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When the water droplets are on some superhydrophobic surfaces, the surface only needs to be inclined at a very small angle to make the water droplets roll off. Hence, building a superhydrophobic surface on the material substrate, especially the metal substrate, can effectively alleviate the problems of its inability to resist corrosion and easy icing during use, and it can also give it special functions such as self-cleaning, lubrication, and drag reduction. Therefore, this study reviews and summarizes the development trends in the fabrication of superhydrophobic surface materials by non-traditional processing techniques. First, the principle of the superhydrophobic surfaces fabricated by laser beam machining (LBM) is introduced, and the machining performances of the LBM process, such as femtosecond laser, picosecond laser, and nanosecond laser, for fabricating the surfaces are compared and summarized. Second, the principle and the machining performances of the electrical discharge machining (EDM), for fabricating the superhydrophobic surfaces, are reviewed and compared, respectively. Third, the machining performances to fabricate the superhydrophobic surfaces by the electrochemical machining (ECM), including electrochemical oxidation process and electrochemical reduction process, are reviewed and grouped by materials fabricated. Lastly, other non-traditional machining processes for fabricating superhydrophobic surfaces, such as ultrasonic machining (USM), water jet machining (WJM), and plasma spraying machining (PSM), are compared and summarized. Moreover, the advantage and disadvantage of the above mentioned non-traditional machining processes are discussed. Thereafter, the prospect of non-traditional machining for fabricating the desired superhydrophobic surfaces is proposed.

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Laser interaction with nanoparticles in liquid is the fundamental theoretical basis for many applications but it is still challenging to observe this nanoscale phenomenon within a few nanoseconds in liquid by experiment. The successful implementation of the two-temperature method integrated with molecular dynamics (TTM-MD) in laser interaction with bulk material has shown great potential in providing a panoramic view of the laser interaction with the nanoparticles. However, the current TTM-MD model has to divide the system into cubic cells, which leads to mistakes near the nanoparticle’s surface. We introduce the latest model, which performs the TTM-MD on each individual cluster instead of the cubic cells, and its high-performance parallel cluster analysis algorithm to update the cluster size. The cluster-based TTM-MD revealed the nanoparticle formation mechanism of laser fragmentation in liquid (LFL) and facilitated the study of laser fluence’s effect on the size distribution. In addition to LFL, this model is promising to be implemented in the laser thermal therapy of tumors, laser melting in liquid (LML), etc. Although cluster-based TTM-MD has proven to be a powerful tool for studying laser interaction with nanoparticles, a few challenges and future developments for the cluster-based TTM-MD, especially the ionization induced by femtosecond, are also discussed.

Liquid crystal display (LCD) is a display device based on liquid crystal electro-optic effect, and LCDs have gradually appeared and have become an indispensable part of people’s lives. In the development of LCD technology, the detection of Mura defects is a key concern in the manufacturing process. The Mura defect is a kind of display defect with low contrast and an irregular shape. This study first explains the mechanism of Mura defects in the LCD manufacturing process and classifies typical Mura defects. Then, three main purposes for the defect detection of LCDs are compared, and the advantages and disadvantages are conducted. Following that, this research examines reviews the linked literature on image preprocessing, feature extraction, dimension reduction, and classifiers of Mura defects. Finally, the future development trend and research direction of Mura defect detection based on machine vision can be drawn by this study.