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<p>In Electrical Discharge Machining (EDM), electrode material selection is a critical factor determining the quality, productivity, and tool life of the machining process. Materials with high wear resistance can reduce tool costs and increase machining accuracy. This research utilizes the investment casting method, a precise casting technique capable of forming materials with complex geometries and casting difficult-to-cast alloys. The electrode is casted from three material types, including CuZn5, CuZn30, and ASTM A532 Class III Type A (A532), all produced by the investment casting method. Among these, A532 is a high-wear-resistant material that can only be shaped by investment casting. This study aims to evaluate the machining capability of materials made from investment-cast electrodes and simultaneously assess the influence of technological parameters (including material type, current, Ton, and Toff on the material removal rate (MRR), tool wear rate (TWR), and workpiece surface roughness (WSR) of the DC53 workpiece material using the Taguchi L9 method. Taguchi and Analysis of Variance (ANOVA) were employed to identify the most influential factor on the output results and to find the optimal parameter set for each parameter. Furthermore, Grey Relational Analysis (GRA) was also used for multi-objective optimization of the input and output parameters. The results demonstrated that current has the strongest and most statistically significant effect on the material removal rate (52.43%), and the optimal parameter set for MRR was M = CuZn30, I = 7&#xa0;A, Ton = 150 µs, and Toff = 30 µs with an optimal MRR of 0.104012&#xa0;g/min. With contribution percentages of 52.72% and 63.45%, respectively, A532 had the biggest impact on EWR (with the optimal EWR being 0.00297&#xa0;g/min), and CuZn30 had the most factor influence on WSR (WSR was 3.24&#xa0;μm). The optimal variable set for EWR was M = A532, I = 3&#xa0;A, Ton = 90 µs, and Toff = 30 µs; and the optimal parameter set for WSR was M = CuZn30, I = 3&#xa0;A, Ton = 90 µs, and Toff = 30 µs. Moreover, with GRA, the optimal parameter set for the four output factors was M = A532, I = 3&#xa0;A, Ton = 120 µs, and Toff = 90 µs. This research opens up the possibility of creating EDM electrodes from difficult-to-machine alloys with good electrode wear resistance.</p>

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Evaluated the effect of the casted-electrode materials on the EDM process of cold-worked mold steel

  • 期刊论文

  • 发表日期：

    2026年2月18日

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  • Thanh Tan Nguyen,
  • Thanh Duy Nguyen,
  • Quoc Dung Huynh,
  • Van Tron Tran,
  • Hieu Giang Le,
  • Van-Thuc Nguyen

摘要

In Electrical Discharge Machining (EDM), electrode material selection is a critical factor determining the quality, productivity, and tool life of the machining process. Materials with high wear resistance can reduce tool costs and increase machining accuracy. This research utilizes the investment casting method, a precise casting technique capable of forming materials with complex geometries and casting difficult-to-cast alloys. The electrode is casted from three material types, including CuZn5, CuZn30, and ASTM A532 Class III Type A (A532), all produced by the investment casting method. Among these, A532 is a high-wear-resistant material that can only be shaped by investment casting. This study aims to evaluate the machining capability of materials made from investment-cast electrodes and simultaneously assess the influence of technological parameters (including material type, current, Ton, and Toff on the material removal rate (MRR), tool wear rate (TWR), and workpiece surface roughness (WSR) of the DC53 workpiece material using the Taguchi L9 method. Taguchi and Analysis of Variance (ANOVA) were employed to identify the most influential factor on the output results and to find the optimal parameter set for each parameter. Furthermore, Grey Relational Analysis (GRA) was also used for multi-objective optimization of the input and output parameters. The results demonstrated that current has the strongest and most statistically significant effect on the material removal rate (52.43%), and the optimal parameter set for MRR was M = CuZn30, I = 7 A, Ton = 150 µs, and Toff = 30 µs with an optimal MRR of 0.104012 g/min. With contribution percentages of 52.72% and 63.45%, respectively, A532 had the biggest impact on EWR (with the optimal EWR being 0.00297 g/min), and CuZn30 had the most factor influence on WSR (WSR was 3.24 μm). The optimal variable set for EWR was M = A532, I = 3 A, Ton = 90 µs, and Toff = 30 µs; and the optimal parameter set for WSR was M = CuZn30, I = 3 A, Ton = 90 µs, and Toff = 30 µs. Moreover, with GRA, the optimal parameter set for the four output factors was M = A532, I = 3 A, Ton = 120 µs, and Toff = 90 µs. This research opens up the possibility of creating EDM electrodes from difficult-to-machine alloys with good electrode wear resistance.