In this paper, statistical models were developed to investigate effect of cutting parameters on surface roughness and root mean square of work piece vibration in boring of stainless steel. A mixed level design of experiments was prepared with process variables of nose radius, cutting speed and feed rate. According to design of experiments, eighteen experiments were conducted on AISI 316 stainless steel with PVD coated carbide tools. Surface roughness, tool wear and vibration of work piece were measured in each experiment. A laser Doppler vibrometer was used to measure vibration of work piece in the form of acousto optic emission signals. These signals were processed and transformed in to different frequency zones using a fast Fourier transformer. Analysis of variance was used to identify significant cutting parameters on surface roughness and root mean square of work piece vibration. Predictive models like response surface methodology, artificial neural network and support vector machine were used to predict the surface roughness and root mean square of work piece vibration. Cutting parameters were optimized for minimum surface roughness and root mean square of work piece vibration using a multi response optimization technique.

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In this work, accurate 3D finite element models were developed to study and predict machining characteristics like tool vibration, tool wear, surface roughness, cutting force and thrust forces in the boring of Ti-6Al-4V alloy. Experiments were conducted on the proposed metal using carbide inserts at three levels of spindle speeds, depth of cuts and feed rates and experimental results were collected. Numerical simulation was carried out using Deform 3D software. Johnson-cook material model was also used in simulation to predict the machining characteristics. A Usui’s wear model was taken in simulation to calculate tool wear at different working conditions. Experimental data of the five machining characteristics were analysed using analysis of variance to identify the most significant parameters. Cutting speed, depth of cut and feed rate were found to be the most significant parameters. Simulated results of the machining characteristics were compared with the experimental data and found in a good agreement between them. An Artificial neural network (ANN) model was also developed and trained with the experimental data to validate the results. FEM simulation models provide relevant machining information without conducting experimentation for any metal.

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