Technical Brief

Study on the Generalized Holo-Factors Mathematical Model of Dimension-Error and Shape-Error for Sheet Metal in Stamping Based on the Back Propagation (BP) Neural Network

[+] Author and Article Information
Lizhi Gu

College of Mechanical Engineering and Automation, Huaqiao University,
Xiamen 361021, China
e-mail: gulizhi888@163.com

Tianqing Zheng

College of Mechanical Engineering and Automation, Huaqiao University,
Xiamen 361021, China
e-mail: ztq386@163.com

Manuscript received December 12, 2014; final manuscript received March 19, 2016; published online April 29, 2016. Assoc. Editor: Gracious Ngaile.

J. Manuf. Sci. Eng 138(6), 064502 (Apr 29, 2016) (3 pages) Paper No: MANU-14-1671; doi: 10.1115/1.4033156 History: Received December 12, 2014; Revised March 19, 2016

Precision improvement in sheet metal stamping has been the concern that the stamping researchers have engaged in. In order to improve the forming precision of sheet metal in stamping, this paper devoted to establish the generalized holo-factors mathematical model of dimension-error and shape-error for sheet metal in stamping based on BP neural network. Factors influencing the forming precision of stamping sheet metal were divided, altogether ten factors, and the generalized holo-factors mathematical model of dimension-error and shape-error for sheet metal in stamping was established using the back-propagation algorithm of error based on BP neural network. The undetermined coefficients of the model previously established were soluble according to the simulation data of sheet punching combined with the specific shape based on the BP neural network. With this mathematical model, the forecast data compared with the validate data could be obtained, so as to verify the fine practicability that the previously established mathematical model had, and then, it was shown that the generalized holo-factors mathematical model of size error and shape-error had fine practicality and versatility. Based on the generalized holo-factors mathematical model of error exemplified by the cylindrical parts, a group of process parameters could be selected, in which forming thickness was between 0.713 mm and 1.335 mm, major strain was between 0.085 and 0.519, and minor strain was between −0.596 and 0.319 from the generalized holo-factors mathematical model prediction, at the same time, the forming thickness, the major strain, and the minor strain were in good condition.

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Grahic Jump Location
Fig. 2

Formed part in experiment

Grahic Jump Location
Fig. 3

Comparison between the experimental values of wall thickness distribution and predicting value of the holo-factors mathematical model




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