistent. The cutting tool is most critical elements in the various machining process, since the tool material should be harder and tougher of the machine workpiece. Tool material improvement has been characterized and finalized by an increase in wear resistance and thermal resistance. Typical cases include high speed steel, cemented tungsten carbide and polycrystalline diamond.

In hard turning tool material used should have high mechanical strength, hardness and chemically inert even at high temperature. Ceramics and CBN are widely used under cutting condition. H.K Tonshoff [1] has identified that interrupted cutting condition CBN are used but ceramics cannot be used because of low fracture toughness. Also CBN is the appropriate one due to its high thermal conductivity and low thermal co-efficient of thermal expansion. Even though PCD is harder than CBN, it cannot be used as diffusion ability of carbon in ferrous material is high even at low temperature.

Literature Survey:

Zhang et al, [1] proved that Hard turning has been recognized as a substitute for abrasive-based processes due to its Operational flexibility, economic benefit, and low environmental impact. Besides these advantages, hard turning can induce compressive residual stresses, which increase the fatigue life of the work piece.

Horng et al., [2] in their paper focuses on the development of a fast and effective algorithm to determine the optimum manufacturing conditions fortuning Hadfield steel with [Al.sub.2][O.sub.3] /TiC mixed ceramic tool by coupling the grey relational analysis with the fuzzy logic. The flank wear and surface roughness were adopted to evaluate the machiniablity performances. Various cutting parameters, such as cutting speed, feed rate ,depth of cut and nose radius of tool were explored by experiment.

Eichlseder [3] et al., in their paper said that Steel components often have to be machined after heat treatment in order to obtain the correct shape as well as the required surface finish. Surface quality influences characteristics such as fatigue strength, wear rate, corrosion resistance, etc. Hard turning allows manufacturers to simplify their processes and still achieve the desired surface finish quality.

Jain et al., [4] In their study, an attempt has been made to investigate the effect of cutting parameters(cutting speed, feed rate and depth of cut) on cutting forces (feed force, thrust force and cutting force) and surface roughness in finish hard turning of MDN250 steel (equivalent to 18Ni(250) maraging steel) using coated ceramic tool. The machining experiments were conducted based on response surface methodology (RSM) and sequential approach using face centered central composite design.

Sahin [5] This paper describes a comparison of tool life between ceramics and cubic boron nitride (CBN) cutting tools when machining hardened bearing steels using the Taguchi method. An orthogonal design, signal-to-noise ratio (S/N) and analysis of variance (ANOVA) were employed to determine the effective cutting parameters on the tool life. First order linear and exponential models were carried out to find out the correlation between cutting time and independent variables. Second order regression model was also extended from the first order model when considering the effect of cutting speed (V), feed rate (f), hardness of cutting tool (TH) and two-way of interactions amongst V, f, TH variables.

Guo et al., [6] in their paper Surface topography induced by precision machining is critical for component performance. Four representative surface topographies of turned and ground surfaces were prepared at "extreme" machining conditions (gentle and abusive) and compared in terms of 3- dimensional (3D) surface features of amplitude, area and volume, spatial, and hybrid parameters..

Pawade [7] et al., in their paper said that stringent control on the quality of machined surface and subsurface during high-speed machining of Inconel 718 is necessary so as to achieve components with greater reliability and longevity. This paper extends the present trend prevailing in the literature on surface integrity analysis of super alloys by performing a comprehensive investigation to analyze the nature of deformation beneath the machined surface and arrive at the thickness of machining affected zone (MAZ).

He Design of Experiments Process:

The DOE process is divided into three main phases which encompasses all experimentation approaches.

The three main phases are

1. Planning phase

2. Conducting phase

3. Analysis phase

Experimental methodology:

Introduction:

The second phase of design of experiments is conducting phase. In conducting phase experiment are conducted for the selected process parameter combinations at a random order. The conducting phase involves the following tasks.

