Abstract
By optimizing the process parameters, we can reduce the die trial time, lower the production cost and improve the forming quality of the stamped parts. Taking cistern forming as an example, a computer aided engineering model was established to reduce the maximum thinning rate. The back propagation (BP) neural network was trained by combining the partial factor test method with the numerical simulation of stamping process, using the friction coefficient, die clearance, sheet thickness, binder force and the drag coefficient of the draw bead obtained from the simulation experiments as input values and the maximum thinning rate as output values. Particle swarm optimization (PSO) was used to optimize the stamping process parameters. The conclusion has guiding significance for stamping process design.
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