Three-Stage Optimization of Surface Finish in WEDM of D2 Tool Steel via Taguchi Design and ANOVA Analysis

Wire electrical discharge machining (WEDM) is a standard micro-manufacturing technology. In WEDM, surface roughness (SR), deviation dimension (DD), and machining time (MT) are critical requirements that impact machining quality and are affected by various input parameters. The workpiece often performs multiple machining steps (roughing, semi-finishing, and finishing) to achieve high accuracy. Each machining step directly affects the accuracy and machining time, and the preceding machining step influences the subsequent machining step parameters. Many input control parameters regulate WEDM’s performance. Thus, optimizing process control parameters at each step is essential to achieve optimal results. This study investigates the influence of input parameters, including pulse on time (Ton), pulse off time (Toff), and servo voltage (SV), on SR, DD, and MT when machining AISI D2 mold steel through rough, semi-finish, and finish cutting. Taguchi and Analysis of Variance (ANOVA) are applied to analyze and optimize this WEDM process. The results display that the optimal surface roughness values for rough, semi-finish, and finish-cut stages are 2.03 µm, 1.77 µm, and 0.57 µm, corresponding to the parameter set of Ton = 6 μs, Toff = 10 μs, and SV = 30 V; Ton = 3 μs, Toff = 15 μs, and SV = 60 V; and Ton = 21 μs, Toff = 45 μs, and SV = 60 V, respectively. In addition, in the finish-cut stage, the parameters for optimal DD of 0.001 mm (0.04%) are Ton = 3 μs, Toff = 15 μs, and SV = 40 V. In contrast, those values for optimal MT of 218 s are Ton = 3 μs, Toff = 30 μs, and SV = 40 V. All optimal input values are confirmed by the manufacturing mold and die parts.

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Publication Details

Journal
Metals
Published
2025-06-19
DOI
https://doi.org/10.3390/met15060682
Citations
3
Primary Topic
Advanced Machining and Optimization Techniques
Type
article
Field-Weighted Citation Impact
2.19
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article

Three-Stage Optimization of Surface Finish in WEDM of D2 Tool Steel via Taguchi Design and ANOVA Analysis

Van Tron Tran, Thanh Tan Nguyen, Van–Thuc Nguyen, Văn Thành Nguyễn et al.
3 citations
Metals
Advanced Machining and Optimization Techniques
2.19
article

Three-Stage Optimization of Surface Finish in WEDM of D2 Tool Steel via Taguchi Design and ANOVA Analysis

Van Tron Tran, Thanh Tan Nguyen, Van–Thuc Nguyen, Văn Thành Nguyễn, Bui Phuoc Phi
article en
3 citations

Abstract

Wire electrical discharge machining (WEDM) is a standard micro-manufacturing technology. In WEDM, surface roughness (SR), deviation dimension (DD), and machining time (MT) are critical requirements that impact machining quality and are affected by various input parameters. The workpiece often performs multiple machining steps (roughing, semi-finishing, and finishing) to achieve high accuracy. Each machining step directly affects the accuracy and machining time, and the preceding machining step influences the subsequent machining step parameters. Many input control parameters regulate WEDM’s performance. Thus, optimizing process control parameters at each step is essential to achieve optimal results. This study investigates the influence of input parameters, including pulse on time (Ton), pulse off time (Toff), and servo voltage (SV), on SR, DD, and MT when machining AISI D2 mold steel through rough, semi-finish, and finish cutting. Taguchi and Analysis of Variance (ANOVA) are applied to analyze and optimize this WEDM process. The results display that the optimal surface roughness values for rough, semi-finish, and finish-cut stages are 2.03 µm, 1.77 µm, and 0.57 µm, corresponding to the parameter set of Ton = 6 μs, Toff = 10 μs, and SV = 30 V; Ton = 3 μs, Toff = 15 μs, and SV = 60 V; and Ton = 21 μs, Toff = 45 μs, and SV = 60 V, respectively. In addition, in the finish-cut stage, the parameters for optimal DD of 0.001 mm (0.04%) are Ton = 3 μs, Toff = 15 μs, and SV = 40 V. In contrast, those values for optimal MT of 218 s are Ton = 3 μs, Toff = 30 μs, and SV = 40 V. All optimal input values are confirmed by the manufacturing mold and die parts.

MetalsVol. 15(6)
Industrial University of Ho Chi Minh City (VN), Ho Chi Minh City University of Technology and Engineering (VN)
Openalex Percentile: Top 12%
Advanced Machining and Optimization Techniques
2.19
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