Laser powder bed fusion (LPBF) enables the fabrication of intricate porous metallic structures, such as the sheet-based gyroid, recently used in orthopedic implants. Many implants are subjected to a complex stress environment, making strength verification across different loading modes imperative. This study investigates the effect of both unit cell and build orientation on gyroid structures. Build orientation and unit cell orientation were varied from 0° to 90° in 15° increments to determine the degree of anisotropy of Ti-6Al-4V samples in tension, compression, and torsion. For the relatively isotropic gyroid structure, build orientation was the most influential factor on anisotropy in tension and compression. The samples with 30° build orientation (B30) showed the highest strength across all three loading modes due to the overall print quality and orientation of layers withstanding the applied forces. These results guide the design optimization of 3D printed orthopedic implants with varying build and unit cell orientation.

Interest in electroformed nickel (Ni) molds has continued increasing due to their high precision, low cost and high surface finish. Nevertheless, pure Ni molds still rely on extra surface treatments employing release agents to achieve defects-free demolding and meanwhile, mitigate the residual contamination. To address these issues, lubricant-retaining Ni mold was achieved by doping low surface tension polytetrafluoroethylene (PTFE) nano-fillers into the Ni matrix via electrodeposition. The introduction of surfactant mixtures facilitated the successful incorporation of PTFE into the Ni matrix, causing them to perfectly integrate and form as a whole. Such mold exhibited excellent mechanical performance with the enhanced hardness of 452 HV (2.3-fold increase), low surface roughness of 23 nm in S_a and low surface energy of 28.1 mJ/m² (33.6% decrease), resulting in a maximum reduction of 28.6% in demolding force. This Ni-PTFE mold can withstand more than 1 500 demolding cycles without the need for additional demolding agents or the removal of residual contaminants. Importantly, no PTFE nanoparticles were detected on the produced cyclic-olefin-copolymer (COC) chips, as confirmed by energy dispersive X-ray spectroscopy analysis and Raman spectroscopy, confirming no contamination to the polymer and no lubrication degradation of such mold. Polymer chips produced from such mold displayed well-defined structures and excellent biocompatibility, rendering them suitable for microfluidic applications. Finally, this facile and cost-effective method enables creating a reusable, high-resolution mold with low surface energy, ensuring defects-free demolding for the mass production of polymer parts.

Metal mirrors with ultra-smooth surfaces have a wide range of applications in X-ray and other optics. The fabrication of X-ray mirrors usually requires high-precision turning and grinding, which has a periodic texture with anisotropic characteristics. To obtain a stable low roughness surface over the full-aperture surface, it is crucial to study the evolution law of these textures during polishing. In this article, a model for the evolution of periodic texture roughness based on contact mechanics and fluid micro-cutting has been established. It was found that the fluid cutting stress caused by the periodic texture orientation had a significant impact on the evolution of roughness. When the orientation of periodic texture is perpendicular to the rotation direction of polishing wheel, the contribution of fluid micro-cutting to the evolution of the roughness reaches its maximum. The evolution speed of surface roughness is the fastest. Polishing experiments using single direction rotating wheel on turned electroless nickel plate were performed to verify the theory. The experimental results were in good agreement with the theoretical results. This work shows that the fluid micro-cutting plays an important role in the evolution of periodic texture roughness. It provides useful guidance for full-aperture polishing of anisotropic textures.

The combination of wire electro-discharge grinding (WEDG) and a rotary spindle provides an effective means for the online fabrication of microtool electrodes, thus eliminating secondary clamping errors. However, significant wear of the electrodes occurs during the micro-electrical discharge machining (micro-EDM) process, causing rapid degradation of usability. Therefore, for the practical application of micro-EDM in continuous manufacturing processes, it is essential to integrate electrode wear compensation into the spindle feed function. This study proposes a high-precision spindle head with a combined function of rotation and inchworm feed for micro-EDM with WEDG. The spindle head maximizes tool electrode length utilization with a unique arrangement of upper and lower clamps. The separate control of rotational drive and accuracy, as well as the servo feed for machining gap and inchworm compensation, enhanced the electrode’s rotational and feed precision. The measured radial runout is less than 3.9 μm, and the deviation angle of parallelism error equals 0.019°. Utilizing the tangential feed WEDG process, the diameter consistency of the prepared electrodes is less than 2 μm, and the consistency accuracy of electrodes in repeated production is less than 3 μm. Arrayed Φ 65 μm and Φ 40 μm micro-holes with great dimensional consistency are achieved using prepared Φ 55 μm and Φ 30 μm electrodes, respectively. Moreover, electrodes with noncircular cross sections were prepared to machine square and triangular-arrayed micro-holes with high shape and size accuracy. Using the developed servo scanning micro-EDM technology and a layered depth-constrained algorithm, we machined micro-patterns of gears, stars, and special Chinese characters for “Tsinghua University”, as well as arrayed hemispheres, pentagons, and hexagons. The dimensions and shapes are consistent with the design models, with the least cumulative depth errors less than 2 μm and shape errors primarily arise from the inevitable rounded corners due to electrode radius.

