Stability Analysis of Milling Operation for Higher Productivity

Abstract

In the present work, both theoretical and experimental works have been carried out to predict, detect and investigate tool chatter. In the theoretical analysis; 2 degrees of freedom mathematical model has been developed. Thereafter, developed theoretical models have been validated by comparing with the experimental ones. In the experimental analysis, total 27 end milling experiments (slotted) have been performed considering full factorial design of three process variables viz. axial depth of cut, table feed rate and spindle speed. Machining signals have been acquired using microphone. These recorded signals have been processed using three self-adaptive signal processing techniques viz. Empirical Mode Decomposition (EMD), Local Mean Decomposition (LMD) and Spline based Local Mean Decomposition (SBLMD). The chatter signals have been reconstructed considering two major indicators, viz. Correlation Coefficient (CC) and Normalized Energy Ratio (NER). newlineThese reconstructed signals have been further analysed to evaluate the response viz. Chatter Indicator (CI) in order to represent the chatter severity. Moreover, another response viz. Material Removal Rate (MRR) has also been ascertained for all the 27 milling experiments. In order to develop, real online monitoring system, twelve time domain based statistical indicators are invoked. newlinePrediction models of CI and MRR have been developed considering four techniques viz. Response Surface Methodology (RSM), Gray Relational Analysis (GRA), Artificial Neural Network (ANN) and Self Organizing Maps (SOM). Further, these models have been compared to select the most suitable one for predicting the chatter severity and MRR, simultaneously. It has been deduced that among these, ANN model is better for the aforementioned purpose. newlineFinally, ANN based prediction model have been optimized using Multi-Objective Particle Swarm Optimization (MOPSO) in order to obtain an optimal range of input milling parameters that will yield higher material removal rate with minimal chatter. newline