In this paper, a cold forging process design method based on the induction of analytical knowledge is proposed. The analysis engine, which is a finite-element-based program, is used to analyze various multi-stage cold forging processes based on pre-defined process condition parameters and tooling geometry. According to the simulation results, a knowledge-acquisition procedure is instituted, i.e. a neural network model is constructed, in which the multi-layer network and the back-propagation algorithm are utilized to learn the training examples from the simulation results. In the last part of this paper, an industrial case study for the multi-stage cold forging process design of a low-carbon steel speaker tip is discussed. The optimal process condition parameters, such as the preform punch geometry and the preform punch stroke are decided, based on the requirement. of homogeneous plastic deformation of the cold-forged product. The proposed method is useful for the shop floor to decide the cold forging process parameters for producing a sound product within the required minimum quantity of the die set.
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