The customers' most specified requirements are surface roughness which is the indicator of surface quality and get the product at minimum cost. In manufactures point of view, maximization of metal removal rate is high priority in order to reduce the manufacturing cost. Hence, it is important to explore the influence of cutting parameters on surface roughness (Ra) and Metal Removal Rate (MRR). In the present paper, AISI 1042 is considered as workpiece material as it has wide applications in manufacturing of Dies, gears, shafts, machine tool parts, etc. Selected tools are PVD (PR1125) and CVD (CR5515), consisting of the coating surfaces of TiA1N and TiCN+Al O +TiN, respectively. The experimentation 2 3 for this work was based on Taguchi's design of experiments (DOE) and orthogonal array. In this work, three cutting parameters, namely, cutting speed, depth of cut, and feed rate were considered as process parameters and responses are Material removal rate and Surface roughness. Experiments were conducted by using Taguchi Design of experiments of L9 orthogonal array for both CVD and PVD tools. The main objective of this paper is to determine the optimum cutting parameters and the tool used in turning AISI 1042 material with multiple output responses using ANOVA and Grey Relational analysis.

Turning, Surface Roughness (Ra), Material Removal Rate (MRR), ANOVA, Grey Relational Analysis (GRA).
How to Cite this Article?
Kumar, A. H., Rao, G. S., and Rajmohan, T. (2017). Optimization of Process Parameters on Surface Roughness & Metal Removal Rate on AISI 1042 with Coated Tools by using Anova and Grey Relational Analysis. i-manager’s Journal on Mechanical Engineering, 7(1), 8-15.
[1]. Konig W., Komanduri, R, Tonshoff. H.K., and Ackershon G, (1984). “Machining of hard materials”. Annals of CIRP, Vol.33, No.2, pp.417-427.
[2]. Tonshoff H.K, C. Arendt and Ben Amor R, (2000). “Cutting of hardened steel”. Annals of CIRP, Vol.49, No.2, pp.547-566.
[3]. Sheehy. T., (1997). “Taking hard out of hard turning manufacturing”. Manufacturing Engineering, Vol.118, pp.100-106.
[4]. Tonshoff H.K, and Hetz F., (1986). “Surface integrity of Difficult to machine Materials”. 2nd IMEC Session II, pp.120-136.
[5]. Bossom, P.K, (1990). “Finish machining of hard ferrous workpieces”. Industrial Diamond Review, Vol.5, pp.228-233
[6]. Chryssolouris G, (1982). “Effects of machine tool workpiece stiffness on the wear behavior of super hard cutting materials”. Annals of CIRP, Vol.31, No.1, pp.65-69.
[7]. Devitt A.J. (1998). “Sliding way design Primer”. Manufacturing Engineering SME, pp. 68-74.
[8]. F. Jafarian, M. Taghipour, and H. Amirabadi, (2013). “Application of Artificial Neuural Network and Optimization Algorithms for Optimizing Surface roughness, tool life, and cutting forces in turning operation”. Journal of Mechanical Science and Technology, Vol.27, No.5, pp.1469-1477.
[9]. Davim J.P., (2001). “A note on the determination of optimal cutting conditions for surface finish obtained in turning using design of experiments”. J. Mater. Process. Technology, Vol.116, pp.305–308.
[10]. J. T. Black, and B. J. Schroer (1988). “Decouplers in Integrated Cellular Manufacturing Systems”. Journal of Engineering for Industry, Vol.110, No.1, pp.9.
[11]. Tosun, Nihat. (2006). “Determination of optimum parameters for multi – performance characteristics in drilling by using Grey Relational Analysis”. International Journal of Advanced Manufacturing Technology, Vol.28, No.5, pp.450-455.
[12]. Abhang, LB, and Hameedullah, M. (2011). “Determination of optimum parameters for multiperformance characteristics in turning by using Grey Relational Analysis”. International Journal of Advanced Manufacturing Technology. Vol.63, No.1, pp.13-24.
[13]. Ali, S. M., and Dhar, N. R. (2010). Tool wear and surface roughness prediction using an Artificial Neural Network (ANN) in turning steel under Minimum Quantity Lubrication (MQL) . World Academy of Science, Engineering and Technology, Vol.62, pp.830-839.
[14]. Natarajan, C., Muthu, S., and Karuppuswamy, P. (2011). ”Prediction and analysis of surface roughness characteristics of a non-ferrous material using ANN in CNC turning . The International Journal of Advanced Manufacturing Technology, Vol.57, No.9-12, pp.1043-1051.
[15]. Kolahan, F., and Khajavi, A. (2010). A statistical approach for predicting and optimizing depth of cut in AWJ machining for 6063-T6 Al alloy . International Journal of Mechanical Systems Science and Engineering, Vol.2, No.2, pp.143-146.
Username / Email
Don't have an account?  Sign Up
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.

Purchase Instant Access





We strive to bring you the best. Your feedback is of great value to us. Feel free to post your comments and suggestions.