Artificial neural network application to microstructure design of Nb-Si alloy to improve ultimate tensile strength

作者：Liu, Guangxu; Jia, Lina^*; Kong, Bin; Feng, Shangbin; Zhang, Huarui; Zhang, Hu

来源：Materials Science and Engineering A-Structural Materials Properties Microstructure and Processing, 2017, 707: 452-458.

DOI：10.1016/j.msea.2017.09.039

• Nb-Si alloy
• Ultimate tensile strength
• Silicide
• Artificial neural network
• Modeling and prediction

In this paper, a feed forward neural network with back propagation artificial neural network (BP ANN) was developed to predict ultimate tensile strength (UTS) and optimize microstructure. The alloys were produced by directional solidification and heat treatment. The UTS was measured for ANN output. Five characteristic factors used for ANN input were abstracted and measured. As the result of this study, the ANN model with high accuracy and good generalization ability to predict UTS within the range of 343.5-1063.3 MPa was established and mutual verified with sensitivity analysis. Based on the optimized ANN model, a new way to design microstructure of Nb-Si alloy to obtain required UTS was proposed. With silicide design maps made by ANN model, the microstructure of the sample of 343.5 MPa was optimized and the UTS reached the target UTS (600 MPa) successfully.

• 出版日期2017-11-7
• 单位北京航空航天大学