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APPLICATION OF META-HEURISTIC TECHNIQUE IN TURNING PROCESS PARAMETER OPTIMIZATION BASED ON DIFFERENT OPERATING CONDITION

Devendra Patel, Jayesh Barve, Abhishek Shrotriya

Abstract


This work depicts an experimental study of the effect of cutting parameters such as spindle speed, depth of cut, and feed on tool abrasion and tool life. Choice of cutting parameters is an important part of the metal cutting process as it affects the requirement of power, tool wear, and tool life, surface quality of the workpiece and time of manufacturing, therefore, it is mandatory to develop an optimization method for selection of convenient cutting criterion during the material removing process. The objective of this work is to develop an optimization model through meta-heuristic techniques like PSO (Particle Swarm Optimization) to help the designer/manufacturer during the choice of appropriate cutting criterion for the metal cutting process.

In this work, a number of the experiment is conducted for the determination of life of tool and tool-wear rate. The data obtained from the experiment is analyzed further to forecast the best range of cutting velocity, the rate of feed and depth of cut. The machining of steel AISI 1016 work-piece material was carried out in a heavy duty all geared conventional lathe to estimate the machinability. After obtaining a regression equation from MINITAB software meta-heuristic technique named PSO are enforced over it by using MATLAB software, to find out the optimized result. By two-way general linear model analysis of variance ANOVA, the impact of cutting parameters on tool wear are explored with 95% confidence level. Also, the statistical significances have been checked. The results indicate that flank wear is mainly influenced by the cutting speed and depth of cut.

Keywords:  Cutting Speed, Depth of Cut, Feed, GA, PSO, CSA, ANOVA, Tool Wear


Keywords


utting Speed, Depth of Cut, Feed, GA, PSO, CSA, ANOVA, Tool Wear

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Copyright (c) 2017 Devendra Patel, Jayesh Barve, Abhishek Shrotriya