Prediction on Tribology Behavior of PEEK Composites Using Back Propagation Artificial Neural Networks

摘要：

A multi-layer back propagation artificial neural network (BPNN) was used to predict the friction coefficient and the specific wear rate of short fiber reinforced polyetheretherketone (PEEK) composites. The best network structure for property forecast is decided as 5-|29|1-2. The train function is trainlm, and the transfer functions from input to hidden layers and from hidden to output layers are logsig and purelin, respectively. For the network of ingredient forecast, the best network structure is 2-|300|1-|150|2-4. The train function is trainscg, and logsig, tansig are the transfer functions from input to hidden layers and within the hidden layers, respectively. Purelin is transfer function between hidden and output layers. The results show that ANN techniques can effectively be used to predict the tribology behavior and the components of composites.

关键词： back propagation artificial neural network (BPNN) artificial neural networks (ANN) PEEK composite tribology behavior component (key words)

作者: Hua Fu Li Fu Jin-ge Liu Xian-Wu Zhang

作者单位: Institute of Materials Science and Engineering, Shijiazhuang Railway Institute, 050043, Shijiazhuang Institute of information Science and Engineering, Hebei University of Science and Technology, Shijia Institute of Materials Science and Engineering, Shijiazhuang Railway Institute, Shijiazhuang, China The 16th Department of Professional, China Electronics Group the 13th Research Institute, Shijiazhua

会议类型: 国际会议

会议名称: 2009 WASE International Conference on Information Engineering(2009年国际信息工程会议)(ICIE 2009)

会议地点: 太原

会议语种:英文

页码: 275-277

在线出版日期: 2009-07-10（万方平台首次上网日期，不代表论文的发表时间）