Optimization of Process Parameters for Wire Electrical Discharge Machining of 9Cr18Mov Based on Grey Relational Analysis

9Cr18MoV stainless steel is widely employed in cutting-tool applications owing to its exceptional hardness and corrosion resistance. In this study, we systematically optimized the wire electrical discharge machining (WEDM) process parameters for 9Cr18MoV stainless steel through an L16 (44) orthogonal experimental design. The key parameters investigated include pulse width (Ton), pulse interval (Toff), peak current (IP), and wire feed speed (WS), with cutting efficiency (CE) and surface roughness (Ra) serving as the primary optimization objectives. A signal-to-noise ratio (SNR) analysis was applied to assess the effects of the individual parameters and derive single-objective optimal configurations. Subsequently, grey relational analysis (GRA) integrated with analytic hierarchy process (AHP)-based weighting was employed to establish a multi-objective optimal parameter set, which was experimentally validated. The results reveal that the optimal multi-objective performance was attained at Ton = 28 μs, Toff = 3 μs, IP = 9 A, and WS = level 3. SEM characterization confirmed that this parameter combination yields a more uniform surface morphology, with diminished oxidation and molten debris deposition, thereby significantly enhancing surface integrity. The adoption of this optimized parameter set not only ensures superior machining efficiency but also results in improved surface quality.

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Publication Details

Journal
Processes
Published
2025-05-17
DOI
https://doi.org/10.3390/pr13051547
Primary Topic
Advanced Machining and Optimization Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Optimization of Process Parameters for Wire Electrical Discharge Machining of 9Cr18Mov Based on Grey Relational Analysis

Zhou Sun, Linglei Kong, Shuchuan Gan, Weining Lei et al.
Processes
Advanced Machining and Optimization Techniques
article

Optimization of Process Parameters for Wire Electrical Discharge Machining of 9Cr18Mov Based on Grey Relational Analysis

Zhou Sun, Linglei Kong, Shuchuan Gan, Weining Lei, Yuexiang Du, Ruixin Mao
article en

Abstract

9Cr18MoV stainless steel is widely employed in cutting-tool applications owing to its exceptional hardness and corrosion resistance. In this study, we systematically optimized the wire electrical discharge machining (WEDM) process parameters for 9Cr18MoV stainless steel through an L16 (44) orthogonal experimental design. The key parameters investigated include pulse width (Ton), pulse interval (Toff), peak current (IP), and wire feed speed (WS), with cutting efficiency (CE) and surface roughness (Ra) serving as the primary optimization objectives. A signal-to-noise ratio (SNR) analysis was applied to assess the effects of the individual parameters and derive single-objective optimal configurations. Subsequently, grey relational analysis (GRA) integrated with analytic hierarchy process (AHP)-based weighting was employed to establish a multi-objective optimal parameter set, which was experimentally validated. The results reveal that the optimal multi-objective performance was attained at Ton = 28 μs, Toff = 3 μs, IP = 9 A, and WS = level 3. SEM characterization confirmed that this parameter combination yields a more uniform surface morphology, with diminished oxidation and molten debris deposition, thereby significantly enhancing surface integrity. The adoption of this optimized parameter set not only ensures superior machining efficiency but also results in improved surface quality.

ProcessesVol. 13(5)
Quanzhou Normal University (CN), Jiangsu University of Technology (CN)
National Natural Science Foundation of China, Jiangsu University, Natural Science Foundation of Fujian Province, Jiangsu University of Technology
Openalex Percentile: Top 91%
Advanced Machining and Optimization Techniques
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Optimization of Process Parameters for Wire Electrical Discharge Machining of 9Cr18Mov Based on Grey Relational Analysis — Zhou Sun, Linglei Kong, et al. · Processes (2025) | TGRS Research Map | TGRS