The steel manufacturing industry currently urgently needs highly accurate detection algorithms for electrical connection devices to slow down the time and danger of electrical connections to torpedo cans during high-temperature operations. The fisheye effect and fuzzy features of industrial cameras seriously affect accuracy and effectiveness, hindering the widespread application of object detection algorithms in the manufacturing industry. We propose a feature enhancement preprocessing algorithm for torpedo can electrical devices based on the pyramid structure that resists fisheye effects and serves to detect and locate electrical connection devices. With the aid of this preprocessing algorithm, the detection efficiency and accuracy of state-of-the-art (SOTA) object detection models are significantly improved. Experimental validation confirms the superiority of our method over other SOTA methods. With the application of our preprocessing algorithm, the production capacity of the steel plant increased by 31.8%, and material wastage caused by transportation decreased by 10.9%.

By leveraging the synergy between 3D printing and smart memory materials, this approach allows structures to adapt their shapes, performance, and functions in response to external stimuli. This study primarily investigates the precise control of deformation in single-material structures, offering simplicity and rapid manufacturability compared with multi-material approaches. This study establishes a correlation between the manufacturing parameters and deformation curvature in bilayer actuators using fused deposition modeling (FDM)-4D printing. It further explores how the area ratio of different units within the design plane influences the deformation of the folding functional primitives. An optimization process using the NSGA-II algorithm fine-tunes both the area ratio and manufacturing parameters, achieving a Pareto front that optimizes the deformation of these primitives. Experimental validations confirmed the effectiveness of this method, demonstrating control over the primary deformation within the prescribed parameters while ensuring structural deformation quality. This method was applied to complex structures, such as triangular pyramids and hexahedral shapes, illustrating its practicality. This paper concludes by acknowledging the limitations of this method and proposing future enhancements through machine learning and improved FDM structural models. These advancements are aimed at enhancing the reliability of 4D printed structures, paving the way for their application in transformable vehicles and other advanced fields.

Coverage path planning (CPP) is an essential process in robotic grinding, particularly with the increasing demand for large-scale multiconnected free-form surfaces, such as high-speed rail shells, car shells, and aeronautical parts. Owing to its multi-connectivity, achieving full coverage with a single continuous path is challenging. Additionally, large curvatures make the path spacing difficult to control, leaving some areas uncovered. Existing methods often fail to optimize continuity and coverage rates simultaneously, resulting in redundant tool-feeding and lifting processes that significantly reduce processing efficiency. Thus, a novel method for free-form surface CPP is proposed based on reinforcement learning (RL), which enables the learning of an optimal path with optimized continuity and coverage rates. Specifically, to regulate the path spacing, a uniform grid map is constructed based on the least-squares conformal mapping (LSCM) method, which parameterizes the grinding surface to a two-dimensional (2D) plane with controllable distortion. Furthermore, a CPP-specific evaluation criteria (CEC) is designed to evaluate the path through various key factors, including coverage rate, continuity, and smoothness. Finally, a grinding path is generated using the CEC-guided RL framework. The method was verified through several simulations, and a grinding experiment on a high-speed rail head surface was conducted as a typical application. The results showed high path continuity and coverage rates, demonstrating its potential for addressing CPP problems in different manufacturing scenarios.

A large amount of work in the ultrasonic cutting of honeycomb cores is concentrated in the roughing stage, but the existing roughing path planning methods cannot achieve high efficiency machining. To solve this problem, this paper proposes a novel method that utilizes a straight blade for overlapping V-shaped cuttings. The proposed method reduces the number of disc cutter cuts by reducing the residual height, thereby significantly reducing the overall machining time. By establishing a cutting efficiency model for the traditional and proposed methods, we demonstrate the high efficiency of the proposed method. Additionally, methods for generating tool pre-processing paths are provided for planar, inclined, and curved parts. By analyzing the machining characteristics of the overlapping V-shaped process, we propose corresponding post-processing schemes. Subsequently, the analysis results of the pre-processing and post-processing were integrated and compiled, and a dedicated processor for honeycomb core roughing-path planning was developed using Matlab. Cutting research on the three roughing processes was conducted using the simulation software Vericut, which further verified the efficiency of the overlapping V-shaped process quantitatively. Finally, by comparing the machining effects of the simulation and experiment, we proved that the honeycomb core roughing processor developed in this study could satisfy actual machining requirements.

