International Journal of Applied Science and Engineering
Published by Chaoyang University of Technology

K. Devaki Devia*, K. Satish Babub, and K. Hemachandra Reddyc

aDepartment of Mechanical Engineering , G Pulla Reddy Engineering College, India
bDepartment of Mechanical Engineering, Ravindra College of Engineering for Women, India
cRegistrar, JNTUA, Anatapuram, India


Download Citation: |
Download PDF


Quality plays a vital role since the extent of quality of the produced item influences the degree of satisfaction of the consumer during its usage. Apart from quality, productivity is the next criterion, which is directly related to the profit of the organization. The present paper invites optimization problem which seeks identification of the best process condition or parametric combination for the said manufacturing process. If the problem is related to a single quality attribute then it is called single objective optimization. If more than one attribute comes into consideration it is very difficult to select the optimal setting which can achieve all quality requirements simultaneously. In order to tackle such a Multi-Objective Optimization problem, the present study applied Response Surface Methodology through an experimental study in straight turning of brass bar. The study aimed at evaluating the best process environment which could simultaneously satisfy requirements of both quality and as well as productivity. Finally the effect of four input variables namely cutting speed, feed, depth of cut and type of coolant on different output parameters is studied in the study. The predicted optimal setting ensured minimization of surface roughness and maximization of MRR (Material Removal Rate), tool life and machinability index. Optimal result was satisfactorily verified through confirmatory test.

Keywords: Multi-Objective Optimization; response surface methodology; surface roughness; material removal rate; tool life; machinability index.

Share this article with your colleagues



  1. [1] Routara, B. C., Sahoo, A.K., and Parida, A.K., 2012. Response Surface Methodology and GeneticAlgorithm used to Optimize the Cutting condition for Surface Roughness Parameters in CNC Turning. Procedia Engineering, 38: 1893–1904.

  2. [2] Thatoi, D.N., Acharya, A.K., Routara, B.C., 2012. Application of Response surface method and Fuzzy logic approach to optimize the process parameters for surface roughness in CNC turning. Advances in Engineering, Science and Management (ICAESM), International Conference, 30-31 March 2012, 47 – 52, ISBN: 978-1-4673-0213-5, Nagapattinam, Tamil Nadu.

  3. [3] Sharma, K, Murtaza, Q., and Garg, S.K., 2010. Response Surface Methodology & Taguchi techquines to Optimization of C.N.C. Turning process. International Journal of Production Technology and Management, 1, 1: 13-31.

  4. [4] Sahoo, A.K., Sahoo, B., 2011 Surface Roughness Model and Parametric Optimization in Finish Turning using Coated Carbide Insert: Response Surface Methodology and Taguchi Approach, International Journal of Industrial Engineering Computations, 2, 4: 819-830.

  5. [5] Kandananond,, 2010. Using the Response Surface Method to Optimize the Turning Process of AISI 12L14 Steel. Advances in Mechanical Engineering, Sage Journals, 2: p-6.

  6. [6] Doniavi,, Eskanderzade, M. and Tahmsebian, M., (2007). Empirical Modeling of Surface Roughness in Turning Process of 1060 steel using Factorial Design Methodology. Journal of Applied Sciences, 7, 17: 2509- 2513. 

  7. [7] Natarajan,, Arun, P., Periasamy, V. M., (2007). On-line Tool Wear Monitoring in Turning by Hidden Markov Model (HMM). Institution of Engineers (India) Journal (PR), 87: 31-35.

  8. [8] Wang, M. Y. and Lan, T. S., (2008). Parametric Optimization on Multi-Objective Precision Turning Using Grey Relational Analysis. Information Technology Journal, 7, 1072-1076.

  9. [9] Srikanth, T, Kamala, V., 2008. A Real Coded Genetic Algorithm for Optimization of Cutting Parameters in Turning. IJCSNS Int. J. Comput. Sci. Netw. Secur, 8, 6:189-193.

  10. [10] Reddy, B. S., Padmanabhan, G. and Reddy, K. V. K., 2008. Surface Roughness Prediction Techniques for CNC turning. Asian Journal of Scientific Research, 1, 3: 256-264.

  11. [11] Thamma, R., 2008. Comparison between Multiple Regression Models to Study Effect of Turning Parameters on the Surface Roughness, Proceedings of the 2008 IAJC-IJME International Conference, 133, 103: 1-12.

  12. [12] Biswas, C. K., Chawla, B. S., Das, N. S., Srinivas, E. R. K. N. K., 2008. Tool Wear Prediction using Neuro-Fuzzy System. Institution of Engineers (India) Journal (PR), 89:  42-46. 

  13. [13] Aggarwal, A. & Singh, H., 2005. Optimization of machining techniques- A retrospective and literature review. Sadhana, 30: 699-711.

  14. [14] Alauddin, M., El Baradie, M.A., and Hasmi, M.S.J Optimization of Surface Finish In End Milling Inconel 718. Journal of Materials Processing Technology, 56: 54-65.

  15. [15] Wassila, B. 2005. Cutting parameter optimization to minimize production time in high speed turning. Journal of material processing technology, 161: 388-395.

  16. [16] Cemal, C.M. and Adem, G., 1998. Optimization and graphical representation of machining conditions in multi-pass turning operations. Journal of Computer Integrated Manufacturing Systems, 11, 3: 157-170.

  17. [17] Cheung, C.F. and Lee, W.B., 2000. A theoretical and experimental investigation of surface roughness formation in ultra-precision diamond turning. International Journal of Machine Tools & Manufacture, 40: 979–1002.

  18. [18] Choudhary, I.A., Kl-Baradie, M.A. 1998. Machinability assessment of inconel 718 by factorial design of experiment coupled with response surface methodology. Journal of  Materials Processing Technology, 95: 30-39.

  19. [19] Choudhury, S.K, Jain, V.K. and Rao, Ch.V.V. Rama 1999“On-line monitoring of tool wear in turning using a neural network” International Journal of Machine Tools & Manufacture, 39: 489–504.

  20. [20] Davim, J.P., Goitonde, V.N., and Karnik, S.R. 2006. An investigative study of de-lamination in drilling of medium density fiber board (MDF) using response surface models. International Journal Advance Manufacturing Technology,37, 1-2: 49-57.

  21. [21] Devim, J.P. 2003. Design of optimization of cutting parameters for turning metal matrix composites based on orthogonal arrays. Journal of Material Processing Technology, 132: 340-344.

  22. [22] Montgomerry, D.C. – “Design and Analysis of Experiments”, Wiley Publishers, 8th edition.


Received: 2014-01-03
Revised: 2014-04-09
Accepted: 2014-12-09
Available Online: 2015-03-01

Cite this article:

Devi, K.D., Babu, K.S., Reddy, K.H. 2015. Mathematical modeling and optimization of turning process parameters using response surface methodology. International Journal of Applied Science and Engineering, 13, 55–68.

We use cookies on this website to improve your user experience. By using this site you agree to its use of cookies.