Implementation of Taguchi method with hybrid decision making tools for prediction of surface characteristics for powder-mixed EDM of WC

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• 作者：Jagdeep Singh ; Rajiv Kumar Sharma ; ^{erjagdeep88@gmail.com" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Electrical discharge machining ; Surface roughness ; Micro-hardness ; Fuzzy and ANFIS
• 刊名：Perspectives in Science
• 出版年：2016
• 出版时间：September 2016
• 年：2016
• 卷：8
• 期：Complete
• 页码：455-458
• 全文大小：358 K

文摘

This paper aims to develop a method that optimize the multi-performance characteristics (MPCs), i.e. micro-hardness (μ-H) and surface roughness (SR) for the powder mixed electrical discharge machining (PMEDM) of Tungsten Carbide (WC-Co) alloy. Initially, authors successfully achieved the optimal parameter selection for PM-EDM of WC alloy by using grey relational analysis (Sharma and Singh, 2014a). There is a still chance of presence of uncertainty/fuzziness in GRA results as it has “higher-the-better” and “lower-the-better” characteristics. Therefore, authors established the grey-fuzzy and grey-ANFIS approach to handle that uncertainty and discreteness present in the data, this study also shows the comparison between these methods. Theoretical prediction of grey-fuzzy approach shows that the proposed approaches can prove useful for optimizing MPCs. It is observed that experiment no. 24 with pulse-on time, 100 μs (A₃); pulse-off, 50 μs (B₂); current, 9 Å (C₃) and powder, C (D₁) factor combination provides best MPC'S amongst 27 experiments. This study shows that the use of graphite powder is found to be more suitable for improvement in surface characteristics of WC-Co. Results shows that pulse-on time is the dominating factor comparative to others factors which affect the study.