3D finite element modeling and multi-objective optimization for controlling the electrochemical discharge drilling parameters using the tool feed monitoring system. (July 2022)

• Record Type: Journal Article
• Authors: Rajput, Viveksheel; Goud, Mudimallana; Suri, Narendra Mohan
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 236:Number 14(2022)
• Page Start: 7829
• Page End: 7852
• Publication Date: 2022-07
• Subjects: 3D finite element method -- Gaussian heat -- sparks -- tool contact -- regression -- grey relational analysis; Mechanical engineering -- Periodicals; 621.05
• DOI: 10.1177/09544062221079170
• Languages: English
• ISSNs: 0954-4062

Abstract

Electrochemical discharge machining drilling (ECDD) is acknowledged as a hybrid machining process that employs the thermal erosion and etching action for drilling. Apart from experimental findings, very rare 3D simulation studies have been covered for analyzing the ECDD. In the present investigation, a 3D simulation of the ECDD process is performed to analyze the process based on the finite element method (FEM). The predicted results are found to be in consensus with the experimental results. An increment of 10.79 mg and 5.04 mg in MRR is noticed with the concentration level increase from 10 wt.% to 60 wt.% and voltage level increase from 35V to 55V. However, a maximum error of 12.68% is observed with the FEM method of prediction when compared to experimental results. The simulation study is followed by the multi-objective optimization (Taguchi's L16) using grey relational analysis (GRA) for controlling the ECDD parameters. The simultaneous numerical and experimental assessment of the process with 3D Gaussian heat distribution using a novel tool feed monitoring system during soda-lime glass machining leads to the article's novelty. A unique study on a number of tool contacts (NTC) with the work material is performed to minimize it to improve the geometrical characteristics. Material removal rate, overcut, number of tool contacts, and heat-affected zone are picked as response characteristics while tool feed rate, electrolyte concentration, applied voltage, and inter-electrode gap are selected as input variables. GRA optimized variables are obtained as 40V, 10 wt.%, 3 mm/min, and 35 mm with tool feed rate identified as the ascendant input variable having 35.46 percentage contribution.