1. Preparation of work piece

2. Procurement of insert

3. Conducting experiments

4. Measuring surface roughness

5. Measuring cutting forces.

Work Material Preparation:

The work material is AISI 52100 Alloy Steel (En31). The different property such as physical, mechanical thermal properties and composition of AISI 52100 Alloy Steel (En31) is given below.

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Work Material specification:

TYPE--AISI 52100 Alloy Steel (En31)

SIZE--60x220mm

SHAPE--Solid cylinder without interruptions

Heat Treatment:

Before the heat treatment process, the work pieces have to be returned. This is done in order to eliminate the tapers in the work pieces. During returning, the diameter of the work piece was reduced from 62mm to 60mm.The heat treatment for the AISI 52100 work piece has been carried out using the gas carburizing furnace. The temperature to 840[degrees]C at a rate of 150[degrees]C/Hr. After that the soaking (holding) process was carried out. This is the process of holding the temperature at 840[degrees]C. The holding time for our component was approximately 2 hours. After that oil quenching was done for one hour. The hardness of the work piece increases to an extend than the target hardness after this process. So in order to bring back the required hardness tempering is done 2 times in a tempering furnace. It took 21/2 hours and the temperature was 400[degrees]C--420[degrees]C. Finally, air cooling is done. The final hardness is achieved was around 55 HRC.

Tooling:

Cutting tool:

Due to high resistance and easy machinability for the hardened steel (AISI 52100), the tool material chosen for the study is Cubic Boron Nitride (CBN). The tool insert is CCMT 21.51 with nose radius 0.4MM.PTFNR tool holder is used which provided 5[degrees] side cutting-edge angle, 50 end cutting--edge angle, -5[degrees] back rake angle.

Machine Tool Specification:

A computer numerical control is used for conducting experiments. Specifications of the HMT ECONO 26 machine are shown in Table

Experimental Setup:

Experiments are conducted on HMT ECHONO CNC 26 LATHE IN PSG Industrial Institute, Coimbatore. For different sets of machining conditions experiments are conducted in order to obtain the surface roughness, cutting forces and temperatures. For measuring cutting forces nine set of experiments were conducted as per DOE (Design of Experiments). The work material is fixed to the chuck and the job is centered. The insert is clamped to the tool holder and the necessary settings are made. The process parameters selected for the experiments are speed, feed and depth of cut. The turning operations are carried out with the Tungsten carbide insert which are specified earlier. The entire experiments are carried out in dry condition without using any coolant.

Experimental Design:

According to the Taguchi's design an L9 orthogonal array is selected and nine combinations of experiments are performed for the selected levels of cutting parameters given.

Surface roughness measurement: Surfcorder: Specification of surfcorder are listed below: 1. Maximum measuring length 25mm 2. Straightness Accuracy 8[micro]m/10mm 3. Maximum range resolution 520 [micro]m/.008 [micro]m 4. Cut of length(?c) 08,.025,.08,2.5mm 5. Evaluation length [lambda]c x1,2,3,4,5/ any length of .08-25mm 6. Measuring Speed .2,.5mm/sec 7. Return speed (Auto) 2,.5,1mm/sec

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RESULTS AND DISCUSSION

Optimization of surface roughness:

In this study MINITAB-15 Statically software is used to analyses taguchi L9 array. Main effects Plot and interaction plots for surface roughness and S/N ratio given in Fig 7.1-7.8. The surface roughness value should be low and S/N ratio should be high for selected level

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Effect of cutting speed, feed, DOC on S/N ratio:

Effect of cutting speed, feed, DOC on Surface Roughness Ra:

From the main effects plots, roughness value is very low for the combination [speed.sub.2], [feed.sub.2], [DOC.sub.1]. S/N values are also high for this combination. Here if we select [speed.sub.3] instead of [speed.sub.2] the roughness value increase by 0.33 [micro]m

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Interaction plot for S/N ratio:

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Interaction plot for Average Roughness:

