Artificial Intelligence Optimization and Experimental Study of Auto Panel Stamping Process

Lin Zhou; Xiao Min Cheng

doi:10.4028/www.scientific.net/AMM.55-57.1794

Paper Titles

The Effects of Nano Pt/Carbon Black Compound Coating on the Electro-Catalysis Properties of the Graphite Electrode
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Comparison of KP Pulping Properties between Heartwood and Sapwood of Cedrus deodara (Roxb.) G. Don
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The Influence of Micro-Arc Oxidation on Corrosive-Wear Behavior of Magnesium Alloy
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A New Hybrid Optimization Algorithm and its Application in Job Shop Scheduling
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Artificial Intelligence Optimization and Experimental Study of Auto Panel Stamping Process
p.1794

The Study on Operation Charateristic of Double-in and Double-Out Ball Mill Based on Data Mining
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Study on the Fault Diagnosis of Turbine Based on Support Vector Machine
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The Study of Risk Assessment on the Ship Oil Spill Diffuseness
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The Research on Maintain Efficiency of Industrial Maintenance System Based on Markov Analysis Model
p.1813

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 55-57Artificial Intelligence Optimization and...

Artificial Intelligence Optimization and Experimental Study of Auto Panel Stamping Process

Abstract:

Auto panel stamping is a complicated plastic deformation process with geometry nonlinear, material nonlinearity and numerous process parameters. The stamping process of a typical auto panel wheel wrap was studied by artificial intelligent optimization and physical experiment. The prediction model of object function was established using artificial neural network. In object function, blank-holder force, drawbead height and fillet radius were selected as the optimized variables and prevention of rupture was considered as the optimization objective. Process parameters optimization was performed with genetic algorithm. The optimized process parameters were used to guide die design and testing, and the result of wheel wrap stamping showed that the forming quality was obviously improved. So the process optimization based on artificial neural network and genetic algorithm is feasible and efficient for auto panel stamping.