Ascertaining of chatter stability using wavelet denoising and artificial neural network

In modern machining industries, tool chatter detection and suppression along with maximized metal removal rate is a challenging task. Inexpedient vibration between cutting tool and work piece promotes unstable cutting. This results in enhanced detritions of tool and poor surface finish along with unpredictable metal removal rate. In the present work, effect of machining parameters such as depth of cut (d), feed rate (f) and spindle speed (N) on chatter severity and metal removal rate have been ascertained experimentally. Experimentally recorded raw chatter signals have been denoised using wavelet transform. An artificial neural network model based on feed forward back propagation network has been proposed for predicting stable cutting zone and metal removal rate in turning process. It has been deduced that Tangent Sigmoid activation function in an artificial neural network is the best option to achieve the aforesaid objectives. Well correlation between the artificial neural network predicted results and experimental ones validate the developed technique of ascertaining the tool chatter severity.