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Laser surface texturing (LST) is a non‐contact manufacturing process for fabricating functional surfaces in a manner that improves the corresponding wettability, and is widely used in biomedicine and industry. Laser surface texturing is a facile approach that is compatible with various materials, can result in a hierarchical texture, and enables a high degree of surface wetting (i.e., extreme wetting). In addition to surface structures, surface chemical modification is a primary factor in producing extreme wetting surfaces. This review discusses the effects of various surface textures and surface chemistries on wettability. Optimal laser parameters for the desired surface texture are based on the fundamental wettability and laser mechanism. In particular, bumps in the morphology are conducive to obtaining extreme wetting. Diverse surface chemical strategies result in extreme wetting by different mechanisms. This paper makes a rigorous evaluation of the laser parameters and optimal surface chemical modifications by elucidating the relationships between the surface structure, surface chemical modification, and wettability, and in so doing, determines the final wettability. The unresolved problems of LST are presented in the conclusion. This review provides guidance, development directions, and an integrated framework for LST, which will be useful for fabricating extreme wetting surfaces on various metals.

Clustering is an unsupervised learning technology, and it groups information (observations or datasets) according to similarity measures. Developing clustering algorithms is a hot topic in recent years, and this area develops rapidly with the increasing complexity of data and the volume of datasets. In this paper, the concept of clustering is introduced, and the clustering technologies are analyzed from traditional and modern perspectives. First, this paper summarizes the principles, advantages, and disadvantages of 20 traditional clustering algorithms and 4 modern algorithms. Then, the core elements of clustering are presented, such as similarity measures and evaluation index. Considering that data processing is often applied in vehicle engineering, finally, some specific applications of clustering algorithms in vehicles are listed and the future development of clustering in the era of big data is highlighted. The purpose of this review is to make a comprehensive survey that helps readers learn various clustering algorithms and choose the appropriate methods to use, especially in vehicles.

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