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.

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.

Electrical Discharge Machining (EDM) is a machining method commonly used to produce complex shapes and deep holes by eroding hard metals with an electric arc. There is a growing demand for process simulation using finite element models in order to improve the quality and efficiency of EDM, to reduce costs, to improve resource efficiency, and to facilitate its application in critical areas such as aerospace and mechanical engineering. Finite element models have greatly improved the prediction accuracy of EDM processes, simulated complex hybrid machining processes, and provided important guidance for the optimization of EDM processes. This paper systematically reviews the research progress of finite element modeling for EDM. Finite element method modeling is evaluated mainly in terms of four indicators: material removal rate, surface roughness, tool wear ratio, and recast layer thickness. Firstly, the importance and application of EDM are described, and the EDM finite element method modeling and its advantages are summarized. Then, the single-spark simulation model and the multi-spark simulation model of EDM are compared and discussed. Among the mainstream finite element models, the prediction error of the material removal rate for single-spark simulation ranges from 8.2% to 14.75%, while the prediction error of the recast layer thickness for multi-spark simulation can be as low as 1.98%. Finally, the applications of finite element modeling in EDM hybrid machining processes’ performance prediction and new material machining are summarized, and future research directions and trends in EDM finite element modeling are predicted.

In the era of Industry 5.0, human-centric manufacturing necessitates deep integration between workers and intelligent workshop scheduling systems. However, the inherent variability in worker efficiency due to learning and forgetting effects poses challenges to human–machine–logistics collaboration, thereby complicating multi-resource scheduling in smart workshops. To address these challenges, this study proposes a real-time task-driven human–machine–logistics collaborative framework designed to enhance multi-resource coordination in smart workshops. First, the framework incorporates a learning-forgetting model to dynamically assess worker efficiency, enabling real-time adjustments to human–machine–logistics resource states. Second, a task-driven self-organizing approach is introduced, allowing human, machine, and logistics resources to form adaptive groups based on task requirements. Third, a task slack-based matching method is developed to facilitate real-time, adaptive allocation of tasks to resource groups. Finally, the proposed method is validated through an engineering case study, demonstrating its effectiveness across different order scales. Experimental results indicate that, on average, completion time is reduced by no less than 10%, energy consumption decreases by at least 8%, and delay time is reduced by over 70%. These findings confirm the effectiveness and adaptability of the proposed method in highly dynamic, multi-resource production environments.

As energy plays a fundamental role in our modern life and most of a building’s energy is used for air conditioning, understanding the sustainable regulation theory of central air conditioning remains a significant scientific issue. In view of three shortcomings of existing energy-saving regulation methods of central air conditioning: (1) few studies on low-latency, high-reliability, and safer energy-saving control operation modes, (2) lack of consideration for human comfort, and (3) insufficient analysis of the comprehensive impact of the human–machine–environment, this paper proposes an energy-saving control framework of central air conditioning based on cloud–edge–device architecture. The framework establishes a prediction model of human comfort based on recurrent neural network. An intelligent energy-saving control strategy is proposed to ensure indoor personnel’s thermal comfort, considering the human–machine–environment factors. This study provides a basis for better understanding the sustainable control theory of building central air conditioning. Finally, the experiment proves that the proposed method can effectively reduce the energy consumption of central air conditioning. Compared with traditional regulation approaches, the proposed real-time control strategy can save up to 91% of energy consumption, depending on the environment, and advance control strategies can save an average of 4%.