From the speed-feed interactions plots the roughness values are very low and S/N values are high for the combination [speed.sub.3], [feed.sub.2]. Even though from the main effects speed plot the level selected is [speed.sub.2], level [speed.sub.3] is selected to minimize the interaction effect. If [speed.sub.3] is selected instead of [speed.sub.2] the roughness value decrease by 0.48 [micro]m.from the DOC-Feed interaction plots the selected combination is [feed.sub.2], [DOC.sub.1] which agrees with main effects plots

Analysis of Variance:

The percentage contribution of each factor for surface roughness can be calculated by the ANOVA technique. Three-Way ANOVA is performed for the average surface roughness values and the contribution for each cutting parameter can be seen in the table

SS-sum of squres; DOF- degrees of freedom; v-variance; Fcal- Variance ratio P-Percentage of c ombinatiion

From table it can been seen that speed and DOC Fails to pass the F-test at 90% confidence level. So the significant parameter affecting surface roughess is feed rate which contributes upto 72.53%. A Pie-Chart is constructed which shows the percentage contribution of each factor the Pie-chart is shown below

[FIGURE 8.5 OMITTED]

Confirmation experiment results for Surface Roughness Trail Cutting Feed DOC Surface no speed rate (mm) Roughness (m/min) (mm/rev) in Ra ([micro]m) 1 150 0.05 0.10 0.853 2 150 0.05 0.10 0.897 3 150 0.05 0.10 0.818

It is evident from the above Table that the surface roughness values resulted by setting the optimized value of process parameters lie between the estimated limits.

Regression Model:

Regression analysis is a part of statistics that deals with the investigation of the relationship between two or more variables. Simple linear regression examines the linear relationship between two continuous variables: one response (y) and one predictor (x).When the two variables are related, it is possible to predict a response value from a predictor value with better than chance accuracy. Regression provides the line that 'best' fits the data. This line can then be used for two reasons one is to examine how the response variable changes as the predictor variable changes and the second is to predict the value of a response variable (y) for any predictor variable (x). In statistics, regression analysis examines the relation of a dependent variable (response variable) to specified independent variables (predictors). The mathematical model of their relationship is called as regression equation. The dependent variable is modeled as a random variable because of uncertainty as to its value, given values of the independent variables. Regression analysis estimates relationships between one or more response variables (also called dependent variables, explanatory variables, control variables).

Multiple linear regression analysis is done using MINITAB software which predicts surface roughness as a function of the input cutting speed, feed and depth of cut. Multiple linear regression analysis is done using MINITAB software which predicts surface roughness as a function of the input cutting speed, feed and depth of cut.

The regression equation is Surface roughness Ra (m) = -0.80 + 26.5 FEED + 8.21 DOC - 0.0005 SPEED R2 = 0.627

Conclusions:

Surface roughness is optimized using Taguchi technique and the optimum cutting levels found from this experimental study are Cutting speed = 150 m/min, Feed rate= 0.05 mm/rev, Depth of cut = 0.1 mm. Using ANOVA technique the parameters affecting the surface roughness are found to be feed (72.53%) followed by depth of cut (17.08%).

REFERENCES

[1.] Zhang Xueping, Gao Erwei, C.Richard Liu, 2008." Optimization of Process Parameter of Residual Stresses for Hard Turned Surfaces" Journal of Materials Processing Technology.

[2.] Jenn-Tsong Horng, Nun-Ming Liu, Ko-Ta Chiang, 2008." Investigating the mach inability evaluation of Hadfield steel in the hard turning with Al2O3/TiC mixed ceramic tool based on the response surface methodology", journal of materials processing technology.

[3.] Ataollah Javidi, Ulfried Rieger, Wilfried Eichlseder, 2008 "The effect of machining on the surface integrity and fatigue life", International Journal of Fatigue.

[4.] Lalwani, D.I., N.K. Mehta, P.K. Jain, 2008. "Experimental investigations of cutting parameters influence on cutting forces and surface roughness in finish hard turning of MDN250 steel", journal of materials processing technology.

[5.] Sahin, Y., 2008. "Comparison of tool life between ceramic and cubic boron nitride (CBN) cutting tools when machining hardened steels", journal of materials processing technology.

