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Proceedings Paper

Modeling and optimizing electrodischarge machine process (EDM) with an approach based on genetic algorithm
Author(s): Iman Zabbah
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Paper Abstract

Electro Discharge Machine (EDM) is the commonest untraditional method of production for forming metals and the Non-Oxide ceramics. The increase of smoothness, the increase of the remove of filings, and also the decrease of proportional erosion tool has an important role in this machining. That is directly related to the choosing of input parameters.The complicated and non-linear nature of EDM has made the process impossible with usual and classic method. So far, some methods have been used based on intelligence to optimize this process. At the top of them we can mention artificial neural network that has modelled the process as a black box. The problem of this kind of machining is seen when a workpiece is composited of the collection of carbon-based materials such as silicon carbide. In this article, besides using the new method of mono-pulse technical of EDM, we design a fuzzy neural network and model it. Then the genetic algorithm is used to find the optimal inputs of machine. In our research, workpiece is a Non-Oxide metal called silicon carbide. That makes the control process more difficult. At last, the results are compared with the previous methods.

Paper Details

Date Published: 13 January 2012
PDF: 7 pages
Proc. SPIE 8349, Fourth International Conference on Machine Vision (ICMV 2011): Machine Vision, Image Processing, and Pattern Analysis, 83493C (13 January 2012); doi: 10.1117/12.921077
Show Author Affiliations
Iman Zabbah, Islamic Azad Univ. (Iran, Islamic Republic of)


Published in SPIE Proceedings Vol. 8349:
Fourth International Conference on Machine Vision (ICMV 2011): Machine Vision, Image Processing, and Pattern Analysis
Zhu Zeng; Yuting Li, Editor(s)

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