In recent years, infrared thermographic (IRT) technology has experienced notable advancements and found widespread applications in various fields, such as renewable industry, electronic industry, construction, aviation, and healthcare. IRT technology is used for defect detection due to its non-contact, efficient, and high-resolution methods, which enhance product quality and reliability. This review offers an overview of active IRT principles. It comprehensively examines four categories based on the type of heat sources employed: pulsed thermography (PT), lock-in thermography (LT), ultrasonically stimulated vibration thermography (UVT), and eddy current thermography (ECT). Furthermore, the review explores the application of IRT imaging in the renewable energy sector, with a specific focus on the photovoltaic (PV) industry. The integration of IRT imaging and deep learning techniques presents an efficient and highly accurate solution for detecting defects in PV panels, playing a critical role in monitoring and maintaining PV energy systems. In addition, the application of infrared thermal imaging technology in electronic industry is reviewed. In the development and manufacturing of electronic products, IRT imaging is used to assess the performance and thermal characteristics of circuit boards. It aids in detecting potential material and manufacturing defects, ensuring product quality. Furthermore, the research discusses algorithmic detection for PV panels, the excitation sources used in electronic industry inspections, and infrared wavelengths. Finally, the review analyzes the advantages and challenges of IRT imaging concerning excitation sources, the PV industry, the electronics industry, and artificial intelligence (AI). It provides insights into critical issues requiring attention in future research endeavors.

In recent years, biomedical materials have been used in the response to the emergence of medical infections that pose a serious threat to the health and life of patients. The construction of superhydrophobic coatings and antimicrobial coatings is among the most effective strategies to address this type of medical derived infection. Firstly, this paper reviews the preparation methods of superhydrophobic surface coatings and their applications; summarizes the advantages and disadvantages of superhydrophobic surface preparation schemes based on the template method, spraying methods, etching methods, and their respective improvement measures; and focuses on the applications of superhydrophobic surfaces in self-cleaning and antibacterial coatings. Then, the action mechanisms of contact antibacterial coatings, anti-adhesion bacteriostatic coatings, anti-adhesion bactericidal coatings, and intelligent antibacterial coatings are introduced, and their respective characteristics, advantages, and disadvantages are summarized. The application potential of antimicrobial coatings in the field of biomedical materials is highlighted. Finally, the applications of superhydrophobic and antimicrobial coatings in medical devices are discussed in detail, the reasons for their current difficulties in commercial application are analyzed, and the future directions of superhydrophobic coatings and antimicrobial coatings are considered.

Electrical discharge machining (EDM) is a non-traditional process, which can cut materials with a high melting point, high hardness, high strength, and low brittleness. However, the kerosene (dielectric of EDM) produces aerosols and toxic gases at high temperatures, which seriously affect the health of operators and air quality. This means that it is not conducive to the green manufacturing and sustainable development of EDM. In this study, thereafter, sunflower seed oil (SSO) and kerosene were used as dielectrics of EDM for machine SKD11, and the machining performance of the two dielectrics under different current, duty ratio, pulse duration and electrodes were comparatively analyzed, such as material remove rate (MRR), surface roughness (Ra), energy efficiency per volume (EEV) and exhaust emissions characteristics (EEC). This investigation found that the minimum value of EEV in SSO was 0.3879 kJ/mm³, which was about 25% lower than the minimum value of 0.4849 kJ/mm³ in kerosene. The emission rate of Cu electrode in SSO was 62.017 µg/min, which was lower than that in 78.857 µg/min, decreasing by about 21.36%, in kerosene. In addition, a super depth of field optical micro-scope was subsequently used in the experiments to observe the diameter of the debris. The results indicated that SSO has a larger proportion of debris of more than 35 µm in diameter. Therefore, SSO can be adopted as a substitute for kerosene dielectric to improve the sustainability of electrical discharge machining and realize green manufacturing.

Electrical discharge machining (EDM) can use soft tool electrodes to process hard workpieces to achieve “soft against hard”, because it directly uses electrical energy and thermal energy to remove metal materials. Then, it can generate complex features on harder materials and meet the requirements of excellent surface quality. Since EDM involves many process parameters, including electrical parameters, non-electrical parameters, and materials properties, it is essential to optimize its process parameters to obtain good performance. In this direction, the application of the swarm intelligence (SI) technique has become popular. In this paper, the existing literature is comprehensively reviewed, and the application of the SI technique in the optimization of EDM process parameters is summarized. Sinker-EDM (SEDM), wire-EDM (WEDM), and micro-EDM (MEDM) with various hybrid techniques are among the EDM methods considered in this study because of their broad adoption in industrial sections. The fundamental nature of all review articles will assist engineers/workers in determining the process parameters and processing performance, the SI algorithm, and the optimal technique by which to obtain the desired process parameters. In addition, discussions from the perspectives of the similarity, individuality, and complementarity of various SI algorithms are proposed, and necessary outlooks are predicted, which provides references for the high performance of the EDM processes in the future.

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.

Hydrogen energy is recognized as the clean energy with the most development potential, and hydrogen fuel cell technology is considered the ultimate solution utilizing hydrogen energy. The proton exchange membrane fuel cell (PEMFC) has the merits of high energy efficiency, high energy density, low operating temperature, is clean, and affords environmental protection. Improving the structure of each functional layer could play a significant role in improving PEMFC performance. In addition, membrane electrode assemblies (MEAs) are the core components of a PEMFC, and their structure includes three main parts, namely, the gas diffusion layer (GDL), catalytic layer (CL), and proton exchange membrane (PEM). Therefore, this review focuses on progress in the modeling and simulation of the material structure in MEAs. First, the GDL simulation models are critically reviewed, including two-phase calculation models and microscopic simulation models. Second, CL microstructure models are comprehensively evaluated, involving power density enhancement, catalyst loading distribution, electrochemical reaction and its performance optimization. Third, the PEM simulation model, relating to molecular dynamics (MD) simulation techniques, 3D numerical techniques, and multiphysics simulation, are reviewed. Finally, the three aspects of similarity, individuality, and complementarity of these simulation models are discussed, and necessary outlooks, including the current limitations and challenges, are suggested, providing a reference for low-cost, high-performing PEMFC membrane electrodes for the future.