With the continuous advancement of science and technology, alongside the increasing significant attention within the manufacturing industry, high-performance demands are placed on advanced equipment and components because of extreme temperatures, heavy impact loads, and other challenging operating conditions. The importance of resource conservation and environmental preservation is becoming more widely recognized. This paper reviews green machining technology, driven by digital intelligence. Initially, the background of green machining powered by digital technologies is introduced, focusing on digitalization, intelligence, and sustainability as key factors for improving machining efficiency, enhancing product performance, and minimizing both energy consumption and environmental pollution. Subsequently, the paper elaborates on the current research and development in digital intelligence-driven green machining technologies, highlighting four critical areas: smart toolholders, minimal quantity lubrication (MQL), machine tool compensation, machine tool energy consumption monitoring, and intelligent carbon emission control. Lastly, the future trends and challenges in these technologies are discussed, with an outlook on the growing importance of green machining in response to technological advancements and evolving market demands.
Computer-aided engineering (CAE) is widely used in the industry as an approximate numerical analysis method for solving complex engineering and product structural mechanical performance problems. However, with the increasing complexity of structural and performance requirements, the traditional research paradigm based on experimental observations, theoretical modeling, and numerical simulations faces new scientific problems and technical challenges in analysis, design, and manufacturing. Notably, the development of CAE applications in future engineering is constrained to some extent by insufficient experimental observations, lack of theoretical modeling, limited numerical analysis, and difficulties in result validation. By replacing traditional mathematical mechanics models with data-driven models, artificial intelligence (AI) methods directly use high-dimensional, high-throughput data to establish complex relationships between variables and capture laws that are difficult to discover using traditional mechanics research methods, offering significant advantages in the analysis, prediction, and optimization of complex systems. Empowering CAE with AI to find new solutions to the difficulties encountered by traditional research methods has become a developing trend in numerical simulation research. This study reviews the methods and applications of combining AI with CAE and discusses current research deficiencies as well as future research trends.
Nano-lubricant minimum quantity lubrication (NMQL) is an eco-friendly precision technology used for grinding challenging aerospace materials. However, its film-forming ability and anti-friction performance in high-speed and high-pressure grinding zones cannot satisfy the processing requirements. To address this limitation, a novel method using magnetic traction nano-lubricant was investigated. By applying an external magnetic field, a gradient magnetic field is formed on the surface of the grinding wheel to absorb the magnetic lubricant and improve the infiltration performance. A permanent magnet was used to magnetize the grinding wheel matrix, thereby directing the magnetic flux lines and guiding the distribution of the magnetic field through the grinding wheel. Hence, the magnetic field distribution was numerically simulated by adjusting the distribution, geometric position, and parameters of the permanent magnet. In type I (wherein there is repulsion between the N-S poles on the left and right), a uniform and strong magnetic field can be generated when L=6–16 mm, β=0°–30°, and H is suitably increased. This set up can achieve a maximum magnetic field intensity of 1.1×105 A/m. Furthermore, the impact of the geometrical parameters (L, H, and β) of the magnetic-assisted device on the grindability of Ti-6Al-4V was examined using an orthogonal experiment. The optimum parameters for the permanent magnet arrangement and the geometric position were L=12 mm, H=10 mm, and β=0°, thereby resulting in a smoother workpiece with fewer defects.
High-temperature-resistant and chemically stable ceramic materials exhibit great adaptability across numerous industrial applications. Grinding is an essential component of the precision shaping and manufacturing processes for ceramic structural components. However, the low machining efficiency and high machining damage rate caused by hard and brittle material properties have been a challenge in both academia and industry. Grinding force is the most critical parameter reflecting the grinding system, and establishing an accurate prediction model is highly significant in reducing machining damage. However, a knowledge gap remains in the comprehensive review and evaluation of grinding force models for ceramic materials, which is undoubtedly not conducive to further theoretical advances. This review discusses the removal mechanism for polycrystalline ceramic materials. Subsequently, it comprehensively reviews and comparatively evaluates detailed grinding force modeling knowledge. Furthermore, it explores the specificities of the ultrasonic and laser energy-field-assisted grinding of ceramic materials in terms of their physical behavior and mechanical modeling. Finally, the theoretical value of grinding force modeling for predicting the damage to ceramic materials is explored. The current limitations of the grinding process, mechanical modeling of ceramic materials, corresponding potential research directions, and valuable research content are provided. The goal is to derive actionable low-damage grinding guidelines and establish a robust theoretical framework that enhances the quality of grinding processes for ceramics and other hard and brittle solids.
Over the past decades,the substantial carbon emissions resulting from energy-intensive manufacturing processes,which heavily rely on non-renewable resources,have increasingly attracted significant academic and industrial attention.In response,sustainable and green manufactur-ing technologies have gained prominence in the machining industry,primarily driven by international strategies target-ing"peak carbon dioxide emissions".
