Scholarlink

<p>Titanium alloys have exceptional hardness and high toughness, which can cause significant challenges in traditional machining. Wire-electrical discharge machining (WEDM) process offers excellent accuracy and high precision compared to conventional machines. Design of experimental (DOE) technique provides a systematic way to conduct the experimental runs with the least trials by saving time and cost. Thus, the current work focuses on the modelling of WEDM process at numerous input process environments using Taguchi and BBD-RSM approach. The variable input factors of WEDM process include pulse-on-time (T<sub>on</sub>), pulse current (I<sub>p</sub>), and pulse-off-time (T<sub>off</sub>), whereas the response measures of material removal rate (MRR) and surface roughness (SR) were taken. The performance and adequacy of Taguchi and BBD-RSM models were assessed by using ANOVA, coefficient of determination (R<sup>2</sup>), and residual plots. The effect of WEDM factors on performance measures was studied by using main effect plots. Based on Entropy criterion, the weights of MRR and SR response factors were computed to, in turn, 0.52 and 0.48. The practical tests defined in the DOE along with the MRR and SR were considered as inputs to the Naive Bayes (NB) predictive model. The prediction findings indicated the appropriate performance of the NB algorithm. The authors believe that the present study, which compares DOE techniques and their application in predicting process outcomes using Naive Bayes classifier, will be useful for users in different domains and various applications.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

An inclusive parametric study for performance improvement in WEDM process of pure titanium using Naive Bayes classifier

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月9日

查看全文
  • Jay Vora,
  • Shahil Rathod,
  • Masoud Seidi,
  • Saeed Yaghoubi,
  • Rakesh Chaudhari,
  • Subraya Krishna Bhat

摘要

Titanium alloys have exceptional hardness and high toughness, which can cause significant challenges in traditional machining. Wire-electrical discharge machining (WEDM) process offers excellent accuracy and high precision compared to conventional machines. Design of experimental (DOE) technique provides a systematic way to conduct the experimental runs with the least trials by saving time and cost. Thus, the current work focuses on the modelling of WEDM process at numerous input process environments using Taguchi and BBD-RSM approach. The variable input factors of WEDM process include pulse-on-time (Ton), pulse current (Ip), and pulse-off-time (Toff), whereas the response measures of material removal rate (MRR) and surface roughness (SR) were taken. The performance and adequacy of Taguchi and BBD-RSM models were assessed by using ANOVA, coefficient of determination (R2), and residual plots. The effect of WEDM factors on performance measures was studied by using main effect plots. Based on Entropy criterion, the weights of MRR and SR response factors were computed to, in turn, 0.52 and 0.48. The practical tests defined in the DOE along with the MRR and SR were considered as inputs to the Naive Bayes (NB) predictive model. The prediction findings indicated the appropriate performance of the NB algorithm. The authors believe that the present study, which compares DOE techniques and their application in predicting process outcomes using Naive Bayes classifier, will be useful for users in different domains and various applications.