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APPLICATION OF HEURISTIC OPTIMIZATION TOWARDS OPTIMAL PROCESS PARAMETERS IN ADVANCED MACHINING PROCESS.

Authors :
Faisal, Nadeem
Kumar, Kaushik
Davim, J. Paulo
Source :
Journal of Manufacturing Technology Research. 2018, Vol. 11 Issue 3/4, p143-163. 21p.
Publication Year :
2018

Abstract

Electrical discharge machining is a non-conventional machining method that is used for machining of hard-to-machine materials, components in which length to diameter ratio is very high or products with a very complicated shape. The process is commonly used in automobile, biomedical, chemical, aerospace, tool and die industries etc. It is very essential to select optimum values of input process parameters to maximize the machining performance. In this paper an attempt has been made to carry out multiobjective optimization of the surface roughness (SR) and material removal rate (MRR) for the EDM process of EN 19 on a CNC EDM machine using copper electrode through evolutionary optimization techniques like teaching Learning Based Optimization (TLBO) technique and biogeography-based optimization (BBO) technique. The input parameter considered for the optimization are Current (A), Voltage (V), Pulse off time (µs), and Pulse on time (µs). TLBO and BBO technique wereused to obtain maximum MRR and minimize the SR. It was found that SR and MRR increased linearly when discharge current was in mid-range however non-linear increment of MRR and SR was found when the current was too small or too large. Scanning Electron Microscope (SEM) images also indicated a decreased SR. In addition, obtained optimized values were validated for testing the significance of the TLBO and BBO technique and a very small error value of MRR and SR was found. BBO outperformed TLBO in every aspect like less percentage error and better-optimized values, however, TLBO took less computation time than the BBO. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
19438095
Volume :
11
Issue :
3/4
Database :
Academic Search Index
Journal :
Journal of Manufacturing Technology Research
Publication Type :
Academic Journal
Accession number :
134921069