Additive manufacturing (AM) is revolutionizing the fabrication of high-performance metal cutting tools by transcending the geometric and material constraints of conventional methods. This study establishes a design-process-material-performance integration framework to overcome critical challenges in AM-enabled tool development. Through systematic review and analysis, three innovation pathways are proposed: i) novel design methodologies leveraging topological optimization and bio-inspired structures, ii) development of high-performance materials, and iii) process optimization strategies for micro-structure regulation, densification control, and hardness–toughness balance. The analysis reveals persistent limitations in current AM tool technologies; these include material defects induced by process instabilities, post-processing bottlenecks, lack of standards, and cost barriers hindering industrial adoption. Frontier research directions for propelling future advancements are delineated as follows: AI-driven full-process development, smart tool integration with embedded sensors, and nano-reinforced composite materials. Concurrently, engineering-oriented priorities should emphasize user-specific design customization, process–material compatibility studies, and quality assurance protocols for AM tool standardization. This work provides theoretical frameworks and actionable roadmaps to bridge the gap between academic exploration and industrial implementation of AM-based cutting tools.

Automatic segment assembly, which increases boring efficiency and construction safety, is a trend in tunnel boring. However, the current situation still relies on manual operation and experience. Electro-hydraulic rotation systems are crucial in segment grasping, transporting, and assembly. This article presents the automation of rotation systems in segment assembly to improve motion smoothness and accuracy. As a result of strong nonlinearity and system complexity, parameter estimation is performed by using a noise reduction method based on multi-algorithm fusion and the stochastic gradient deviation correction recursive least squares identification algorithm. Active disturbance rejection control (ADRC) is introduced into sliding mode control (SMC) to compensate for model uncertainty and disturbance. An improved cuckoo algorithm is used to optimize influential parameters in ADRC. Moreover, full-scale bench tests are conducted to verify the proposed system automation. Results indicate that the proposed method has a better displacement tracking performance and lower tracking error than the ADRC, SMC, and proportional–integral–derivative methods. Furthermore, such a procedure facilitates the success rate of complete segment assembly.

Enhancing the motion performance of wheeled biped robots amidst uncertain disturbances remains a challenge due to their under-actuated and inherently unstable nature. Aiming to address this issue, this paper proposes a disturbance adaptive control framework for such robots. The framework introduces a disturbance variable to describe the comprehensive effect of disturbances due to environmental interactions on the robotic system. A Kalman filter is also employed to estimate the robot’s center of mass (CoM) state and the uncertain disturbances by leveraging the dynamic coupling intrinsic to the robots. Estimated results are then integrated into a nominal model predictive control framework to generate an optimal CoM trajectory over a finite time horizon. This approach enables the robot to adapt to various types of external disturbances in the sagittal plane while maintaining accurate velocity tracking. The efficacy of the proposed approach is validated by conducting experimental evaluations on a hydraulically driven wheeled biped robot.

A parallel wheelset suspension (PWS) designed for a heavy-duty lunar vehicle, specifically for a multi-wheeled pressurized lunar rover (MWPLR), is beneficial for adapting wheels to rough terrain and absorbing vertical vibrations passively. It is a 2-degree-of-freedom spatial parallel mechanism. However, when a lunar vehicle is driven over rough terrain, the wheelset alignment parameters of the PWS vary substantially, resulting in poor wheel-to-ground contact. This paper aims to address these problems. It first presented a PWS design approach, used simulations to confirm the correctness of the kinematic model, evaluated the initial suspension performance, and established an optimization objective. We then analyzed the suspension’s instantaneous screw axis variations as the wheelset crossed the obstacle. The results help us determine the causes and optimization variables that affect the alignment parameters. Finally, based on the kinematic and simulation analysis methods, the optimized suspension ensured that the variation in the camber, toe, and inclination angle of the steering axis would be [-1^{\circ },1^{\circ }] when the MWPLR crossed a 0.4 m high obstacle. The simulation demonstrated that the PWS improved the ride comfort of the MWPLR and that the optimized PWS enhanced the straight-line drivability and flexible steering capability of the MWPLR. PWS and its design methodology provide a design reference for other multi-wheeled rovers.

