TURNING PARAMETER OPTIMIZATION FOR SURFACE ROUGHNESS OF ASTM A242 TYPE-1 ALLOYS STEEL BY TAGUCHI METHOD

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1 TURNING PARAMETER OPTIMIZATION FOR SURFACE ROUGHNESS OF ASTM A242 TYPE-1 ALLOYS STEEL BY TAGUCHI METHOD Jitendra Verma 1, Pankaj Agrawal 2, Lokesh Bajpai 3 1 Department of Mechanical Engineering, Samrat Ashok Technological Institute, Vidisha (M.P) , India. 2&3 Professor in Department of Mechanical Engineering, Samrat Ashok Technological Institute, Vidisha (M.P.) , India. ABSTRACT The purpose of this research paper is focused on the analysis of optimum cutting conditions to get lowest surface roughness in turning ASTM A242 Type-1 ALLOYS STEEL by Taguchi method. Experiment was designed using Taguchi method and 9 experiments were conducted by this process. The results are analyzed using analysis of variance (ANOVA) method. Taguchi method has shown that the cutting speed has significant role to play in producing lower surface roughness about 57.47% followed by feed rate about 16.27%. The Depth of Cut has lesser role on surface roughness from the tests. The results obtained by this method will be useful to other researches for similar type of study and may be eye opening for further research on tool vibrations, cutting forces etc. KEYWORDS: ASTM A242 Type-1 alloy steel; Machining; Dry turning; Signal-to- noise ratio; Taguchi method I. INTRODUCTION Increasing the productivity and the quality of the machined parts are the main challenges of metalbased industry; there has been increased interest in monitoring all aspects of the machining process. Surface finish is an important parameter in manufacturing engineering. It is a characteristic that could influence the performance of mechanical parts and the production costs. The ratio between costs and quality of products in each production stage has to be monitored and immediate corrective actions have to be taken in case of Deviation from desired trend. Surface roughness measurement presents an important task in many engineering applications. Many life attributes can be also determined by how well the surface finish is maintained. Machining operations have been the core of the manufacturing industry since the industrial revolution [1] and the existing optimization researches for Computer Numerical Controlled (CNC) turning were either simulated within particular manufacturing circumstances or achieved through numerous frequent equipment operations. These conditions or manufacturing circumstances are regarded as computing simulations and their applicability to real world industry is still uncertain and therefore, a general optimization scheme without equipment operations is deemed to be necessarily developed. Surface roughness is commonly considered as a major manufacturing goal for turning operations in many of the existing researches. The machining process on a CNC lathe is programmed [13]. Many surface roughness prediction systems were designed using a variety of sensors including dynamometers for force and torque. Taguchi and Analysis Of Variance (ANOVA) can conveniently optimize the cutting parameters with several experimental runs well designed [15]. Taguchi parameter design can optimize the performance characteristics through the settings of design parameters. This study describe how to select the control factors levels (cutting speed, feed rate, Depth of cut) that can minimize the effect of noise factors on the response (surface roughness). An experimental work will be conducted to analyse the influence of 255 Vol. 3, Issue 1, pp

