TC4 titanium alloy has excellent comprehensive properties. Due to its light weight, high specific strength, and good corrosion resistance, it is widely used in aerospace, military defense, and other fields. Given that titanium alloy components are often fractured by impact loads during service, studying the fracture behavior and damage mechanism of TC4 titanium alloy is of great significance. In this study, the Johnson–Cook failure model parameters of TC4 titanium alloy were obtained via tensile tests at room temperature. The mechanical behavior of TC4 titanium alloy during the tensile process was determined by simulating the sheet tensile process with the finite element software ABAQUS. The macroscopic and microscopic morphologies of tensile fracture were analyzed to study the deformation mechanism of the TC4 titanium alloy sheet. The results provide a theoretical basis for predicting the fracture behavior of TC4 titanium alloy under tensile stress.

The plastic forming process involves many influencing factors and has some inevitable disturbance factors, rendering the multi-objective collaborative optimization difficult. With the rapid development of big data and artificial intelligence (AI) technology, intelligent process optimization has become one of the critical technologies for plastic forming. This paper elaborated on the research progress on the intelligent optimization of plastic forming and the data-driven process planning and decision-making system in plastic forming process optimization. The development trend in intelligent optimization of the plastic forming process was researched. This review showed that the intelligent optimization algorithm has great potential in controlling forming quality, microstructure, and performance in plastic forming. It is a general trend to develop an intelligent optimization model of the plastic forming process with high integration, versatility, and high performance. Future research will take the data-driven expert system and digital twin system as the carrier, integrate the optimization algorithm and model, and realize the multi-scale, high-precision, high-efficiency, and real-time optimization of the plastic forming process.

Low-carbon steel pipelines are frequently used as transport pipelines for various media. As the pipeline transport industry continues to develop in extreme directions, such as high efficiency, long life, and large pipe diameters, the issue of pipeline reliability is becoming increasingly prominent. This study selected Q235 steel, a typical material for low-carbon steel pipelines, as the research object. In accordance with the pipeline service environment and the accelerated corrosion environment test spectrum, cyclic salt spray accelerated corrosion tests that simulated the effects of the marine atmosphere were designed and implemented. Corrosion properties, such as corrosion weight loss, morphology, and product composition of samples with different cycles, were characterized through appearance inspection, scanning electron microscopy analysis, and energy spectrum analysis. The corrosion behavior and mechanism of Q235 low-carbon steel in the enhanced corrosion environment were studied, and the corrosion weight loss kinetics of Q235 steel was verified to conform to the power function law. During the corrosion process, the passivation film on the surface of the low-carbon steel and the dense and stable α-FeOOH layer formed after the passivation film was peeled off played a role in corrosion resistance. The passivation effect, service life, and service limit of Q235 steel were studied and determined, and an evaluation model for quick evaluation of the corrosion life of Q235 low-carbon steel was established. This work provides technical support to improve the life and reliability of low-carbon steel pipelines. It also offers a theoretical basis for further research on the similitude and relevance of cyclic salt spray accelerated corrosion testing.

To simulate sheet metal forming processes precisely, an in-house dynamic explicit code was developed to apply a new solid-shell element to sheet metal forming analyses, with a corotational coordinate system utilized to simplify the nonlinearity and to integrate the element with anisotropic constitutive laws. The enhancing parameter of the solid-shell element, implemented to circumvent the volumetric and thickness locking phenomena, was condensed into an explicit form. To avoid the rank deficiency, a modified physical stabilization involving the B-bar method and reconstruction of transverse shear components was adopted. For computational efficiency of the solid-shell element in numerical applications, an adaptive mesh subdivision scheme was developed, with element geometry and contact condition taken as subdivision criteria. To accurately capture the anisotropic behavior of sheet metals, material models with three different anisotropic yield functions were incorporated. Several numerical examples were carried out to validate the accuracy of the proposed element and the efficiency of the adaptive mesh subdivision.

The stress strain curve of 7075 aluminum alloy in the temperature range of 310 °C to 410 °C was obtained by Gleeble-3800. By Nakazima test, the isothermal thermoforming limit diagrams of 7075 aluminum alloy at different deformation temperatures and stamping speeds were acquired. Moreover, the parameters of automotive S-rail hot stamping process were optimized by GA-BP neural network. The results show that the forming limit curve of 7075 aluminum alloy increases as the deformation temperature and stamping speed increase. The predicted optimal parameters for hot stamping of automotive S-rails by GA-BP neural network are: stamping speed is 50 mm/s, friction coefficient between die and blank is 0.1, and blank holder force is 5 kN. The maximum thinning rate at this process parameter is 9.37%, which provided a reference for 7075 aluminum alloy automotive S-rail hot stamping.

Based on an 33Cr23Ni8Mn3N thermal simulation experiment, the application of an artificial neural network (ANN) in thermomechanical processing was studied. Based on the experimental data, a microstructure evolution model and constitutive equation of 33Cr23Ni8Mn3N heat-resistant steel were established. Stress, dynamic recrystallization (DRX) fraction, and DRX grain size were predicted. These models were evaluated by a variety of statistical indicators to determine that these models would work well if applied in predicting microstructure evolution and that they have high precision. Then, based on the weight of the ANN model, the sensitivity of the input parameters was analyzed to achieve an optimized ANN model. Based on the most widely used sensitivity analysis (SA) method (the Garson method), the input parameters were analyzed. The results show that the most important factor for the microstructure of 33Cr23Ni8Mn3N is the strain rate ( $\dot{\mathsf{\epsilon }}$ ). For the control of the microstructure, the control of the $\dot{\mathsf{\epsilon }}$ is preferred. ANN was applied to the development of processing map. The feasibility of the ANN processing map on austenitic heat-resistant steel was verified by experiments. The results show that the ANN processing map is basically consistent with processing map based on experimental data. The trained ANN model was implanted into finite element simulation software and tested. The test results show that the ANN model can accurately expand the data volume to achieve high precision simulation results.