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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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Transparent nano-ceramics have an important high-transmittance, material-integrating structure and function and a variety of potential applications, such as use in infrared windows, optical isolators, composite armors, intelligent terminal screens, and key materials of solid-state lasers. Transparent ceramics were originally developed to replace single crystals because of their low fabricating cost, controllable shape, and variable composition. Therefore, this study reviews and summarizes the development trends in transparent nano-ceramics and their potential applications. First, we review the research progress and application of laser nano-ceramic materials, focusing on the influence of controllable doping of rare earth ions on thermal conductivity and the realization of large-scale fabrication technology. Second, the latest research progress on magneto-optical transparent nano-ceramics, mainly including terbium gallium garnet (Tb3Ga5O12, TGG) ceramics and terbium aluminum garnet (Tb3Al5O12, TAG) ceramics, are summarized, and their performance is compared. Third, the research progress of transparent armor nano-ceramic materials, represented by MgAl2O3 and Aluminum oxynitride (AlON), are reviewed. Lastly, the progress in electro-optical transparent nano-ceramics and scintillation transparent nano-ceramics is reported, and the influence of the material-fabrication process on electro-optic effect or luminous intensity is compared. Moreover, the effect of particle diameter on fabrication, the relationship between nano powder and performance, and different sintering methods are discussed. In summary, this study provides a meaningful reference for low-cost and sustainable production in the future.

This paper proposed a hybrid intelligent process model, based on a hybrid model combining the two-temperature model (TTM) and molecular dynamics simulation (MDS) (TTM-MDS). Combined atomistic-continuum modeling of short-pulse laser melting and disintegration of metal films [Physical Review B, 68, (064114):1–22.], and Gaussian process regression (GPR), for micro-electrical discharge machining (micro-EDM) were also used. A model of single-spark micro-EDM process has been constructed based on TTM-MDS model to predict the removed depth (RD) and material removal rate (MRR). Then, a GPR model was proposed to establish the relationship between input process parameters (energy area density and pulse-on duration) and the process responses (RD and MRR) for micro-EDM machining. The GPR model was trained, tested, and tuned using the data generated from the numerical simulations. Through the GPR model, it was found that micro-EDM process responses can be accurately predicted for the chosen process conditions. Therefore, the hybrid intelligent model proposed in this paper can be used for a micro-EDM process to predict the performance.

This paper introduces novel methods for detection of sprouting in potatoes using machine vision. An ensemble‐based classifier was trained to detect sprouting considering the diversity of both feature extraction and classifiers. Two categories, namely manual feature extraction and automatic feature extraction, were utilized to enhance the diversity of feature extraction. In experiments, the proposed ensemble‐based classifier without multiple channels CNN (MC‐CNN) outperformed mainstream methods and achieved state‐of‐the‐art prediction rate of 0.916 and f‐measure of 0.905 under lower standard deviation. Furthermore, with the help of three different CNN classifiers, there was already an obvious improvement in the performance of ensemble‐based classifier with MC‐CNN. The prediction rate and f‐measure increased about 4∼5%, comparing to that without MC‐CNN. The results indicate that this approach has a better capability to combine the features for detection of sprouting in potatoes, in which the diversity of both feather extraction and classifier had been enhanced. Practical Applications Discrimination and classification of potato sprouting or not, which uses ensemble‐based classifier system with combine the best features.

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Lévy flights random walk is one of key parts in the cuckoo search (CS) algorithm to update individuals. The standard CS algorithm adopts the constant scale factor for this random walk. This paper proposed an improved beta distribution cuckoo search (IBCS) for this factor in the CS algorithm. In terms of local characteristics, the proposed algorithm makes the scale factor of the step size in Lévy flights showing beta distribution in the evolutionary process. In terms of the overall situation, the scale factor shows the exponential decay trend in the process. The proposed algorithm makes full use of the advantages of the two improvement strategies. The test results show that the proposed strategy is better than the standard CS algorithm or others improved by a single improvement strategy, such as improved CS (ICS) and beta distribution CS (BCS). For the six benchmark test functions of 30 dimensions, the average rankings of the CS, ICS, BCS, and IBCS algorithms are 3.67, 2.67, 1.5, and 1.17, respectively. For the six benchmark test functions of 50 dimensions, moreover, the average rankings of the CS, ICS, BCS, and IBCS algorithms are 2.83, 2.5, 1.67, and 1.0, respectively. Confirmed by our case study, the performance of the ABCS algorithm was better than that of standard CS, ICS or BCS algorithms in the process of EDM. For example, under the single-objective optimization convergence of MRR, the iteration number (13 iterations) of the CS algorithm for the input process parameters, such as discharge current, pulse-on time, pulse-off time, and servo voltage, was twice that (6 iterations) of the IBCS algorithm. Similar, the iteration number (17 iterations) of BCS algorithm for these parameters was twice that (8 iterations) of the IBCS algorithm under the single-objective optimization convergence of Ra. Therefore, it strengthens the CS algorithm’s accuracy and convergence speed.