2 cutting parameters (control factors) on surface roughness (signal factor), then select the optimal cutting parameters which lead to optimal response by assistance of optimal signal factor [18]. Sundaram and Lambert [20, 21] considered six variables i.e. speed, feed, and depth of cut, time of cut, nose radius and type of tool to monitor surface roughness. To improve the efficiency of these turning processes, it is necessary to have a complete process understanding and model. To this end, a great deal of research has been performed in order to quantify the effect of various hard turning process parameters to surface quality. These factors can be divided into a) setup variables, b) tool variables, and c) work piece variables. In order to gain a greater understanding of the turning process it is necessary to understand the impact of the each of the variables, but also the interactions between them. It is impossible to find all the variables that impact surface roughness in turning operations. In addition, it is costly and time consuming to discern the effect of the every variable on the out put. In order to simplify the problem, one needs to eliminate or select specific variables that correspond to practical applications. II. TAGUCHI METHOD Taguchi method is a powerful tool for the design of high quality systems. It provides simple, efficient and systematic approach to optimize designs for performance, quality and cost [22]. Taguchi method is efficient method for designing process that operates consistently and optimally over a variety of conditions. To determine the best design it requires the use of a strategically designed experiment [23]. Taguchi approach to design of experiments in easy to adopt and apply for users with limited knowledge of statistics, hence gained wide popularity in the engineering and scientific community [17-18]. The desired cutting parameters are determined based on experience or by hand book. Cutting parameters are reflected. Steps of Taguchi method are as follows: (1) Identification of main function, side effects and failure mode. (2) Identification of noise factor, testing condition and quality characteristics. (3) Identification of the main function to be optimized. (4) Identification the control factor and their levels. (5) Selection of orthogonal array and matrix experiment. (6) Conducting the matrix experiment. (7) Analysing the data, prediction of the optimum level and performance. (8) Performing the verification experiment and planning the future action. [4] III. EXPERIMENTAL SET UP AND CUTTING CONDITIONS MATERIALS AND METHODS Experimental procedures and conditions: In this study ASTM A242 TYPE-1 ALLOY steel and 250 mm long with 50 mm diameter was used as work material for experimentation using a lathe turning machine. The chemical composition of the selected work piece is shown as Table 1 Composition OF ASTM A242 Type-1 ALLOYS STEEL C Mn Si P Cr Cu S P Nb+V+Ti 0.15% 1.0% 0.4% 0.15% 0.5% 0.2% 0.05% 0.045% 0.15 Universal turning machine tool was used in the experiments.. Cutting speed, feed rate and depth of cut were selected as the machining parameters to analyse their effect on surface roughness. A total of 9 experiments based on Taguchi s orthogonal array were carried out with different combinations of the levels of the input parameters. Among them, the settings of cutting speed include 100, 125 and 150 rpm; those of feed rate include 0.05, 0.1, 0.15 mm rev-1; the depth of cut is set at 0.5, 1.0 and 1.5 mm. The cutting parameters are shown in the Table 2. Three levels of cutting speed, three levels of feed rate and three levels of depth of cut were used and are shown in the Table 2. The different alloying elements present in a work piece are shown in the table Vol. 3, Issue 1, pp

3 IV. Table 2 Cutting parameters Level Level Symbol Parameters/Level Level 1 Units 2 3 A Cutting speed m/min B Feed rate mm/rev. C Depth of cut mm RESULTS AND DISCUSSION 4.1 Analysis of S/N ratio based on Taguchi Method To select an appropriate orthogonal array for experiments, the total degrees of freedom need to be computed. The degrees of freedom are defined as the number of comparisons between process parameters that need to be made to determine which level is better and specifically how much better it is. For example, a Three-level process parameter counts for four degrees of freedom. The degrees of freedom associated with interaction between two process parameters are given by the product of the degrees of freedom for the two process parameters [8]. The mean S/N ratio for each level of the other cutting parameters can be computed in the similar manner. The mean S/N ratio for each level of the cutting parameters is summarized and called the S/N response table for surface roughness. In addition, the total mean S/N ratio for the nine experiments for surface roughness, listed in Table.3 the greater S/N ratio corresponds to the smaller variance of the output characteristic around the desired value. Test. No Cutting Speed Feed rate DOC Table 3 Experimental Results Mean Surface Roughness Ra (µm) Signal to Noise Ratio (S/N) According to Taguchi method, S/N ratio is the ratio of Signal representing desirable value, i.e. mean of output characteristics and the noise representing the undesirable value i.e., squared deviation of the output characteristics. It is denoted by η and the unit is db. The S/N ratio is used to measure quality characteristic and it is also used to measure significant welding parameters [9]. Table No. 4 Response table Parameters/Level Level 1 Level 2 Level 3 Max-Min Rank Cutting speed Feed rate Depth of cut Vol. 3, Issue 1, pp