The assembly quality of the tube system, which serves as the “blood vessels” and “trachea” of the aircraft, has a crucial impact on the stable and reliable service of aircraft. At present, the tightening control method of the tube system is mainly the torque control method, which exhibits considerable discreteness and has difficulty achieving precise control of the assembly quality. This paper constructed a torque-angle tightening control model based on the torque–force relationship and angle–force relationship of aviation tube systems. Through tightening experiments with five tube diameters, the initial tightening degree, re-tightening operation, lubrication condition, tube material, and connector material were investigated by comparing the torque-angle tightening curves. Results showed that the re-tightening operation can increase the tightening torque by at least 8%, the lubrication condition can reduce the target torque by about 16%, and the control angle range can be reduced by about {10}^{\circ }. Further theoretical and experimental analysis revealed that the system stiffness remained stable at 90000 N/mm when the tube diameter was greater than 6 mm. The torque–tension coefficient remained stable at around 0.16, while the angle–tension coefficient gradually decreased with the increase in the tube diameter.

This paper presents a multi-joint desensitization design (MDD)-based assembly distribution and precision evolution for industrial parallel robots. The optimization of a 3-UPS/S parallel robot is demonstrated through the optimization of its performance index and precision performance, achieved through the construction of a global error sensitivity. The precision degradation law for independent sources of uncertainty is introduced, and the accelerated degradation after multiple repairs is considered to establish a source-split maintenance yield model to formulate an optimized operation and maintenance strategy. Experiment demonstrates that the MDD method significantly enhances the precision and reliability of the equipment. Compared with that in the pre-optimization stage, the lifetime of the equipment is extended by 38.88%, while the cost remains unchanged. In addition, the effectiveness of MDD in additive manufacturing is demonstrated through an industrial bending pipe case.

Air-coupled ultrasonic transducers (ACUTs) have been applied in industrial non-destructive testing, structural health monitoring, and medical ultrasound. However, conventional passive focusing methods often result in undesired effects on sensitivity due to variations in acoustic impedance matching conditions, which are critical in ACUT design, where sensitivity is the top priority. Accordingly, a novel active focusing method for ACUTs is proposed in this study. The key idea is to create multiple concentric ring electrode patterns on a bulk planar piezoelectric plate so that a quarter-wavelength acoustic impedance matching layer of uniform thickness can be attached onto the plate. The initial structural parameters of the electrode patterns are determined based on the design methodology of a Fresnel zone plate (FZP). Those parameters are optimized through finite element simulation, with the transducer’s sensitivity as the objective function, while ensuring only slight variations to the focal length and lateral resolution. Single-sided multiple concentric ring electrode patterns are then fabricated on 1–3 piezoelectric fiber/epoxy resin composite plate by screen printing and combined with a high-performance acoustic impedance matching layer made from epoxy resin composite filled with hollow glass microspheres. The planar active focusing ACUT is developed, while two types of conventional passive focusing ACUTs using FZP and concave lens are fabricated with the same piezoelectric and acoustic matching materials. Comparative experimental testing is carried out. The developed planar active-focusing ACUT achieves significant sensitivity improvements of 7.1 and 17.4 dB, respectively, while maintaining comparable radial and axial full-width at half maximum. The results of this study offer a novel approach for the design of high-performance ACUTs.

