期刊名称：IOP Conference Series: Earth and Environmental Science
印刷版ISSN：1755-1307
电子版ISSN：1755-1315
出版年度：2019
卷号：233
期号：5
页码：052022
DOI：10.1088/1755-1315/233/5/052022
出版社：IOP Publishing
摘要：Due to the increasing application of the Carbidic Austempered Ductile Iron (CADI) with carbides, it is of great significance to predict the CADI chemical composition and heat treatment parameters to meet the requirements of process prediction in the complete design process of CADI parts. This study combines a backpropagation neural network (BPNN) and the genetic algorithm (GA). Based on the domestic production data, six key influencing parameters are selected to establish the BPNN prediction model. The prediction results of the non-optimized BPNN and the BPNN optimized using the genetic algorithm (GA-BP) are compared with the real industrial data. The results show that the optimized prediction model can meet the design requirements for the accuracy and stability.
