Prediction of Springback in Metal Forming of Diaphragm of Automotive Horn Based on BPANN

Yan Qiu Zhang; Shu Yong Jiang; Yu Feng Zheng

doi:10.4028/www.scientific.net/AMR.139-141.594

Paper Titles

Application of Numerical Simulation on Cast-Steel Axle’s V-Method Foundry
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Research and Prospects of Laser Welding Technologies of Magnesium Alloys
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Gap Setting Model and Application in Plate Rolling Process
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The Research for Drawing Process of High-Speed Railway’s Track Template
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Prediction of Springback in Metal Forming of Diaphragm of Automotive Horn Based on BPANN
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Role of Material Properties in Improving Sheet Formability in SPIF Process
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Thermal Deformation Behavior of 1Cr12Ni3Mo2VNbN Alloy and Its Application in the Blade Forging
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Effects of Steel Coatings Microstructure on Weldability in Resistance Spot Welding of Galvannealed Steel Sheets
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Finite Element Simulation for the Stress of Cavity Die Wall in Tube Open-Die Cold Extrusion Process
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Prediction of Springback in Metal Forming of Diaphragm of Automotive Horn Based on BPANN

Abstract:

The spring steel strip 50CrVA which is cold rolled was applied to manufacture the diaphragm of the automotive horn by means of sheet metal forming. The combination of the experiments with back-propagation artificial neural network (BPANN) is used to solve the springback problem of the diaphragm. Experiments have shown that a 4-8-1 BPANN is able to predict the springback of the diaphragm successfully, and the network is able to model the relationship between the springback of the diaphragm and the process parameters rationally. BPANN simulation results and experimental ones have shown that the springback of the diaphragm is particularly influenced by such parameters as blank thickness, Young’s modulus, punch radius and yield ratio. Furthermore, the springback of the diaphragm decreases with the increase of blank thickness and Young’s modulus, but increases with the increase of punch radius and yield ratio.