This study proposes a novel single-degree-of-freedom (SDOF) hexagonal mechanism that utilizes a cam mechanism to regulate its movement. The hexagonal mechanism adopts a conjugate cam as joint slideways, forming an internal closed-chain cam mechanism that is connected to an external 6R linkage via connecting links. By integrating the cam mechanism’s rotation with the driving system, the mechanism can achieve variable length of the central collinear cranks through coupling structure. Moreover, by utilizing the closed-chain cam mechanism and variable parameters of the central cranks, motion control and adjustment of the external structure with three degrees of freedom can be achieved by central driving with SDOF. On the basis of the locomotion planning and kinematics analysis, the structure design and parameter analysis of the hexagonal mechanism are presented. Moreover, the parameters are analyzed using the repeatability of joint trajectories on the basis of locomotion planning that reduces initial motion conditions, ensures motion continuity, and reduces collision energy loss. Finally, the rationality of the design of the hexagonal mechanism with dynamic rolling locomotion is verified through theory, simulation, and experiment.

Pixel-wise segmentation techniques based on deep learning have been widely applied in the inspection of product surface defects to ensure product quality. However, existing models based on deep learning primarily focus on separate specific defect types, which introduces challenges in generalizing them to the detection of diverse product defects. The relatively low occurrence probability of certain defects also sets obstacles in obtaining sufficient defect samples for effective model training. Herein, an innovative dual-branch edge-based graph reasoning network (DEGRNet) is demonstrated for the few-shot segmentation (FSS) of industrial surface defects, which can be easily generalized to various defects with minimal labeled defect samples. DEGRNet mainly consists of a background eliminating (BE) module, an edge reinforcement (ER) module, and a graph reasoning module. The BE module can effectively fuse the feature information from the support image and support mask to reduce background interference, while the ER module works to accurately extract edge contour information. The rough segmentation maps from BE and the boundary-enhanced segmentation maps from ER are simultaneously input into the dual-branch graph reasoning module to enhance the modeling capability of long-distance feature information and refine segmentation boundaries. This feature enables our model to fully utilize global image features and gain robust generalization capabilities for unknown defect types. The results of multiple experiments validate the effectiveness of the as-proposed modules. Our model achieves state-of-the-art performance metrics in few-shot defect segmentations. Specifically, our model exhibits 2.61% and 3.50% improvements in mean intersection over union under the 1-shot and 5-shot conditions, respectively, compared with existing state-of-the-art FSS models.

This paper presents a novel wheel bonnet polishing (WBP) process, combining the flexible contact characteristics of bonnet polishing with the high-efficiency material removal capabilities of wheel polishing. A comprehensive tool preparation scheme is developed, including a surface approximation-based method for polishing pad design and a precision dressing technique, ensuring good adhesion to the bonnet base and high rotational accuracy. Finite element simulations and experimental validation reveal the effects of internal pressure and clamping distance on bonnet deformation and contact regulation. The material removal characteristics of the wheel bonnet under various process parameters are systematically studied, confirming its capability to generate a stable, symmetric elliptical Gaussian removal profile. Continuous area polishing experiments show that scratches potentially appear when the tool feed direction aligns with the tangential speed of the bonnet. However, adjusting the polishing posture and path spacing can substantially reduce scratches. The roughness evolution of the polished area shows a rapid decrease, eventually stabilizing. Finally, the technique is applied to large-area smooth polishing of high-precision air floating guide rails. Experimental results show that surface roughness (Sa) rapidly decreases from an average 1.5 μm to a uniform 10 nm, revealing a converging form accuracy (PV) to 0.8 μm and an RMS value reaching 179.73 nm. Overall, the WBP process shows remarkable potential for high-quality surface processing, highlighting its value in advanced manufacturing.

