基于T-GRA、TOPSIS和ANN混合模型的Inconel 718上PMEDM多响应优化

IF 1.2 Q3 ENGINEERING, MECHANICAL
Ram Sai, Jeavudeen Shiek, Shaul Syed
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引用次数: 0

摘要

因科乃尔718是一种镍基高温合金,由于其在高温下保持硬度的特性,被认为是最难加工的材料之一。本研究考察了基于edcuter的PMEDM加工在Inconel 718上的性能。田口l9oa已用于电流,脉冲关闭时间&脉冲开启时间作为工艺参数,输出侧压力为6 bar,用于氧化铝混合介质。材料去除率(MRR)、刀具磨损率(TWR)、以表面粗糙度(SR)作为输出响应。结果已通过MADM技术,即基于田口的灰色关联分析(T-GRA) &指标值的分析。此外,从T-GRA &TOPSIS已通过开发单层人工神经网络模型进行了验证。人工神经网络模型预测的T-GRA和TOPSIS的排名相同,r值为0.924 &0.871,分别。方差分析也被用来分析参数对输出响应的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-response optimization of PMEDM on Inconel 718 using hybrid T-GRA, TOPSIS, and ANN model
Inconel 718 is one of the Nickel-based superalloys considered one of the most difficult-to-machine materials owing to its property to retain hardness at higher temperatures. This study examined the performance of Edcutor-based PMEDM machining on Inconel 718. Taguchi L9 OA has been used with current, Pulse-OFF time & Pulse-ON time as process parameters with a delivery side pressure of 6 bar for the Alumina mixed dielectric. Material removal rate (MRR), Tool wear rate (TWR), & surface roughness (SR) have been taken as output responses. The results have been investigated by MADM techniques, namely Taguchi-based Grey Relational Analysis (T-GRA) & TOPSIS analysis. Furthermore, the ranks obtained from T-GRA & TOPSIS have been validated by developing a single layered ANN model. Ranks predicted by the ANN model are the same for T-GRA and TOPSIS and the R-values are 0.924 & 0.871, respectively. ANOVA has also been used to analyze parameter effects on output responses.
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来源期刊
FME Transactions
FME Transactions ENGINEERING, MECHANICAL-
CiteScore
3.60
自引率
31.20%
发文量
24
审稿时长
12 weeks
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