Ti-based bulk metallic glasses (BMGs) are very attractive for applications because of their excellent properties such as high specific strength and high corrosion resistance. In this paper, we briefly review the current status of the research and development of Ti-based bulk metallic glasses. Emphasis is laid on glass-forming ability, mechanical properties, corrosion resistance, and biocompatibility.

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.

The accurate calibration of material parameters in crystal plasticity models is essential for applying crystal plasticity (CP) simulations. Identifying these parameters usually requires unfeasible single-crystal experiments or expensive time costs due to the use of traditional genetic algorithm (GA) optimization. This study proposed an efficient and interpretable method for calibrating the constitutive parameters with macroscopic mechanical tests. This approach utilized the Bayesian neural network (BNN)-based surrogate-assisted GA (SGA) optimization method to identify a group of constitutive parameters that can reproduce the experimental stress–strain curve and crystallographic orientation by crystal plasticity simulation. The proposed approach was performed on the calibration of typical high-entropy alloy material parameters in two different CP models. The use of the surrogate model reduces the call count of simulation in the parameter searching process and speeds up the calibration significantly. With the help of infill sampling, the accuracy of this optimization method is consistent with the CP simulation and not limited by the accuracy of the surrogate model. Another merit of this method is that the pattern that the BNN surrogate found in the model parameters can be interpreted with its integrated gradients, which helps us to understand the relationship between constitutive parameters and the output mechanical response. The interpretation of BNN can guide further experiment design to decouple particular parameters and add constraints provided by the attached experiment or prior knowledge.