Experimental, Cae Evaluation and Optimization of Hard Turning of Aisi D3 Tool Steel

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Author(s): Varaprasad Bhemuni, Srinivasa Rao Chalamalasetti, Pavan Kumar Konchada, & Venkata Vinay Pragada
Source: Varaprasad Bhemuni, Srinivasa Rao Chalamalasetti, Pavan Kumar Konchada, & Venkata Vinay Pragada, Experimental, Cae Evaluation and Optimization of Hard Turning of Aisi D3 Tool Stee, Journal of Mechanical Engineering Research and Developments, vol. 40, no. 2, pp. 349-356, 2017. 
Document Type: Research Article

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Abstract: Machining operation is still irrefutably vital process in manufacturing, that involves complex relation of process parameters (like cutting speed, feed rate, depth of cut, nature of cooling, etc.) to performance characteristics (like surface finish, tool life, tool wear, machining time, machining cost, etc.). Numerous efforts were noticed in literature, to relate these parameters with each other so as to implement cost effective machining with lees efforts. The present work is one such attempt made towards optimisation of cutting parameters for turning of  AISI D3 under wet environment using reliable optimization techniques like Shifted Hammersley Sampling (Screening) and Multi-Objective Genetic Algorithm (MOGA) using Response Surface Generation Technique. The Design of Experiments (DOE) was established based on Central Composite Design (CCD) with 3 levels for each input parameter. The results of experiments were verified by simulating similar machining conditions within a virtual machining facility eased by CAE software package ‘Deform 3D’. The optimisation of cutting parameters is carried out in ‘ANSYS workbench 15 DesignXplorer for optimization’. Various response surfaces are generated using Kriging Technique to understand the effect of process parameters on each performance characteristic that is to be optimized. The optimum values suggested by each optimization technique are compared so as to arrive at the best feasible optimum value for wet machining.

Keywords: Hard turning; Computer aided engineering; Optimization; Shifted hammersley sampling; MOGA.