Aerostatic bearings are extensively utilized in applications such as semiconductor processing and ultraprecision machining. However, turbulence in the bearing recess induces micro-vibration, which significantly affects stability. This study proposes an innovative arrayed multi-orifice restrictor (AMR) with square or circular distribution to limit the generation of turbulence and diminish bearing micro-vibration. The steady air flow field properties and static performance of bearings with square and circular AMRs are compared under various AMR geometric parameters through computational fluid dynamics. Through three-dimensional large eddy simulation, the transient flow properties of the flow field and pressure fluctuations of the bearings with AMRs are analyzed. This analysis clarifies the influence of the array number, spacing, and distribution types on the suppression of turbulent vortex occurrence and vibration amplitude. Results demonstrate that the optimized design of AMR significantly suppresses the generation of turbulent vortices, which makes the airflow entering the recess more uniform and orderly. Consequently, the vibration amplitude of bearings can be reduced effectively without sacrificing the load-carrying capacity and stiffness. Bearings with circular AMR have better stability and weaker vibration amplitude than those with square AMR.

We present a truncated hierarchical B-splines-oriented adaptive isogeometric topology optimization (THB-AITO) framework for shell structures. The method integrates the isogeometric analysis (IGA) approach using Kirchhoff–Love theory with THB-splines to achieve adaptive topology optimization for shell structures. IGA ensures the accuracy of displacement fields and sensitivity values during numerical analysis. Compared with non-uniform rational B-spline (NURBS) with the tensor product structure, the basis functions of THB-splines are more suitable for performing the local adaptive refinement on shell structure meshes. Specifically, intermediate density meshes near the topological boundary are refined to achieve a highly accurate topology configuration with few degrees of freedom (DOFs), facilitated by a fully adaptive marking strategy. Numerical results indicate that, compared with NURBS-based isogeometric topology optimization, THB-AITO can solve the compliance minimization shell problem with fewer DOFs and design variables. In addition, comparisons with other refined splines were conducted to demonstrate the advantages of our proposed THB-AITO approach. The THB-AITO is a promising way of optimizing thin-shell structures.

The core objectives of intelligent manufacturing are to enhance product quality, reduce production costs, and improve manufacturing efficiency. One of the key technologies to achieve this goal is the monitoring and control of the machining process. Traditional offline tool condition monitoring (TCM) methods are prone to human error and require additional downtime. By contrast, tool condition online monitoring technology enables real-time monitoring throughout the machining process. This study reviews the progress of research on online monitoring of tool conditions and process parameter control based on tool status. It provides a detailed analysis from three perspectives: tool condition perception, algorithms for monitoring and control, and applications of monitoring and control. In terms of tool condition perception, the advantages and drawbacks of various sensing methods are explored. In terms of monitoring and control algorithms, developments from traditional monitoring algorithms to intelligent monitoring and parameter control algorithms are progressively reviewed. With regard to applications, the review focuses on how to adjust process parameters online on the basis of TCM by building on abnormal detection, tool wear monitoring, and life prediction to protect tools and improve machining efficiency. Then, the main development trends in tool condition online monitoring and control are introduced. Unlike previous reviews, this work extends the discussion beyond TCM to include recent progress in process parameter control on the basis of tool condition. The integration of tool online monitoring and process parameter control is recognized as one of the essential technologies for realizing intelligent manufacturing.

Grinding force is an essential metric for determining whether a material is grindable. It can directly affect surface integrity, machining efficiency, and tool life. Therefore, the mechanical behavior of grinding has long been a topic of research. A large number of studies have demonstrated that ultrasonic vibration-assisted grinding (UVAG) reduces grinding force and improves surface quality after grinding. However, these studies have many gaps, and the mechanism underlying UVAG force has not been explored. First, the study investigates the influence of grinding and ultrasonic vibration parameters on grinding force with an empirical model based on statistical theory. Second, the material removal mechanism of UVAG is investigated using a finite element model, and the mechanical properties and grinding force evolution mechanism of various materials (difficult-to-cut alloys, brittle materials, and composite materials) for UVAG are reviewed. Simultaneously, a mathematical analysis model of ultrasonic-assisted grinding force based on kinematics and separation characteristics is presented. The error comparison and source analysis of the grinding force model are performed. Finally, based on the current difficulties and research gaps in the UVAG force model, various future research directions are proposed.