Mechanical Property Prediction of Commercially Pure Titanium Welds with Artificial Neural Network

材料科学技术（英文）, 2005, 21(3): 403-407.

^1, , ^2, , ^3,

1.State Key Laboratory of Advanced Welding Production Technology, Harbin Institute of Technology, Harbin 150001, China

2.Department of Materials Science and Metallurgy, University of Cambridge, Cambridge CB2 3QZ, UK

3.Department of Materials Science and Metallurgy, University of Cambridge, Cambridge CB2 3QZ, UK

基金项目: 教育部留学回国人员科研启动基金

Mechanical Property Prediction of Commercially Pure Titanium Welds with Artificial Neural Network

Factors that affect weld mechanical properties of commercially pure titanium have been investigated using artificial neural networks. Input data were obtained from mechanical testing of single-pass, autogenous welds, and neural network models were used to predict the ultimate tensile strength, yield strength, elongation, reduction of area,Vickers hardness and Rockwell B hardness. The results show that both oxygen and nitrogen have the most significant effects on the strength while hydrogen has the least effect over the range investigated. Predictions of the mechanical properties are shown and agree well with those obtained using the 'oxygen equivalent' (OE) equations.

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Mechanical Property Prediction of Commercially Pure Titanium Welds with Artificial Neural Network

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