Open Access Open Access  Restricted Access Subscription or Fee Access

Optimizing Milling Parameters in Face Milling Operation

Vipul Verma, Peeyush Kr. Gupta, Ayush Goel, Saurabh Awasthi


In statistics, response surface methodology (RSM) establish a relationship between several self or system explanatory variables and one or more response variables. Since many variables are involved to control the process, the requirement of some mathematical models to represent the process is found to be necessary. In order to extract the result by this statistical analysis, the experimental results will have to be processed using the analysis of variance (ANOVA). ANOVA is a statistical computational tool that enables the estimation of the sometimes absolute and mainly relative contributions of each control factors to the overall measured response. In this current work, only the selectively signify parameters are used to develop mathematical models while applying response surface methodology (RSM). Multiple regression method is used in experimental results to build first-order and second-order models. The experimental investigation established that the MRR can be optimized by the proper settings of machining parameters. High MRR can also result in better machinability and production rate.


ANOVA; MRR; multiple regression method; RSM

Full Text:



Yazdi MRS, Khorram A. Modeling and Optimization of Milling Process by using RSM and ANN Methods. International Journal of Engineering and Technology. 2010; 2 (5): 474-480p.

Joshi A, Kothiyal P, Pant R. Experimental Investigation of Machining Parameters of CNC Milling On MRR By Taguchi Method. International Journal of Applied Engineering Research. 2012; 7 (11): 1796-1800p.

Shah IB, Gawande KR. Optimization of Cutting Tool Life on CNC Milling Machine Through Design Of Experimnets. International Journal of Engineering and Advanced Technology. 2012; 1 (4).

Mukherjee I, Ray PK. A review of optimization techniques in metal cutting processes. Computers & Industrial Engineering. 2006; 50: 15–34p.

Shunmugam MS, Reddy SVB, Narendran TT. Selection of optimal conditions in multi-pass face-milling using a genetic algorithm. International Journal of Machine Tools & Manufacture. 2000; 40: 401–414p.

Baek DK, Ko TJ, Kim HS. Optimization of federate in a face milling operation using a surface roughness model. International Journal of Machine Tools & Manufacture. 2001; 41: 451–462p.

Benardos PG, Vosniakos GC. Prediction of surface roughness in CNC face milling using neural networks and Taguchi’s design of experiments. Robotics and Computer Integrated Manufacturing. 2002; 18: 343–354p.

Kwak JS. Application of Taguchi and response surface methodologies for geometric error in surface grinding process. International Journal of Machine Tools & Manufacture. 2005; 45: 327–334p.

Korkut I, Donertas MA. The influence of feed rate and cutting speed on the cutting forces, surface roughness and tool–chip contact length during face milling. Materials and Design. 2007; 28: 308–312p.

Azadi M, Azadi S, Moradi M, Zahedi F. Multidisciplinary optimization of a car component under NVH and weight constraints using RSM. International Journal of Vehicle Noise and Vibration. 2009; 5 (4).

Khuri AI, Cornell JA. Response Surfaces Design and Analysis 2nd ed. New York: Marcel Dekker; 1996.

Myers RH, Monotgormery DC. Response Surface Methodology. J. Wiley & Sons: Interscience Publication; 2002.

Benardos PG, Vosniakos GC. Predicting surface roughness I machining: a review. International Journal of Machine Tools & Manufacture. 2003; 43: 833–844p.



  • There are currently no refbacks.

Copyright (c) 2018 Journal of Experimental & Applied Mechanics