Optimal Process Parameters for Higher Productivity and Stable Turning on CNC Lathe

Abstract

In the present competitive scenario, it is the prime concern of manufacturing industries to manufacture excellent quality products with higher productivity. Productivity is related to material removal rate (MRR) in turning operations. Moreover, MRR and chatter vibrations are dependent on input process parameters viz. depth of cut, spindle speed and feed rate. So, while selecting these machining parameters for higher MRR, it becomes imperative that effect of these parameters on chatter vibrations should not be overlooked. In the present research work, a new methodology has been proposed for achieving the aforementioned objective. The proposed methodology is based on signal processing, statistical and artificial intelligence approach. Machining signals generated during the turning of Al 6061-T6 have been acquired using a microphone. Initially, acquired signals have been processed using three signal processing techniques viz. Empirical Mode Decomposition (EMD), Local Mean Decomposition (LMD) and Merged Wavelet Denoising and Local Mean Decomposition (WDLMD). It has been observed that among these techniques WDLMD yielded better decomposition results. The decomposed signals have been analyzed using three statistical chatter indicators (Root Mean Square (RMS), Peak to Peak value and Absolute Mean Amplitude (AMA)) considering Nakagami distribution approach for ascertaining the thresholds of chatter severity. Prediction models of most effective statistical chatter indicator (AMA) and MRR have been developed using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) techniques. ANN models yielded better prediction results. Moreover, ANN prediction models have been optimized using Multi-Objective Genetic Algorithm (MOGA) for ascertaining an optimal range of process parameters for stable turning with higher MRR. Finally, obtained stable range has been validated by performing more experiments. newline