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.
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.
Wire arc additive manufacturing (WAAM) is an economical and efficient technology for manufacturing large metal parts with complex physical states that are difficult to observe in situ. However, in-depth systematic research on the fluid flow state and droplet transition behavior in WAAM under complex paths is lacking. Firstly, the free surface of the molten pool was tracked using the volume-of-fluid (VOF) method. Subsequently, by integrating matrix transformation methods, the dual ellipsoidal heat source was varied over time, and its dynamic effects on the molten pool were studied. Finally, the shapes and sizes of the deposited bead and weld pool were determined. The results showed that the droplets brought heat and kinetic energy to the molten pool and that the kinetic energy of the molten pool was more easily dissipated on complex paths than on straight paths. The impact of droplets on the molten pool, creating a negative pressure, is one of the reasons for the precipitation of gas and the eventual formation of a unique bubble distribution. The primary reason for the tilt of the molten pool in the moving direction was the influence of the liquid tension and arc pressure. The simulated profiles of the deposited bead and droplet transfer are validated using experimental cross-sectional and high-speed camera images. The consistency between the simulation results and the experimental outcomes was good, aiding the precise control of specific requirements in future production.
High-shear and low-pressure grinding with a body-armor-like grinding wheel is a novel grinding method with great potential for ultraprecision machining of difficult-to-cut materials. However, the material removal rate model for the new grinding process is still lacking. In this study, elastohydrodynamic pressure distribution at the working interface between a body-armor-like grinding wheel and the workpiece was revealed. The microcontact state of the single abrasive grain in the interface was uncovered. The formulas of the forces acting on the rubbing, plowing, and cutting abrasive grain were analyzed. Based on the force model of the single abrasive grain and the Gaussian distribution of the grain protrusion heights, the actual grinding depth of the cut model and specific removal rate model were proposed for the novel high-shear and low-pressure grinding process. The influence of the grinding wheel and processing parameters on the material removal rate was investigated. It was found that the actual cut grinding depth decreased with the increase of the workpiece feed rate while the material removal rate remained almost constant. By comparing with experimental and theoretical results, it was shown that the model could accurately predict the actual grinding depth of cut and specific removal rate under different processing parameters, with a minimum prediction error of 1.5%. The maximum actual grinding depth of cut (i.e., 0.60 μm), was obtained for Inconel 718 workpiece. The findings of this study provide theoretical guidance for the practical application of high-shear and low-pressure grinding.
Cutting chatter is a major factor that limits machining efficiency and can negatively impact the quality of a cutting surface. Chatter suppression is crucial for improving machining efficiency and maximizing business benefits. However, most chatter suppression techniques are difficult to use on a massive scale in actual production because of their high cost and limited applicability. In the investigation of chatter suppression, particularly in recent years, unique and effective suppression methods have been developed that must be summarized and arranged, and their advantages and disadvantages must be evaluated in depth. Therefore, this paper summarizes and systematically discusses recent research advancements in chatter suppression methods. Furthermore, future research directions for chatter suppression technologies are predicted.
Instantaneous material removal volume (IMRV) is a key parameter for predicting the cutting power, cutting force, and machining process. This paper presents a novel approach, known as the point cloud contour-filling method, for calculating the IMRV for each cutting tool edge at any instantaneous moment. Firstly, the kinematics during milling operations are analyzed to capture the exact motion trajectory envelope point cloud of the cutting tool edge. Secondly, the Z-map algorithm and Boolean operations are utilized to calculate the point cloud of the intersection between the workpiece and tool-edge trajectory envelope within unit time steps Δt (known as the IMRV point cloud). Finally, the 3D alpha method and Delaunay triangulation are employed to calculate the shape and volume of the IMRV. The proposed model considers the real tool-edge trajectory and tool installation errors, and introduces the variable of tool-workpiece engagement time t for the first time. The model is verified using milling tests. The proposed method provides a visualization of instantaneous complex engagement between the tool and workpiece during the milling process and can be further used for simulating milling forces and cutting power.