Ensembled local mean decomposition and genetic algorithm approach to investigate tool chatter features at higher metal removal rate

Improper selection of cutting parameters leads to regenerative chatter and loss in productivity. 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. Chatter signals generated during the turning of Al6061-T6 have been acquired using a microphone. Stability lobes diagram has been plotted to access the stability regime. Further, to study the effect of feed rate on stability, the recorded signals have been processed using local mean decomposition signal processing technique, followed by the selection of dominating product functions using Fourier transform. The decomposed signals have been used to evaluate the new output parameter, that is, chatter index. Prediction models of chatter index and metal removal rate have been developed. Moreover, these prediction models have been optimized using multi-objective genetic algorithm for ascertaining the optimal range of cutting parameters for stable turning with higher metal removal rate. Finally, obtained stable range has been validated by performing more experiments.

Use of artificial neural network and multi-objective genetic algorithm approach to predict and ascertain stable cutting zone in conventional turning process

This article is focused on the investigation of stable cutting zone in turning operation. Experiments have been conducted to acquire raw chatter signals. Generally, raw chatter signals are contaminated with ambient noise. Wavelet transform has been used for pre-processing and denoising these signals. In order to predict the severity of tool chatter, a new parameter denoted as chatter index has been evaluated considering the aforesaid denoised signals. In the present work, mathematical models have been developed for chatter index and metal removal rate using feedforward backpropagation–based artificial neural network considering three activation functions: TANSIG, LOGSIG and PURELIN. Furthermore, multi-objective genetic algorithm technique has been applied to evaluate stable cutting zones with maximized metal removal rate. TANSIG activation function found to be best option to achieve the aforesaid objectives. Good correlation between the artificial neural network predicted results and experimental ones validate the developed technique.

Quantification of tool chatter and metal removal rate using wavelet denoising and statistical approach

Tool chatter is an unavoidable phenomenon encountered in machining processes. Acquired raw chatter signals are contaminated with various types of ambient noises. Signal processing is an efficient technique to explore chatter as it eliminates unwanted background noise present in the raw signal. In this study, experimentally recorded raw chatter signals have been denoised using wavelet transform in order to eliminate the unwanted noise inclusions. Moreover, effect of machining parameters such as depth of cut ( d), feed rate ( f) and spindle speed ( N) on chatter severity and metal removal rate has been ascertained experimentally. Furthermore, in order to quantify the chatter severity, a new parameter called chatter index has been evaluated considering aforesaid denoised signals. A set of 15 experimental runs have been performed using Box–Behnken design of experiment. These experimental observations have been used to develop mathematical models for chatter index and metal removal rate considering response surface methodology. In order to check the statistical significance of control parameters, analysis of variance has been performed. Furthermore, more experiments are conducted and these results are compared with the theoretical ones in order to validate the developed response surface methodology model.