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Data driven surrogate model-based optimization of the process parameters in electric discharge machining of D2 steel using Cu-SiC composite tool for the machined surface roughness and the tool wear

Authors :
Nalin Somani
Arminder Singh Walia
Nitin Kumar Gupta
Jyoti Prakash Panda
Anshuman Das
Sudhansu Ranjan Das
Source :
Revista de Metalurgia, Vol 59, Iss 2 (2023)
Publication Year :
2023
Publisher :
Consejo Superior de Investigaciones Científicas, 2023.

Abstract

Electrical discharge machining (EDM) is mainly utilized for the die manufacturing and also used to machine the hard materials. Pure Copper, Copper based alloys, brass, graphite, steel are the conventional electrode materials for EDM process. While machining with the conventional electrode materials, tool wear becomes the main bottleneck which led to increased machining cost. In the present work, the composite tool tip comprises 80% Copper and 20% silicon carbide was used for the machining of hardened D2 steel. The powder metallurgy route was used to fabricate the composite tool tip. Electrode wear rate and surface roughness were assessed with respect to the different process parameters like input current, gap voltage, pulse on time, pulse off time and dielectric flushing pressure. During the analysis it was found that Input current (I p ), Pulse on time (T on ) and Pulse off time (T off ) were the significant parameters which were affecting the tool wear rate (TWR) while the I p , T on and flushing pressure affected more the surface roughness (SR). SEM micrograph reveals that increase in I p leads to increase in the wear rate of the tool. The data obtained from experiments were used to develop machine learning based surrogate models. Three machine learning (ML) models are random forest, polynomial regression and gradient boosted tree. The predictive capability of ML based surrogate models was assessed by contrasting the R 2 and mean square error (MSE) of prediction of responses. The best surrogate model was used to develop a complex objective function for use in firefly algorithm-based optimization of input machining parameters for minimization of the output responses.

Details

Language :
English, Spanish; Castilian
ISSN :
00348570 and 19884222
Volume :
59
Issue :
2
Database :
Directory of Open Access Journals
Journal :
Revista de Metalurgia
Publication Type :
Academic Journal
Accession number :
edsdoj.b3e7abbb12a04cc195ed3f2dc0d68ca4
Document Type :
article
Full Text :
https://doi.org/10.3989/revmetalm.242