4 S/N RESPO NSE Level 1 Level 2 Level Cutting speed Average Cutting speed Fig. 1 CUTTING SPEED VS S/N RESPONSE S/N RESPONSE Level 1 Level 2 Level Feed Rate Average Feed Rate S/N RESPONSE Fig. 2 FEED RATE VS S/N RESPONSE Level 1 Level 2 Level Depth of cut Average Depth of cut Fig. 3 DEPTH OF CUT VS S/N RATIO 4.2 Analysis of variance (ANOVA) The main aim of ANOVA is to investigate the design parameters and to indicate which parameters are significantly affecting the output parameters. In the analysis, the sum of squares and variance are calculated. F-test value at 95 % confidence level is used to decide the significant factors affecting the process and percentage contribution is calculated [19]. The ANOVA analysis for percentage calibration is shown in Table Vol. 3, Issue 1, pp

5 Table 5 Result of ANOVA for Surface Roughness contribution Symbol Parameter DOF Sum of Square Mean Square Percentage Contribution A Cutting Speed % B Feed Rate % C Depth of Cut % Error % Total V. CONCLUSION Fig. 4 Percentage contribution by Pie Chart for surface roughness The following are conclusions drawn based on the tests conducted on turning ASTM A242 Type-1 ALLOYS STEEL and 250 mm long with 50 mm diameter. 1. From the ANOVA, Table 5 and the P value, the cutting speed is the only significant factor which contributes to the surface roughness i.e % contributed by the cutting speed on surface roughness. 2. The second factor which contributes to surface roughness is the feed rate having %. 3. The third factor which contributes to surface roughness is the depth of cut having 16.27%. 4. The Validation experiment confirms that the error occurred was less than 2.79% between equation and actual value. 5. It is recommended from the above results that cutting of to m/min can be used to get lowest surface roughness. 6. Taguchi gives systematic simple approach and efficient method for the optimum operating conditions. So now it is found by this research how to use Taguchi s parameter design to obtain optimum condition with lowest cost, minimum number of experiments and industrial engineers can use this method. REFERENCES [1] Elias N. Malamas, Euripides G.M. Petrakis, Michalis Zervakis A SURVEY ON INDUSTRIAL VISION SYSTEMS, APPLICATIONS AND TOOLS Department of Electronic and Computer Engineering Technical University of Crete Chania Crete Greece [2] J. Z. Zhang, J. C. Chen, E. D. Kirby, Surface roughness optimization in an end-milling operation using the Taguchi design method, Journal of Materials Processing Technology 184, pp , [3] P. J. Ross, Taguchi technique for quality engineering, New York: McGraw-Hill, [4] Onkar N. panday total quality management 259 Vol. 3, Issue 1, pp

