Prediction of composite ceramics' fracture toughness based on BP neural network

作     者：Sun, De-Ming Liu, Li-Hong Shi, Huai-Wei Ma, Xiang-Yang Zhang, Chang-Qiang Xu, Chong-Hai Lu, Xiao-Yang 

作者机构：School of Materials Science and Engineering Shandong University Ji'nan 250061 China Dept. of Civil Engineering Shandong Institute of Architecture Engineering Ji'nan 250014 China Dept. of Mechanical Engineering Shandong Institute of Light Industry Ji'nan 250100 China 

出 版 物：《Cailiao Kexue yu Gongyi/Material Science and Technology》 (Cailiao Kexue yu Gongyi)

年 卷 期：2005年第13卷第5期

页      面：456-458页

核心收录：

主　　题：Ceramic materials 

摘      要：In order to shorten the experimental procedure of ceramic materials design effectively, a fracture toughness predicting system of advanced ceramic composites based on BP neural network was developed, which can precisely predict the relationship between material composition and the fracture toughness through self-training with the present data, and can perfectly aid the ceramic materials design. This system has friendly interfaces, extensive application, good operating feasibility and reliability examined with the present Al2O3/SiC/(W, Ti)C ceramics.