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

Subject Areas : Journal of Simulation and Analysis of Novel Technologies in Mechanical Engineering

مسعود عظیمی ¹ , امین کلاه دوز ² , سید علی افتخاری ³

1 - MSc Student, Department of Mechanical engineering, Islamic Azad University, Khomeinishahr Branch, Isfahan/Khomeinishahr, Iran
2 - استادیار، دانشکده مهندسی مکانیک، دانشگاه آزاد اسلامی، واحد خمینی شهر
3 - Assistant Professor, Young Researchers and Elite Club, Islamic Azad University, Khomeinishahr Branch, Isfahan/Khomeinishahr, Iran

Received: 2017-02-26 Accepted : 2017-02-26 Published : 2016-04-20

Keywords: Genetic Algorithm, Neural network, Electrical discharge machining, Taguchi, Optimum determinant Optimization,

Abstract :

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

References:

[1] Kalpakjian S, Manufacturing Engineering and Technology, 1995, Addison-Wesley.

[2] Sadr P., Kolahdooz A., Eftekhari S.A., The effect of Electrical Discharge Machining parameters on alloy DIN1.2080 using the taguchi method and optimal determinat, Journal of Solid Mechanics in Engineering, Vol. 8, No. 2, 2015, pp. 71-89, (In Persian).

[3] Uhlmann E., Domingosb D.C., Development and optimization of the die-sinking EDM technology for machining the nickel-based alloy MAR-M247 for turbine components, Procedia CIRP, Vol. 6, 2013, pp. 180–185.

[4] Ayestaa, Izquierdob B., Influence of EDM parameters on slot machining in C1023 aeronautical alloy, Procedia CIRP, Vol. 6, 2013, pp. 129–134.

[5] Gopakalannan S., Sinthelevan T., Modeling and Optimization of EDM Process parameter on Machining of AL 7075-B4 MMC using RSM, Procedia Engineering, Vol. 38, 2012, pp. 685 – 690.

[6] Clijsters S., Liu K., EDM technology and strategy development for the manufacturing of complex parts in SiSiC, Journal of Materials Processing Technology, Vol. 210, 2010, pp. 631–641.

[7] Tzeng Y.F., Development of a flexible high-speed EDM technology with geometrical transform optimization, Journal of materials processing technology, Vol. 203, 2008, pp. 355–364.

[8] Rajmohan T., Prubho R., Optimization of Machining parameter in EDM of 304 Stainless Steel, Procedia Engineering, Vol. 38, 2012, pp. 1030 – 1036.

[9] Zarepour H., Fadaei Tehrani A., Statistical analysis on electrode wear in EDM of tool steel DIN 1.2714 used in forging dies, Journal of Materials Processing Technology, Vol. 187–188,  2007, pp. 708–714.

[10] Tzeng Y.F., Chen F., Multi-objective optimization of high-speed electrical discharge machining process using a Taguchi fuzzy-based approach, Materials and Design, Vol 28, 2007, pp. 1159–1168.

[11] صابونی، حمیدرضا، 1391، طرح پژوهشی، بررسی پارامترهای ماشین‌کاری EDM با ابزار گرافیتی بر روی خواص مکانیکی آلیاژهای حافظ دار NITI، دانشگاه آزاد اسلامی خمینی‌شهر.

[12] عندلیب، مرتضی، 1392، پایان‌نامه، ماشین‌کاری سوپر آلیاژ اینکونل 718 به روش تخلیه الکتریکی و بررسی تأثیر پارامترهای تنظیمی در کیفیت سطح و نرخ براده‌برداری قطعات تولیدی، پایان‌نامه ﮐﺎرﺷﻨﺎﺳﯽ ارﺷﺪ، ﮔﺮوه ﻣﮑﺎﻧﯿﮏ داﻧﺸﮕﺎه ﻓﺮدوﺳﯽ ﻣﺸﻬﺪ.

[13] Joshi S.N., Pandeb S.S., Intelligent process modeling and optimization of die-sinking electric discharge machining, Applied Soft Computing, Vol. 11, 2011, pp. 2743–2755.

[14] Tsai K.M., Wang P.J., Predictions on surface finish in electrical discharge machining based upon neural network models, International Journal of Machine Tool Manufacturing, Vol. 41, 2001, pp. 1385–1403.

[15] Rao G.K.M, Ganardhana G.R., Rao D.H., Rao M.S., Development of hybrid model and optimization of surface roughness in electric discharge machining using artificial neural networks and genetic algorithm, Journal of Material Process Technology, Vol. 209, 2009, pp. 1512–1520.

[16] Zabah I., optimization of EDM machining process using genetic algorithm, The first modern writing area in computer engineering and information lat, Vol. 56, 2011, pp. 1-28.

                .2)/(2015 ,‪  [17] www.esttoolsteel.com

[18] کیا،سید مصطفی، شبکه‌های عصبی در متلب، انتشارات دانشگاهی کیان،1390، چاپ دوم.                          

[19] کیا،سید مصطفی، الگوریتم‌های ژنتیک در متلب، انتشارات دانشگاهی کیان، (1391)، چاپ سوم.