Parametric optimization of EDD using RSM-Grey-TLBO-based MCDM approach for commercially pure titanium
PurposeElectric discharge drilling (EDD) is used to drill quality microholes on any conductive materials. EDD process parameters play a crucial role in the drilling. Depending upon the material characteristics, the cost of drilling also changes. Therefore, a suitable method is required to control th...
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Veröffentlicht in: | Grey systems 2020-04, Vol.10 (2), p.231-245 |
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Sprache: | eng |
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Zusammenfassung: | PurposeElectric discharge drilling (EDD) is used to drill quality microholes on any conductive materials. EDD process parameters play a crucial role in the drilling. Depending upon the material characteristics, the cost of drilling also changes. Therefore, a suitable method is required to control the process parameters and drill quality microholes.Design/methodology/approachThe input process parameters in the present work are peak current (Ip), pulse on-time (Ton) and pulse off-time (Toff). The trials were intended in accordance to central composite face-centered design of response surface methodology (RSM). The output responses, namely drilling rate (DR) and electrode wear ratio (EWR), were converted into a single response, that is, grade using Grey relational analysis (GRA). The grade value is further modeled by regression analysis. The empirical model was figured out using teaching–learning-based optimization (TLBO). The RSM-Grey-TLBO-based multicriteria decision-making (MCDM) is used to investigate the optimized process parameter setting.FindingsThe RSM-Grey-TLBO-based MCDM approach suggests that the optimized setting for DR and EWR is Ip: 3A; Ton: 40 µs; Toff: 42 µs. The percentage errors for the predicted and experimental results are 8.1 and 7.5% in DR and EWR, respectively.Originality/valueThe parametric optimization of EDD using RSM-Grey-TLBO-based MCDM approach while machining commercially pure titanium is still underway. Thus, this MCDM approach will give a path to the researchers working in this direction. |
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ISSN: | 2043-9377 2043-9385 |
DOI: | 10.1108/GS-01-2020-0008 |