Abstract
In order to reduce the dispersion level of fatigue test samples of aluminum alloy welded joints and further improve the fatigue life prediction accuracy, the concept of fatigue characteristic domain is proposed. Based on the attribute reduction of neighborhood rough set, the division method of fatigue characteristic domain is determined. According to the attribute reduction, the key fatigue life influencing factor set of welded joint is used to divide all fatigue test samples into several sub-domain sets, and each sub-domain set corresponds to a fatigue character sub-field. Support vector machine has the characteristics of simple calculation, perfect theoretical foundation and suitable for small sample problems. Based on its characteristics, an improved support vector machine model for fatigue life prediction of aluminum alloy welded joints is established in the determined fatigue characteristics domain. The experimental results show that compared with the least squares fitting method, the support vector machine model has stronger anti-noise ability and higher prediction accuracy.
This project is supported by Liaoning Provincial Education Department Project (JDL2017025) and the Open Project Program of Sichuan Provincial Key Lab of Process Equipment and Control (GK201815).
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Zou, L., Li, H., Jiang, W. (2020). An Intelligent Fatigue Life Prediction Method for Aluminum Welded Joints Based on Fatigue Characteristics Domain. In: Tan, J. (eds) Advances in Mechanical Design. ICMD 2019. Mechanisms and Machine Science, vol 77. Springer, Singapore. https://doi.org/10.1007/978-981-32-9941-2_81
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