Performance Prediction in EDM Process for Al 6061 Alloy Using Response Surface Methodology and Genetic Algorithm

Al-Khawarizmi Engineering Journal

article

Hind Hadi Abdulridha¹  Marwa Qasim Ibraheem¹  Ahmed Ghazi Abdulameer¹ 

[1] Department of Production Engineering and Metallurgy/ University of Technology/ Baghdad/ Iraq

关键词: Electro Discharge Machining;    Genetic Algorithm;    MRR;    Tool Wear.;   

DOI  :  10.22153/kej.2022.08.001

学科分类：社会科学、人文和艺术（综合）

来源: Al-Khwarizmi College of Engineering – University of Baghdad

【 摘 要 】

The Electric Discharge (EDM) method is a novel thermoelectric manufacturing technique in which materials are removed by a controlled spark erosion process between two electrodes immersed in a dielectric medium. Because of the difficulties of EDM, determining the optimum cutting parameters to improve cutting performance is extremely tough. As a result, optimizing operating parameters is a critical processing step, particularly for non-traditional machining process like EDM. Adequate selection of processing parameters for the EDM process does not provide ideal conditions, due to the unpredictable processing time required for a given function. Models of Multiple Regression and Genetic Algorithm are considered as effective methods for determining the optimal processing variables of Electrical Discharge Machining.The material removal rate (MRR) and tool wear (Tw) were investigated using the process variables of pulse on time (Ton ), pulse off time (Toff) , and current intensity (Ip) . The established empirical models were used to perform Genetic Algorithm (GA) to maximize (MRR) and minimize (Tw). The optimization results were utilized to establish machining conditions, validate empirical models, and obtain optimization outcomes. The optimal result that appears in this work was the pulse on (176.261 μs), pulse off (39.42 μs), and current intensity (23.62 Amp.) to maximize the MRR to (0.78391 g/min) and reduce tool wear to (0.0451 g/min).

【 授权许可】

CC BY