An Entropy-Based Neighborhood Rough Set and PSO-SVRM Model for Fatigue Life Prediction of Titanium Alloy Welded Joints

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. joint thickness and load model, the S-N sample data is compressed into a narrow band. In the present study, comprehensive and quantitative analysis of the influencing factors which affect the fatigue life of welded joints is lacking and the problem of low precision of fatigue life prediction of welded joints still exists.Thermodynamics entropy has been used for fatigue life prediction for a long time. For example, a damage metric based on the second law of thermodynamics and statistical mechanics was presented [8]. The proposed thermodynamic framework treated a solid body as a thermodynamic system and required that the entropy production be non-negative. A thermodynamic framework was presented for damage mechanics of solid materials [9]. It was believed that there was no need for physically meaningless empirical parameters to define a phenomenological damage potential surface or a Weibull function to trace damage evolution in solid continuum. 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]. The Shannon's differential entropies of both distributions of the strain in the loading direction and the first principal strain were employed at the tension peak of the cycles as measuring parameters of strain inhomogeneity [11]. A thermo-mechanical fatigue life prediction model based on the theory of damage mechanics was presented in [12]. The damage evolution, corresponding to the mate...

Soft computing methods for fatigue life estimation: A review of the current state and future trends

“…Furthermore, it is a simple structured and easily applicable method that analyses data relationships, determines the importance of qualifications and includes feature reduction. With feature reduction, a minimum feature subset is obtained by deleting the noisy or irrelevant features while maintaining classification accuracy 127 …”

“…Moghaddam et al, 69 Abdalla and Hawileh, 75 Gao et al, 76 Zhang et al 93 and Zou et al 127 Polynomial models Quadratic Salmalian et al, 14 Salmalian et al 16 and Canyurt 89 Qubic Canyurt 89 Exponential model Mohanty et al, 40 Mohanty, 45 Mohanty et al 58 and Chen et al 91 Conventional models Coffin-Manson Kong et al, 19 Zhang and Lin, 38 Abdalla and Hawileh, 75 Song et al 81 and Basan et al 135…”

“…With feature reduction, a minimum feature subset is obtained by deleting the noisy or irrelevant features while maintaining classification accuracy. 127 Yang et al 1 and Yang et al 39 applied data preprocessing using RST to compensate for missing data between data sets, delete invalid data and protect data effectively. Data preprocessing is the preprocessing of missing, inconsistent and dirty data between fatigue data to estimate fatigue life.…”

A New Hybrid Model for Underwater Acoustic Signal Prediction

“…e selection of penalty parameters and kernel parameters directly affects the prediction accuracy and generalization performance of SVR. An intelligent optimization algorithm was widely used in parameter optimization of SVR because of its good optimization performance [25,26]. Wang et al [27] used genetic algorithm (GA) to optimize the SVR model and applied it in the field of energy prediction.…”

Microstructure Control of Welded Joints of Dissimilar Titanium Alloys by Isothermal Forging

“…By this method, the microstructure to some extent can be controlled; however, the original large grains still cannot be broken. Thus, the welded joint sometimes becomes one of the weak links in the structural reliability of the product [ 15 ]. Recently, researchers have attempted to improve the welded joints by deformation.…”