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Multi-attribute optimization of the wedm process for surface characteristics and productivity

Nguyen, T.-T. and Nguyen, K.D. (2021) Multi-attribute optimization of the wedm process for surface characteristics and productivity. Tehnicki Vjesnik, 28 (2). pp. 473-480. ISSN 13303651

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Abstract

Wire-cut electrical discharge machining (WEDM) process is a proficient operation for the precise manufacturing of complex profiles of difficult-to-cut materials. The purpose of the current paper is to determine optimal processing inputs, including the WEDM current (CU), WEDM voltage (DV), pulse duration (Ton), and the speed of the wire electrode (WS) to decrease the depth of the recast layer (DL) as well as the root mean square roughness (RMSR) and enhance the cutting speed (CS) of the WEDM operation. The radius basis function (RBF) approach is employed to develop the predictive models of technical responses. The non-dominated sorting particle swarm optimization (NSPSO) is applied to obtain the optimal values of processing inputs and WEDM performances measured. The findings revealed that the proposed RBF models significantly contribute to the accurate prediction for the WEDM outputs. The optimal values of the CU, Ton, DV, and WS are 5.0 A, 6.0 μs, 33.0 V, and 4 m/min, respectively.The optimized values of the RMSR and DL are decreased by 60.98% and 15.55%, respectively, while the CS is enhanced by 8.90%. This work can be listed as an alternative solution for improving the surface characteristics and productivity of the WEDM process. © 2021, Strojarski Facultet. All rights reserved.

Item Type: Article
Divisions: Faculties > Faculty of Mechanical Engineering
Identification Number: 10.17559/TV-20190728094458
Uncontrolled Keywords: Cutting; Electric discharge machining; Electric discharges; Multiobjective optimization; Optimal systems; Particle swarm optimization (PSO); Productivity; Screening; Accurate prediction; Alternative solutions; Difficult-to-cut materials; Non-dominated Sorting; Radius basis function; Root mean square roughness; Surface characteristics; Wire-cut electrical discharge machining; Predictive analytics
Additional Information: Language of original document: English. All Open Access, Gold.
URI: http://eprints.lqdtu.edu.vn/id/eprint/8662

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