Experimental and numerical investigation of the deep drawing process for an automobile panel and prediction of appropriate amount of parameters by multi-layer neural network

• Autorzy: Najafabadi S. S. , Anaraki A. T. , Moradi M.

Identyfikatory

DOI

10.15632/jtam-pl.55.2.707

• Języki publikacji: EN

Abstrakty

EN

In this paper, the deep drawing process of an automobile panel in order to select the appropriate amount of parameters has been investigated. The parameters include friction between the blank and die, blank width and length, blank thickness and gap between the blank and blank-holder. A multi-layer artificial neural network (ANN) trained by finite element analyses (FEA) is applied in order to improve forming parameters and achieve a better quality. As the FEA results are used to train the ANN, the FEA results have been verified by three experiments. Finally, an appropriate amount of each parameter is predicted by the trained ANN and a FEA has been done based on the ANN prediction to evaluate the accuracy of the trained ANN. Moreover, it is shown that the ANN could predict results within a 10 percent error. In addition, the proposed method for prediction of the appropriate parameters (ANN) is confirmed by comparing with the Taguchi design of experiment prediction. It is also shown that the model obtained by the former method has lower errors than the latter one. In this study, the Taguchi model is used to evaluate the effect of parameters on tearing and wrinkling. Based on the Taguchi design of experiment, while the blank length is the most effective parameter on tearing, the maximum height of wrinkles on flanged parts mainly depends on the blank thickness.

Słowa kluczowe

EN

deep drawing; finite element analysis (FEA); multi-layer artificial neural network (ANN); Taguchi design

• Wydawca: Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
• Czasopismo: Journal of Theoretical and Applied Mechanics
• Rocznik: 2017
• Tom: Vol. 55 nr 2
• Strony: 707--718
• Opis fizyczny: Bibliogr. 19 poz., rys., tab.

Twórcy

autor

Najafabadi S. S.

• Young Researchers and Elite Club, Najafabad Branch, Islamic Azad University, Najafabad, Iran

autor

Anaraki A. T.

• Department of Mechanical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran

autor

Moradi M.

• Department of Mechanical Engineering, Isfahan University of Technology, Isfahan, Iran

Bibliografia

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• 15. Singh D., Yousefi R., Boroushaki M., 2011, Identification of optimum parameters of deep drawing of a cylindrical workpiece using neural network and genetic algorithm, World Academy of Science, Engineering and Technology, 78, 211-217
• 16. Watiti V.B., Labeas G.N., 2010, Finite element optimization of deep drawing process forming parameters for magnesium alloys, International Journal of Material Forming, 3, 1, 97-100
• 17. Wifi A., Abdelmaguid T., 2012, Towards an optimized process planning of multistage deep drawing: an overview, Journal of Achievements in Materials and Manufacturing Engineering, 55, 1, 7-17
• 18. Yagami T., Manabe K., Yamauchi Y., 2007, Effect of alternating blank holder motion of drawing and wrinkle elimination on deep-drawability, Journal of Materials Processing Technology, 187, 187-191
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Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017)