Investigation and optimization for enhanced machining features of heat-treated SKD61 steel as processed by powder-mixed electro-discharge machining in finishing regime

Van-Tao Le; Thi Hong Minh Nguyen; Tien Dung Hoang

doi:10.15625/2525-2518/21413

Author affiliations

Authors

Van-Tao Le \(^1\) ATC, Le Quy Don Technical University, 236 Hoang Quoc Viet, Nghia Do Ward, Ha Noi, Viet Nam https://orcid.org/0000-0001-5952-5117
Thi Hong Minh Nguyen \(^2\) SME, Hanoi University of Science and Technology, 1 Dai Co Viet, Bach Mai Ward, Ha Noi, Viet Nam
Tien Dung Hoang \(^3\) SMAE, Hanoi University of Industry, 298 Cau Dien, Tay Tuu Ward, Ha Noi, Viet Nam https://orcid.org/0000-0003-3543-7539

DOI:

https://doi.org/10.15625/2525-2518/21413

Keywords:

PMEDM, RMR, REW, surface attributes, multi-criteria optimization

Abstract

Machinability investigation of SKD61 steel in heat-treated state as machined by electro-discharge machining (EDM) in finishing regime with adding tungsten compound powder is very restrictive. Hence, the machinability of this material state, comprising rate of material removal (RMR), rate of electrode wear (REW), and surface roughness (R_a), was explored under the influence of control parameters {including peak-current (I_p), pulse-on time (T_on), and powder amount (A_p)}. In addition, finding the optimal domain of control parameters is meaningful in improving the RMR, R_a, and reducing REW. With this goal, the predictive models for RMR, R_a, and REW were instituted and evaluated to confirm these models' adequacy and accuracy by the analysis of variance (ANOVA). Eventually, desirable approach (DA) and technique for order of preference by similarity to ideal solution (TOPSIS) were executed for the multi-criteria optimization. The results revealed that I_p proves the most robust influence on RMR, REW, and R_a. However, the sequent influences are A_p and T_on for REW, while the reverse is for R_a. The value of RMR by DA is increased by 8.67 % versus TOPSIS. Meanwhile, the TOPSIS contributes the best optimum solution for REW and R_a, corresponding to a drop of 10 % and 0.5 % versus DA. In addition, surface features (defects, chemical composition distribution, TRL, surface topography, and crack acreage percentage (CAP)) at optimal parameters of the two algorithms were also considered.

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