Optimization of Flash, Billet Dimensions and Friction Factor in Closed Die Cold Forging Process

Journal of Simulation and Analysis of Novel Technologies in Mechanical Engineering

مقاله 8، دوره 3، شماره 1، آذر 2010، صفحه 71-80   

نوع مقاله: Persian

نویسندگان

Mehdi Zohoor^{* 1}؛ Hossein Shahverdi²؛ Amin Tafakori³

¹Assistant Professor, Faculty of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran

²- Assistant Professor, Mechanical and Aerospace Engineering Department, Science and Research Branch, Islamic Azad University, Tehran, Iran

³M.Sc. Graduate Student, Faculty of Mechanical and Aerospace Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

چکیده

Three important parameters in designing a closed die for forging process are ratio of width to flash thickness, ratio of  height to billet diameter and the friction factor. In this paper the influences of these parameters on the required force for the forging and percentage of die filling were investigated. It was found that by controlling the flash dimension, the material loss is reduced and the percentage of die filling is increased. Also, an experimental model was simulated and analyzed by finite element method. To validate the numerical results obtained by this research, value of gained force from finite element method was compared with the obtained experimental results. In order to coordinate and connect between the mentioned parameters and obtain a performance function, a two layer neural network was used. Finally, by using neural network and genetic algorithm, the optimum sets of parameters which minimized the force and maximized the percentage of die filling were found. These values were compared with the experimental results of other researchers. The genetic algorithm has good correlation with the experimental method as well as it has presented acceptable estimation for effective parameters in the forging process.

کلیدواژه‌ها

forging؛ Billet؛ Flash؛ Closed die؛ FEM Method؛ Neural network؛ Genetic Algorithm

مراجع

اردیبهشت ۱۳۸۵.

[2] Sheridan S. A., Forging Design HandBook, American Society For Metals, MetaPark, Ohio, 1972.

[3]Ranatunga V. R., Gunasekera J. S.,Use of UBET For Design Of Flash Gap in Closed Die Forging, Journal of Materials Processing Technology, Vol. 111, 2001, pp. 107-112.

[4] Tavangar R., Taheri A. K. Prediction of Optimum Flash Dimention in Axisymmetric Closed Die Forging, Second. Mech. Int. Conf., Shiraz University, May 1996.

[5]Saniee F., Jaafari M., Analytical, numerical and experimental analyses of  the close die forging, Journal of Materials Processing Technology, 2002, pp. 334-340.

[6]  www.matweb.com

[7]Rumelhart D. E., Hinton G. E., Williams R.J., Learning  internal representation by error  propagation, Parallel Distributed Processing, Vol. 1, 1986,  pp. 318–362 .

[8] Holland J. H., Genetic Algoritm Scientific American” ,ASM Intl,  July 1992, pp 44-50.

[10] Gen M., Cheng R., Genetic Algoritm and Engineering Design, ASM Intl, 1997.

[9]Hagan M. T., Menhaj M., Training feed forward networks  with the marquardt algorithm, IEEE Transactions on Neural Network, 1994, pp. 989-993.