Assessment of stable cutting zone in CNC turning based on empirical mode decomposition and genetic algorithm approach

摘要

Assessment of optimum stable cutting zone is the key requirement to maintain high productivity with enhanced surface quality of work-piece. Tool chatter is one of the factors responsible for deviation from these features. Despite the immense work done within this domain, still many aspects related to regenerative chatter remains unexplored. Usually, the chatter signals recorded from sensors are contaminated by background noise. Analysis of these contaminated signals results in faulty information regarding the identification of tool chatter. So, it becomes imperative that these signals should be denoised before further processing. In the present work, empirical mode decomposition technique has been adopted to pre-process the acquired raw chatter signals, which have been overlooked by the previous researchers. Initially, acoustic signals have been recorded by performing experiments at different combinations of cutting parameters. The preprocessed signals have been used to evaluate a new output parameter i.e. chatter index. Material removal rate has also been measured for each experiment. For estimating the dependence of output on input cutting parameters, mathematical models have been developed using response surface methodology. Moreover, the optimum cutting zone has been assessed by adopting multi-objective genetic algorithm. Finally, more experiments have been conducted to validate the obtained cutting zone. It has been found that the acquired cutting zone is capable of producing work pieces with good surface finish and acceptable material removal rate.