Optimization of Material Removal Rate in Electrical Discharge Machining Alloy on DIN1.2080 with the Neural Network and Genetic Algorithm

Journal of Simulation and Analysis of Novel Technologies in Mechanical Engineering

مقاله 6، دوره 9، شماره 1 - شماره پیاپی 27، مرداد 2016، صفحه 77-92   

نوع مقاله: Persian

نویسندگان

Masoud Azimi¹؛ Amin Kolahdooz^{* 2}؛ Seyyed Ali Eftekhari²

¹MSc Student, Department of Mechanical engineering, Islamic Azad University, Khomeinishahr Branch, Isfahan/Khomeinishahr, Iran

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

چکیده

Electrical discharge machining process is one of the most Applicable methods in Non-traditional machining for Machining chip in Conduct electricity Piece that reaching to the Pieces that have good quality and high rate of machining chip is very important. Due to the rapid and widespread use of alloy DIN1.2080 in different industry such as Molding, lathe tools, reamer, broaching, cutting guillotine, etc. Reaching to optimum condition of machining is very important. Therefore the main aim in this article is to consider the effect of input parameter such voltage, Current strength, on-time pulse and off-time pulse on the machining chip rate and optimizing this in the electrical discharge machining for alloy DIN1.2080. 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. Optimum parameters in this experiment in this condition are obtained under Current strength 20 ampere, 160 volt, on-time pulse 100 micro second and off-time pulse 12 micro second that is obtained 0.063 cm³/min as rate of machining chip. After doing experiment, surveying the level of error and its accuracy are evaluated. According to the obtained error value that is about 5.18%, used method is evaluated for genetic algorithm

کلیدواژه‌ها

Electrical discharge machining؛ Taguchi؛ Genetic Algorithm؛ Neural network؛ Optimum determinant Optimization

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