Abstract
In order to obtain comprehensive assessment of the factors influencing fatigue life and to further improve the accuracy of fatigue life prediction of welded joints, soft computing methods, including entropy-based neighborhood rough set reduction algorithm, the particle swarm optimization (PSO) algorithm and support vector regression machine (SVRM) are combined to construct a fatigue life prediction model of titanium alloy welded joints. By using an entropy-based neighborhood rough set reduction algorithm, the influencing factors of the fatigue life of titanium alloy welded joints such as joint type, plate thickness, etc. are analyzed and the reduction results are obtained. Fatigue characteristic domains are proposed and determined subsequently according to the reduction results. The PSO-SVRM model for fatigue life prediction of titanium alloy welded joints is established in the suggested fatigue characteristic domains. Experimental results show that by taking into account the impact of joint type, the PSO-SVRM model could better predict the fatigue life of titanium alloy welded joints. The PSO-SVRM model indicates the relationship between fatigue life and fatigue life influencing factors in multidimensional space compared with the conventional least-square S-N curve fitting method, it could predict the fatigue life of the titanium alloy welded joints more accurately thus helps to the reliability design of the structure.
Highlights
Welded components have been widely used in the engineering equipment field under static or dynamic load circumstances because of their advantages of good connectivity, light weight and ease of use
In order to obtain the comprehensive assessment of fatigue life influencing factors of the titanium alloy welded joints, to further reduce the dispersion level of S-N data samples and to improve the accuracy of fatigue life prediction of the welded joints, the concept of fatigue characteristics domain is proposed by using entropy based neighborhood rough set theory and the particle swarm optimization (PSO)-support vector regression machine (SVRM) model for fatigue life prediction of welded joints is established in this work
The SVRM model was established based on the best parameter values of C and σ to predict the fatigue life of the titanium alloy welded joints
Summary
Welded components have been widely used in the engineering equipment field under static or dynamic load circumstances because of their advantages of good connectivity, light weight and ease of use. A generalized theory of evolution based on the concept of tribo-fatigue entropy was proposed where tribo-fatigue entropy was determined by the processes of damage ability conditioned by thermodynamic and mechanical effects causing to the change of states of any systems [10]. From the perspective of information entropy, the principal author’s team has proposed an evaluation model of aluminum alloy welded joint low-cycle fatigue data based on information entropy [16]. In order to obtain the comprehensive assessment of fatigue life influencing factors of the titanium alloy welded joints, to further reduce the dispersion level of S-N data samples and to improve the accuracy of fatigue life prediction of the welded joints, the concept of fatigue characteristics domain is proposed by using entropy based neighborhood rough set theory and the PSO-SVRM model for fatigue life prediction of welded joints is established in this work
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