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<p>Among different hybrid non-traditional machining processes, laser-assisted jet electrochemical machining (LA-JECM) and ultrasonic-assisted electrical discharge machining (UA-EDM) are the two sporadically adopted processes to ensure high quality end products. In this paper, LA-JECM and UA-EDM processes are considered for their parametric optimization using three multi-criteria decision making (MCDM) methods, i.e. weighted aggregated sum product assessment (WASPAS), combinative distance-based assessment (CODAS) and additive ratio assessment (ARAS) in picture fuzzy (PF) environment, considering the corresponding degree of neutrality from three decision makers. In both the examples, the optimal parametric combinations derived employing PF-WASPAS, PF-CODAS and PF-ARAS exactly match with the observations of the past researchers. All the PF-MCDM methods suggest an ideal combination of supply voltage = 80&#xa0;V, electrolyte concentration = 20&#xa0;g/l, inter-electrode gap = 2&#xa0;mm and duty cycle = 60% to achieve a balanced compromise among the performance metrics for the LA-JECM process. Similarly, for the UA-EDM process, they recommend use of copper tool material with gap current = 10 A, discharge voltage = 45&#xa0;V, pulse-on duration = 200&#xa0;µs, pulse-off duration = 15&#xa0;µs and tool lift time = 2&#xa0;s to achieve the best performance across all the responses. A sensitivity analysis is also conducted to validate robustness of the considered MCDM methods for varying criteria (response) weights in PF environment. It indicates that PF-CODAS picks out the same combination of input parameters as optimal under every weighting scenario, whereas, PF-ARAS and PF-WASPAS search out the same intermix in 75% and 61% of the weighting scenarios, respectively, for the LA-JECM process. Furthermore, all the PF-MCDM methods identify the same optimal combination of the input parameters in all the scenarios for the UA-EDM process. Therefore, PF-CODAS appears as the most robust approach being least sensitive to any noise or ambiguity in the decision making process.</p> Graphical Abstract <p></p>

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Optimization of hybrid non-traditional machining processes using multi-criteria decision making methods in picture fuzzy environment

  • 期刊论文

  • 发表日期：

    2025年6月5日

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  • Samriddhya Ray Chowdhury,
  • Srinjoy Chatterjee,
  • Shankar Chakraborty

摘要

Among different hybrid non-traditional machining processes, laser-assisted jet electrochemical machining (LA-JECM) and ultrasonic-assisted electrical discharge machining (UA-EDM) are the two sporadically adopted processes to ensure high quality end products. In this paper, LA-JECM and UA-EDM processes are considered for their parametric optimization using three multi-criteria decision making (MCDM) methods, i.e. weighted aggregated sum product assessment (WASPAS), combinative distance-based assessment (CODAS) and additive ratio assessment (ARAS) in picture fuzzy (PF) environment, considering the corresponding degree of neutrality from three decision makers. In both the examples, the optimal parametric combinations derived employing PF-WASPAS, PF-CODAS and PF-ARAS exactly match with the observations of the past researchers. All the PF-MCDM methods suggest an ideal combination of supply voltage = 80 V, electrolyte concentration = 20 g/l, inter-electrode gap = 2 mm and duty cycle = 60% to achieve a balanced compromise among the performance metrics for the LA-JECM process. Similarly, for the UA-EDM process, they recommend use of copper tool material with gap current = 10 A, discharge voltage = 45 V, pulse-on duration = 200 µs, pulse-off duration = 15 µs and tool lift time = 2 s to achieve the best performance across all the responses. A sensitivity analysis is also conducted to validate robustness of the considered MCDM methods for varying criteria (response) weights in PF environment. It indicates that PF-CODAS picks out the same combination of input parameters as optimal under every weighting scenario, whereas, PF-ARAS and PF-WASPAS search out the same intermix in 75% and 61% of the weighting scenarios, respectively, for the LA-JECM process. Furthermore, all the PF-MCDM methods identify the same optimal combination of the input parameters in all the scenarios for the UA-EDM process. Therefore, PF-CODAS appears as the most robust approach being least sensitive to any noise or ambiguity in the decision making process.

Graphical Abstract