Application of BP Neural Network and PSO Methods for Process Design of Cold Rolling Steel
Abstract
Exploring the relationship between composition, process and mechanical properties of cold rolling steel is very important for steel production. The most direct ways is to directly put into actual production. According to the results of actual production, we can tell the composition and process and to obtain the property. But this method is expensive for steel companies. In this paper, we proposed a prediction model for predicting the mechanical properties of cold rolling steel. And based on the mechanical properties prediction model and particle swarm optimization, a process design method is proposed. This model can predict the material composition and process of the steel given the steel mechanical properties. The two models both achieved good results finally.
Keywords
Process design, Cold rolling steel, Particle swarm optimization, Neural Network
DOI
10.12783/dtetr/ecar2018/26342
10.12783/dtetr/ecar2018/26342
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