Superhydrophobicity, a unique natural phenomenon observed in organisms such as lotus leaves and desert beetles, has inspired extensive research on biomimetic materials. Two main superhydrophobic effects have been identified: the “lotus leaf effect” and the “rose petal effect”, both showing water contact angles larger than 150°, but with differing contact angle hysteresis values. In recent years, numerous strategies have been developed to fabricate superhydrophobic materials, among which 3D printing has garnered significant attention due to its rapid, low-cost, and precise construction of complex materials in a facile way. In this minireview, we provide a comprehensive overview of biomimetic superhydrophobic materials fabricated through 3D printing, focusing on wetting regimes, fabrication techniques, including printing of diverse micro/nanostructures, post-modification, and bulk material printing, and applications ranging from liquid manipulation and oil/water separation to drag reduction. Additionally, we discuss the challenges and future research directions in this burgeoning field.

The field of optimization problems has garnered significant attention due to its importance across various applications, particularly driven by the demand for efficient solutions to complex engineering challenges. Numerous metaheuristic algorithms inspired by animal behaviour or swarm intelligence have been proposed; however, these algorithms often pursue a multitude of strategies, resulting in excessive parameters that complicate tuning and hinder convergence and balance. Additionally, algorithms based on human behaviour remain scarce. To address these limitations, extensive research has been conducted on the interplay between social memory and individual memory, leading to the introduction of a novel human-behaviour-inspired metaheuristic algorithm, named Advanced Social Memory Optimization (ASMO). This algorithm seeks to address the complexities of parameter management, convergence, and balance more effectively with a streamlined set of strategies. Furthermore, a mathematical model based on the mechanisms of social memory formation and individual memory updating underpins the algorithm. Rigorous performance evaluations, utilizing the Wilcoxon Rank-Sum Test and the Friedman Test across multiple benchmark suites (CEC2017, CEC2019, and CEC2022), demonstrate that ASMO, with only two algorithmic strategies, outperforms or matches established algorithms on more than half of the test functions. These findings suggest promising new avenues for research in the field of optimization and, given the succinctness of ASMO’s strategies, underscore its potential as a powerful tool for enhancing and developing solutions to complex engineering design problems. The code for the ASMO algorithm is available in appendix D.

The degradation process of bearing performance in the whole life cycle is a complex and nonlinear process. However, the traditional neural network-based approaches usually consider the degradation process of bearing performance as linear, which does not accord with the actual situation of bearing degradation. To overcome this shortcoming, a rolling bearing’s remaining useful life prediction method based on a Squeeze-and-Excitation-Convolutional long short-term memory (SE-ConvLSTM) neural network was proposed based on the construction of a new health index in the process of bearing life evolution. The proposed method considered the change rule of the health indicator during the whole life cycle evolution of bearings, then constructed the health indicator by using the SE-ConvLSTM neural network, effectively improving the model prediction accuracy and training efficiency. Firstly, the original data are filtered and denoised by Ensemble Empirical Mode Decomposition. Combined with Principal Component Analysis (PCA) dimensionality reduction and the Local Outlier Factor (LOF) algorithm, the bearing’s life evolution interval is divided. Then, the health indicator is constructed based on the proposed SE-ConvLSTM model, and the remaining useful life of rolling bearings is predicted by a particle filter and double exponential model. The proposed method is compared with other related methods with the PHM2012 dataset, and the results show that the proposed method has higher accuracy in remaining useful life predictions. Compared with the traditional method, the health index construction based on the division of the lifespan evolution interval has higher practical significance.

This paper presents numerical failure analysis on cracking of shield machine cutterhead structure during a metro-tunnel construction. The stress intensity factors (SIFs) of surface cracks with different shapes and location angles were analyzed by a finite element simulation method based on linear elastic fracture mechanics (LEFM) theory. The ratios of variation in stress intensity factors of cracks with different shapes were analyzed. The maximum allowable crack depth of the cutterhead panel is 50.23 mm by dynamic stress calculation, and the damage tolerance criterion of the cutterhead panel was proposed. The influence of the Paris model parameter values was analyzed based on mathematical methods. It is proven that the location of the cutterhead cracking angle is mainly determined by the mixed-mode SIF. In practice, the crack section basically expanded into the semi-elliptical shape. The cutterhead structure may directly enter the stage of crack propagation due to welding defects during tunneling. The research results provide a theoretical basis and important reference for crack detection in the key parts of the cutterhead, as well as maintenance cycle determination and life prediction of the cutterhead mileage, both of which have important engineering value.