Prediction of CADI Chemical Composition and Heat Treatment Parameters using a BPNN Optimized with the Genetic Algorithm

2018 International Conference on Construction, Aviation and Environmental Engineering

生态环境科学;生物科学

Wang, Haiquan^1 ; Li, Zesheng^2 ; Ma, Li^1 ; Liang, Liang^3 ; Wu, Gangzhou^4 ; Zhang, Xixing^5

College of Mechanical and Electrical Engineering, Guangdong University of Petrochemical Technology, Maoming, Guangdong

525000, China^1

College of Chemical Technology, Guangdong University of Petrochemical Technology, Maoming, Guangdong

525000, China^2

Telecommunications College, Guangdong University of Petrochemical Technology, Maoming, Guangdong

525000, China^3

College of Science, Guangdong University of Petrochemical Technology, Maoming, Guangdong

525000, China^4

Black Foundry of HARBIN DONGAN AUTO ENGINE CO., LTD, Harbin, Heilongjiang

150000, China^5

关键词: Austempered ductile irons;    Back-propagation neural networks;    Chemical compositions;    Domestic production;    Heat treatment parameters;    Industrial datum;    Influencing parameters;    Process prediction;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/233/5/052022/pdf
DOI  :  10.1088/1755-1315/233/5/052022

来源: IOP

【 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.