An Optimization on the DIN1.2080 Alloy in the Electrical Discharge Machining Process Using ANN and GA

Journal of Modern Processes in Manufacturing and Production

مقاله 4، دوره 6، شماره 1، اردیبهشت 2017، صفحه 33-47   

نوع مقاله: Research Paper

نویسندگان

Masoud Azimi¹؛ Amin Kolahdooz²؛ Seyyed Ali Eftekhari²

¹Department of Mechanical Engineering, Khomeinishahr Branch, Islamic Azad University, Isfahan, Iran

²Young Researchers and Elite Club, Khomeinishahr Branch, Islamic Azad University, Isfahan, Iran

چکیده

Electrical Discharge Machining (EDM) process is one of the most widely used methods for machining. This method is used to form parts that conduct electricity. This method of machining has used for hard materials and therefore selects the correct values of parameters which are so effective on the quality machining of parts. Reaching to optimum condition of the DIN1.2080 alloy (D3) machining is very important due to the rapid and widespread use of different industry such as Molding, lathe tools reamer, broaching, cutting guillotine and etc. Therefore the purpose of this study is to consider the effect of the inlet parameters such as current, voltage, pulse on time and pulse off time on the machining chip rate and optimize the inlet parameters for D3 alloy. So to reach better result after doing some experiments to predict and optimize the rate of removing chip, neural network method and genetic algorithm are used. Then optimizing input parameters to maximize the rate of removing chip are performed. In this condition by decreasing time, the product cost is decreased. In this condition, the optimum parameters are obtained under the current of 20 (A), 160 (V), pulse on time of 100 (ms) and pulse off time of12 (ms). At this condition, the rate of machining chip is obtained 0.063 (cm3/min). Also, surveying the level of error and its accuracy are evaluated. According to the obtained error value that is about 5.18%, the used method is evaluated suitable for genetic algorithm.

کلیدواژه‌ها

EDM؛ Taguchi method؛ Optimum determining؛ optimization؛ Genetic algorithm؛ ANN