6 [5] N. Nalbant, H. Gokkaya, G. Sur, Application of Taguchi method in the optimization of cutting parameters for surface roughness in turning, Materials and Design, date received and date accepted [6] I.A. Choudhury, M.A. El-Baradie, Surface roughness prediction in the turning of high strength steel by factorial design of experiments, Journal of Materials Processing technology, 67 (1997) [7] B. Erginc, Z. Kampu, B. Sustarsic, The use of the Taguchi approach ro determine the influence of injection molding parameters on the properties of green parts, Journal of achievement in Materials and Manufacturing Engineering, 15, [8] P.J. Ross Taguchi Techniques for Quality Engineering. 2nd Ed. Tata McGraw Hill. [9] Ugur Esme Application of Taguchi method for the optimization of resistance spot welding process. The Arabian Journal for Science and Engineering. 34(28): [10] A. G. Thakur, T. E. Rao, M. S. Mukhedkar and V. M. Nandedkar APPLICATION OF TAGUCHI METHOD FOR RESISTANCE SPOT WELDING OF GALVANIZED STEE ARPN Journal of engineering and Applied Sciences Asian Research Publishing Network (ARPN). All rights reserved [11] S. Thamizhmanii, S. Saparudin and S. Hasan, Analyses of roughness, forces and wear in turning gray cast iron, Journal of achievement in Materials and Manufacturing Engineering, volume20, issues 1-2, [12] Palanikumar, L. Karunamoorthy, R. Krathikeyan, Assessment of factors influencing surface roughness on the machining of glass reinforced polymer composites, Journal of Materials and Design, 27 (2006) [13] Xue Ping, C. Richard Liu, Zhenqiang Yao, Experimental study and evaluation methodology on hard surface integrity, International Journal Advanced Manufacturing Technology, ODI /s [14] T. Tamizharasan, T. Selvaraj, A. Noorul Hag, Analysis of tool wear and surface finish in hard turning, International Journal of Advanced Manufacturing Technology (2005), DOI 10/1007 /s [15] W.H. Yang, Y.S. Tang, Design optimization of cutting parameters for turning operations based on Taguchi method, Journal of Materials Processing Technology, 84 (1998) [16] Ersan Aslan, Necip Camuscu Burak Bingoren, Design of optimization of cutting parameters when turning hardened AISI 4140 steel (63 HRC) with Al 2 O3 + TiCN mixed ceramic tool, Materials and Design, date received and date accepted [17] D.C. Montgometry, Design and analysis of experiments, 4 th edition, New York: Wiley; [18] N. Nalbant, H. Gokkaya, G. Sur, Application of Taguchi method in the optimization of cutting parameters for surface roughness in turning, Materials and Design, date received [19] S. Thamizhmanii*, S. Saparudin, S. Hasan Analyses of surface roughness by turning process using Taguchi method ; Journals of Achievements in Materialsand Manufacturing Engineering VOLUME 20ISSUES 1-2January-February2007 [20] R.M. Sundaram, B.K. Lambert, Mathematical models to predict surface finish in fine turning of steel, Part I, International Journal of Production Research 19 (1981) [21] R.M. Sundaram, B.K. Lambert, Mathematical models to predict surface finish in fine turning of steel, Part II, International Journal of Production Research 19 (1981) [22] W.H. Yang, Y.S. Tang, Design optimization of cutting parameters for turning operations based on Taguchi method, Journal of Materials Processing Technology, 84 (1998) [23] Ersan Aslan, Necip Camuscu Burak Bingoren, Design of optimization of cutting parameters when turning hardened AISI 4140 steel (63 HRC) with Al 2 O3 + TiCN mixed ceramic tool, Materials and Design, date received and date accepted ABOUT THE AUTHOR Jitendra Verma was born in 15 th Sep He received his B.Tech in Manufacturing Technology from JSS Academy of Technical Education, Noida (U.P.) in Currently, He is pursuing his M.Tech (C.I.M.) from Samrat Ashok Technological Institute, Vidisha (M.P.). His research interests are surface roughness, welding, Turning and machining. Pankaj Agrawal was born in 18 th August He is currently working as a Professor in Mechanical Engineering Department of Samrat Ashok Technological Institute, Vidisha (M.P.). He has more then 18 years experience in teaching, one year industry and 10 years of research experience. He has done his B.E. in Mechanical Engineering from Samrat Ashok Technological Institute, Vidisha (M.P.) in He has done M.Tech and then Ph.D. in 2003 from BARKATULLAH UNIVERSITY BHOPAL in He has published many papers in 260 Vol. 3, Issue 1, pp

7 various journals and conferences of international repute. His main interests are hybrid manufacturing, stereo lithography, Supply Chain Management and Flexible Manufacturing Systems etc. Lokesh Bajpai was born in 19 th December 1960.He is currently working as Professor in Mechanical Engineering Department of Samrat Ashok Technological Institute, Vidisha (M.P.). He has done B.E. from GEC Jablpur (M.P.) in 1984.He has done M.E., Ph.D. F.I.E. (India), MISME, M.I.S.C.A. He has more then 24 years experience in teaching and 14 years of research experience. He has published many papers in various journals and conferences of international repute. His main interests are computer integrated manufacturing, Flexible Manufacturing, computer aided process planning etc. 261 Vol. 3, Issue 1, pp