Scholarlink

<p>Electrical discharge machining (EDM) is fundamental, considering its advantages in machining complex shapes in difficult-to-cut materials. Although EDM control parameters have been widely investigated, less research has focused on debris removal methods, such as the electrode jump motion (EJM). This paper analyzes the control parameters of the EJM, including halt time (OFF), jump-up distance (JU), and jump-down time (JD), in the machining of Stavax ESR. Using analysis of variance and surface response methodology, empirical models were developed to predict the material removal rate (MRR), surface roughness (SR), and specific energy consumption (SEC). Models using JD and OFF as inputs, with contributions of over 68 % and 14 % respectively, defined the best machining conditions for MRR and SEC with prediction errors under 10 %. JU had a smaller effect on the outputs but helped define the “jump-down speed” parameter, which was later used to find the optimal values for the MRR and SEC.</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

Experimental study on the effect of the electrode jump motion on electrical discharge machining of Stavax ESR

  • 期刊论文

  • 发表日期：

    2026年4月9日

查看全文
  • German Herrera-Granados,
  • Hitoshi Komoto,
  • Jonny Herwan,
  • Ichiro Ogura,
  • Yoshiyuki Furukawa

摘要

Electrical discharge machining (EDM) is fundamental, considering its advantages in machining complex shapes in difficult-to-cut materials. Although EDM control parameters have been widely investigated, less research has focused on debris removal methods, such as the electrode jump motion (EJM). This paper analyzes the control parameters of the EJM, including halt time (OFF), jump-up distance (JU), and jump-down time (JD), in the machining of Stavax ESR. Using analysis of variance and surface response methodology, empirical models were developed to predict the material removal rate (MRR), surface roughness (SR), and specific energy consumption (SEC). Models using JD and OFF as inputs, with contributions of over 68 % and 14 % respectively, defined the best machining conditions for MRR and SEC with prediction errors under 10 %. JU had a smaller effect on the outputs but helped define the “jump-down speed” parameter, which was later used to find the optimal values for the MRR and SEC.