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—This paper proposes an optimization method of turning process parameters based on nondominated sorting genetic algorithm II (NSGA-II) and artificial neural networks (ANNs). In the period of computer-aided process planning (CAPP), each machining operation with its process parameters should be given in sequence for the final output of workshop documentation and machining program. NSGA-II algorithm is used in the optimization of process parameters, including spindle speed, feed rate, depth of cut, etc. ANNs are used to predict the performance parameters, including cut force and surface roughness as constraints or objective of the optimization process. This process is a self-learning process because in the period of machining, data and signal from CNC is collected as training sample to iterate the artificial neural networks.
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