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Process Optimization of the Cold-Rolled Ribbed Steel Using GA and RBF Neural Network

Bangsheng Xing, Changlong Du


To solve the problems of high costs and long cycle in experimental method for the process Optimization of the cold-rolled ribbed steel, we have proposed a new approach based on the genetic algorithms (GA) and radial basis function neural network (RBF). A model of process Optimization of the cold-rolled ribbed steel is constructed. According to the L16 (45) orthogonal table, the RBF network is trained using the orthogonal tests samples, the network after training is used to calculate the individual fitness in the process optimization model. The optimal combination of the cold rolling process parameters is searched using GA to meet the products performance requirements. The results show that the method can optimize the process parameters for cold-rolled ribbed steel rolling reliably, which provides a new technology optimization of rolling process in the actual production.


Cold-rolled ribbed steel bars, process optimization, Radial Basis Function Network, Genetic algorithm.

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