Local mean decomposition and artificial neural network approach to mitigate tool chatter and improve material removal rate in turning operation

Productivity has always been a major concern in the industry. It can be improved by increasing material removal rate. Regenerative chatter during machining is the major obstacle to attain this. In the present work, a methodology has been proposed to select a proper combination of input cutting parameters for stable turning with improved metal removal rate (MRR). Chatter signals generated during the turning of Al 6061 have been acquired using a microphone. Initially, acquired signals have been processed using local mean decomposition (LMD) signal processing technique. The decomposed signals have been analyzed using different statistical chatter indicators considering Nakagami distribution approach for ascertaining the thresholds of chatter severity. Prediction models of most effective statistical chatter indicator and MRR have been developed using an artificial neural network (ANN). Moreover, this prediction models have been optimized using multi-objective genetic algorithm for ascertaining the optimal range of cutting parameters for stable turning with higher MRR. Finally, obtained stable range has been validated by performing more experiments. (C) 2020 Elsevier B.V. All rights reserved.