4.6 Article

Investigating the energy distribution of workpiece and optimizing process parameters during the EDM of Al6061, Inconel718, and SKD11

期刊

出版社

SPRINGER LONDON LTD
DOI: 10.1007/s00170-017-0488-6

关键词

EDM; Energy distribution; Process parameters optimization; Thermal model

资金

  1. Natural Science Foundation of Henan Province, China [16230 0410039]
  2. PhD early development program of Zhengzhou University of Light Industry [2014BSJJ024]

向作者/读者索取更多资源

In the electrical discharge machining (EDM) process, the aspects of the cutting performance such as material removal rate (MRR), surface roughness, crater geometry, and tool wear ratio are affected by the energy distribution. The difference in energy distribution during the EDM processing of Al6061, Inconel718, and SKD11 has been rarely studied. However, energy distribution is one of the most important parameters utilized in most existing models of the EDM process. In this paper, the energy distribution that occurs while processing these three materials is investigated by an experimental study under different EDM parameters, including current and pulse duration. Then, the relationship between the energy distribution and the specific discharge energy (SDE) is investigated. The results demonstrate that the discharge energy transferred to the above three kind of workpiece is small, and the fraction of energy distributed into the workpiece varies with discharge current and pulse duration form 7.998 to 12.005%, 2.879 to 3.485% and 2.976 to 3.716% for Al6061, Inconel718, and SKD11, respectively. These findings are consistent with previous studies. In addition, the optimization of the process parameters is investigated for these three materials, considering the MRR and the discharge energy efficiency simultaneously. This findings presented by this paper may be further used in existing thermal-physical models to improve the technological performance of such models.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据