[6.] Waikar, R.A., Y.B. Guo, 2008. "A comprehensive characterization of 3D surface topography induced by hard turning versus grinding", journal of materials processing technology.

[7.] Pawadea, R.S., Suhas S. Joshia, P.K. Brahmankar, 2008. "Effect of machining parameters and cutting edge geometry on surface integrity of high-speed turned Inconel 718", International Journal of Machine Tools & Manufacture.

(1) T. Ramakrishnan, (2) Mr. K. Sathish, (3) Dr. P. S. Sampath, (4) S. Anandkumar

(1) Assistant Professor, Sri Eshwar College of Engineering, Coimbatore, Tamilnadu, India.

(2) Assistant Professor, Sri Eshwar College of Engineering, Coimbatore, Tamilnadu, India.

(3) Professor, KSR College of Technology, Tiruchengode, Tamilnadu, India.

(4) Development Engineer, Mira Alloy Steels Pvt Ltd, Coimbatore, Tamilnadu, India.

Received 25 January 2016; Accepted 28 April 2016; Available 5 May 2016

Address For Correspondence:

T. Ramakrishnan, Assistant Professor, Sri Eshwar College of Engineering, Coimbatore, Tamilnadu, India.

E-mail: ramakrishnankct@gmail.com

Table 1: Selection of machining factors Factors/parameters name Level 1 Level 2 Level 3 A Cutting speed (m/min) 225 330 350 B Feed (mm/rev) 0.05 0.075 1 C Depth of cut (mm) 1 2 3 Table 2: Orthogonal Array for L9 S.NO. Cutting Feed Depth Speed (mm/rev) of cut (m/min) (mm) 1. 1 1 1 2. 1 2 2 3. 1 3 3 4. 2 1 2 5. 2 2 3 6. 2 3 1 7. 3 1 3 8. 3 2 1 9. 3 3 2 Table 3: Compositions Material Composition (%) Carbon (C) 0.90-1.20 Manganese (Mn) 0.30-0.75 Phosphorous(P) 0.005 -0 .01 Silicon (Si) 0.1-0.35 Sulfur (S) 0.05 Chromium(Cr) 1-1.6 Table 4: Machine Tool Specification Make HMT Model ECONO 26 Distance between centers 1000 Feed Rate 1-7000 m/min Spindle speed 0-2500 rpm Rapid Feed Rate 5 m/min Maximum Feed Rate 1-2000 mm/min Tool Shank Size 25 X 25 Spindle Drive Motor 11 KW Table 5: Selected levels of cutting parameters Symbol Cutting parameters Levels 1 2 3 A Cutting speed(mm/min) 225 330 350 B Feed(mm/rev) 0.05 0.075 0.01 C Depth of cut(mm) 0.1 0.15 0.20 Table 7: surface roughness value for the machined surfaces using Surfcorder Combination TRAIL1 TRAIL2 TRAIL3 Mean ([micro]m) ([micro]m) ([micro]m) Roughness ([micro]m) 111 1.141 1.04 1.059 1.08 122 1.346 1.152 1.146 1.215 133 2.866 2,907 2.944 2.906 212 1.198 1.253 1.284 1.245 223 1.235 1.285 1.316 1.279 231 1.658 1.518 1.63 1.602 313 1.248 1.2771 1.286 1.268 321 .844 .775 .779 .799 332 3.015 3.043 3.102 3.053 Table 8.1: Three way ANOVA Table for Average Surface Roughness Source SS DOF V [F.sub.CAL] [F.sub.tab] P (%) (90%) Speed .238 2 .119 .789 9 4.58 Feed 3.78 2 1.892 12.507 9 72.53 DOC .891 2 .445 2.947 9 17.08 Error .302 2 .152 5.79 Total 5.217 8 .752 100

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Author: | Ramakrishnan, T.; Sathish, K.; Sampath, P.S.; Anandkumar, S. |
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Publication: | Advances in Natural and Applied Sciences |

Date: | May 15, 2016 |

Words: | 2752 |

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