Modeling and optimizing electrodischarge machine process (EDM) with an approach based on genetic algorithm

Iman Zabbah

doi:10.1117/12.921077

13 January 2012 Modeling and optimizing electrodischarge machine process (EDM) with an approach based on genetic algorithm

Iman Zabbah

Author Affiliations +

Proceedings Volume 8349, Fourth International Conference on Machine Vision (ICMV 2011): Machine Vision, Image Processing, and Pattern Analysis; 83493C (2012) https://doi.org/10.1117/12.921077
Event: Fourth International Conference on Machine Vision (ICMV 11), 2011, Singapore, Singapore

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.

Citation Download Citation

Iman Zabbah "Modeling and optimizing electrodischarge machine process (EDM) with an approach based on genetic algorithm", Proc. SPIE 8349, Fourth International Conference on Machine Vision (ICMV 2011): Machine Vision, Image Processing, and Pattern Analysis, 83493C (13 January 2012); https://doi.org/10.1117/12.921077

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