Abstract
The blueprint for an intelligent control system of cap-shape bending has been advanced in this paper using neural network technology, aiming at an accurate control of bending springback, the prominent problem during the forming process for the cap-shape bending of sheet metal. The feed-forward neural network of real-time identification for material performance parameters and the friction coefficient have been established. The neural network identifies the parameters for real-time needed material performance, which utilizes the measurability of the physical quantities, and predicts the parameters for optimum technology, so a satisfied accuracy of convergence has been achieved. The intelligent control experimentation system of cap-shape bending has been established, the validity of which has been tested for four kinds of materials. The result of the tests proves the feasibility of the blueprint of the intelligent control system.
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