Professor of Mechanical Engineering and Smart Structures, School of Computing Engineering and Mathematics, Western Sydney University, Australia. His research interests cover Industry 4.0, Additive Manufacturing, Advanced Engineering Materials and Structures (Metals and Composites), Multi-scale Modelling of Materials and Structures, Metal Forming and Metal Surface Treatment.
Abstract— In machining operation, the quality of surface finish is an important concern for many finished work products. Thus, the choice of optimized cutting parameters like speed, feed and depth of cut is very important for controlling the required surface quality. The focus of present experimental study is to optimize the cutting parameters using the surface roughness as performance measure. The experimental result shows that the work piece surface roughness can be used effectively as a beacon to control the cutting performance. The modeling of the experimentally obtained data is being done using the regression analysis. Optimal cutting parameter for performance measure is obtained employing Particle Swarm Optimization (PSO) in order to get the minimum surface roughness and compared with the experimentally obtained data. The adequacy of the models of surface roughness has been established to achieve minimum surface roughness.
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