Multi-objective decision-making in single-pass turning using response surface methodology

The present paper is aimed at fulfilment of two basic but conflicting objectives concurrently during turning – higher material removal rate (MRR) and lower surface roughness (Ra) by employing a single set of optimal or near optimal process variables namely cutting speed, feed and depth of cut following response surface methodology (RSM). The experimental data is obtained from a test while turning mild steel round by coated cemented carbide insert in a CNC turning centre. Regression equations are developed for the responses and analysed by ANOVA to establish the significance of the models. Response surfaces and contour plots are studied to investigate the prominence of the variables and their levels so as to optimise the responses. Finally multi-response optimisation is carried out using overlaid contour plots and desirability functions. It is recommended that moderate value of feed along with high levels of speed and depth of cut optimise both the responses simultaneously.

[1]  A.N.M. Karim,et al.  Development of surface roughness prediction model using response surface methodology in high speed end milling of AISI H13 tool steel , 2007, 2007 IEEE International Conference on Industrial Engineering and Engineering Management.

[2]  M. RahmanM.,et al.  Optimization of Surface Roughness in End Milling on Mould Aluminium Alloys (AA6061-T6) Using Response Surface Method and Radian Basis Function Network , 2008 .

[3]  S. Oraby,et al.  Surface topography assessment techniques based on an in-process monitoring approach of tool wear and cutting force signature , 2008 .

[4]  K. Abou-El-Hossein,et al.  Torque Prediction Model for Milling 618 Stainless Steel Using Response Surface Methodology , 2006 .

[5]  Zlatan Car,et al.  GA BASED CNC TURNING CENTER EXPLOITATION PROCESS PARAMETERS OPTIMIZATION , 2009 .

[6]  Tuğrul Özel,et al.  Multi-objective optimization for turning processes using neural network modeling and dynamic-neighborhood particle swarm optimization , 2007 .

[7]  Mahmudur Rahman,et al.  Determination of optimal cutting conditions using design of experiments and optimization techniques , 1993 .

[8]  Indrajit Mukherjee,et al.  A review of optimization techniques in metal cutting processes , 2006, Comput. Ind. Eng..

[9]  T. Srikanth.  A Real Coded Genetic Algorithm for Optimization of Cutting Parameters in Turning , 2008 .

[10]  Prasanta Sahoo,et al.  Roughness modeling and optimization in CNC end milling using response surface method: effect of workpiece material variation , 2009 .

[11]  Karen A. F. Copeland.  Design and Analysis of Experiments, 5th Ed. , 2001 .

[12]  Min-Chie Chiu,et al.  Dynamic Material Removal Rate and Tool Replacement Optimization with Calculus of Variations , 2008 .

[13]  A. K. M. Nurul Amin,et al.  TOOL LIFE PREDICTION BY RESPONSE SURFACE METHODOLOGY FOR END MILLING TITANIUM ALLOY Ti-6Al-4V USING UNCOATED CARBIDE INSERTS , 2009 .