The isothermal compression tests of the 2219 Al alloy were conducted at the temperature and the strain rate ranges of 623–773 K and 0.01–10 s⁻¹, respectively, and the deformed microstructures were observed. The flow curves of the 2219 Al alloy obtained show that flow stress decreases with the increase in temperature and/or the decrease in strain rate. The physically based constitutive model is applied to describe the flow behavior during hot deformation. In this model, Young’s modulus and lattice diffusion coefficient are temperature-dependent, and the creep exponent is regarded as a variable. The predicted values calculated by the constitutive model are in good agreement with the experimental results. In addition, it is confirmed that the main softening mechanism of the 2219 Al alloy during hot deformation is dynamic recovery and incomplete continuous dynamic recrystallization (CDRX) by the analysis of electron backscattered diffraction (EBSD) micrographs. Moreover, CDRX can readily occur under the condition of high temperatures, low strain rates, and large strains. Meanwhile, the recrystallization grain size will also be larger.

For a rolling bearing fault that has nonlinearity and nonstationary characteristics, it is difficult to identify the fault category. A rolling bearing clustering fault diagnosis method based on ensemble empirical mode decomposition (EEMD), permutation entropy (PE), linear discriminant analysis (LDA), and the Gath–Geva (GG) clustering algorithm is proposed. Firstly, we decompose the vibration signal using EEMD, and several inherent modal components are obtained. Then, the permutation entropy values of each modal component are calculated to get the entropy feature vector, and the entropy feature vector is reduced by the LDA method to be used as the input of the clustering algorithm. The data experiments show that the proposed fault diagnosis method can obtain satisfactory clustering indicators. It implies that compared with other mode combination methods, the fault identification method proposed in this study has the advantage of better intra-class compactness of clustering results.

Finite element (FE) analysis of welding residual stress and deformation is one of the essential stages in the manufacturing process of mechanical structures and parts. It aids in reducing the production cost, minimizing errors, and optimizing the manufactured component. This paper presents a numerical prediction of residual stress and deformation induced by two-pass TIG welding of Al 2219 plates. The FE model was developed using ABAQUS and FORTRAN packages, Goldak’s heat source model was implemented by coding the nonuniform distributed flux (DFLUX) in user subroutine to represent the ellipsoidal moving weld torch, having front and rear power density distribution. Radiation and convection heat losses were taken into account. The mechanical boundary condition was applied to prevent the model from rotation and displacement in all directions while allowing material deformation. The FE model was experimentally validated and the compared results show good agreement with average variations of 18.8% and 17.4% in residual stresses and deformation, respectively.

Rolling bearing is an important part of mechanical equipment. Timely detection of rolling bearing fault is one of the important factors to ensure the safe operation of equipment. In order to diagnose rolling bearing fault accurately, a novel rolling bearing fault diagnosis method based on adaptive feature selection and clustering is proposed. Firstly, the vibration signal obtained from rolling bearing is decomposed by ensemble empirical mode decomposition(EEMD) to extract as much important information as possible. Feature extraction is performed for each intrinsic mode function(IMF) component and the original signal, and finally 240 features are obtained. And the Chi-square Test algorithm, Variance-Relief-F algorithm, and hierarchical clustering algorithm are used to filter all the features in layers to obtain the optimal features. Then the optimal features are input into fuzzy c-means(FCM) clustering to complete fault diagnosis. After the fault diagnosis analysis of four groups of vibration signal data, it is found that whether the characteristic number parameters are set based on engineering experience or adaptive feature selection, good fault diagnosis results are obtained. Furthermore, through comparative experiments, the fault diagnosis effect of the method based on adaptive parameter setting is better. The results indicate that the proposed adaptive parameter fault diagnosis method is feasible and effective for rolling bearing fault diagnosis.