High-volume-fraction SiC particle-reinforced aluminum (SiCp/Al) metal matrix composites (MMCs) are widely utilized in the electronic packaging of aerospace equipment because of their low density and high thermal conductivity. However, the extremely high hardness of SiC and compact structure of electronic packaging components pose significant challenges to conventional manufacturing techniques. Severe tool wear can reduce the processing efficiency and increase the manufacturing costs. Therefore, this work introduces a fast electrical discharge (ED) milling approach for machining high-volume-fraction SiCp/Al MMCs. This method was successfully applied to the fabrication of gas-film holes. Nevertheless, Ni-based superalloys differ significantly from SiCp/Al, and their material-removal mechanisms and machining capabilities represent core knowledge gaps. Consequently, this study employed an observation setup based on a high-speed camera to capture the gap discharge phenomenon and analyze the machined surfaces and generated debris. This analysis revealed the material-removal processes and mechanisms under two processing conditions with pulse durations of 50 μs and 500 μs. Additionally, the capability of fast ED milling to process high-volume-fraction SiCp/Al MMCs was initially verified through sample machining. The experimental results demonstrated that this method could create parts with complex and precise geometries, achieving satisfactory results in terms of machining accuracy and surface quality. Dimensional errors could be controlled within ± 50 μm, and the average surface roughness was less than 3 μm.

Reaction bonded silicon carbide (RB-SiC) is widely used in the aerospace industry because of its excellent physical and mechanical properties. However, owing to its high hardness and wear resistance, achieving the precise machining of RB-SiC has become a challenge. Ultrasonic-assisted grinding technology has the potential to significantly enhance machining efficiency and minimize surface damage when machining hard and brittle materials. This method is widely considered the optimal approach for machining RB-SiC. Investigating the material-removal mechanism in ultrasonic-assisted grinding is crucial for promoting the application of this technology. A finite element simulation and an experiment on the axial ultrasonic-assisted scratching of RB-SiC were performed, and the material-removal behavior in the ultrasonic-assisted grinding process was studied. Changes in the cross-sectional profile, scratch force, material-removal ability, and surface morphology of the scratches at different scratch depths and ultrasonic amplitudes were compared and analyzed. The effects of axial ultrasonic vibration on the removal behavior of RB-SiC materials were discussed in combination with the strain rate and crack propagation behavior. Compared with conventional scratching, axial ultrasonic-assisted scratching effectively decreased the scratching force and increased the material-removal ability. The maximum reduction value of normal scratching force was 56.73%. The material-removal ability could even reach 24.04 times, which could significantly improve the processing efficiency. The research conducted in this study offers theoretical guidance for understanding the mechanism of damage formation and suppression strategies to control it in the ultrasonic-assisted grinding of RB-SiC.

Zirconia ceramics are often used in electronics, aerospace, biomedicine, and other fields because of their excellent mechanical and optical properties; however, as they are hard and brittle materials, they are highly susceptible to cracking and chipping during processing. Ultrasonic elliptical vibratory-assisted cutting (UEVC) is a promising ceramic processing technology that addresses existing problems in materials processing. In this study, the critical depth of cut (

) of zirconia ceramics was predicted using two models, focusing on the influence of the circular edge of the tool and tool front angle in the actual machining process. Subsequently, a model was established based on the specific cutting energy to predict the

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of zirconia ceramics in UEVC machining. A simulation software was used to simulate the variable depth of zirconia ceramics using the constitutive improved Johnson-Holmquist ceramic (JH-2) model. Finally, the relationship between the cutting speed and

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of zirconia ceramics under conventional cutting (CC) and UEVC machining was investigated using scribing experiments. The results showed that the

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of zirconia ceramics decreased nonlinearly with increasing cutting speed. The

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of zirconia under CC is 0.8 μm, whereas the

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values of zirconia under UEVC machining are 1.79, 1.75, 1.45, and 1.3 μm with a maximum increment of 124%, which corroborates the results predicted by the model, verifying the effectiveness of the model and simulation.

Green manufacturing prioritizes quality, efficiency, low energy consumption, and cleanliness in milling technology. Consequently, a data-driven optimization method was proposed for configuring multiple technological parameters in frozen sand molds, to reduce energy consumption and tool wear during processing. Initially, a power composition model for frozen sand mold processing was developed, based on an analysis of the relationship between the energy consumption and various actions during different processing states. Subsequently, using Archard wear theory, a discrete element model for cutting frozen sand molds was established to investigate the wear characteristics of flat-end milling tools influenced by multiple factors. The cutting wear of the frozen sand mold presents as a continuous abrasive wear form at polycrystalline diamond (PCD) cutting edge and the side end of the tool shank. Comprehensive experiments were conducted to develop a Kriging energy consumption model and radial basis function model for tool wear based on varying cutting technological parameters. The accuracy of the developed surrogate model was confirmed using an optimal Latin hypercube experimental design and leave-p-out cross-validation (LPOCV). Analysis of the technological parameters revealed that the milling speed and feed rate per tooth significantly affected both milling power and tool wear. Finally, the surrogate model was integrated with particle swarm optimization and a genetic algorithm to solve for the Pareto frontier and identify the optimal combination of cutting parameters. The optimized parameters of the multi-objective model reduced the milling power by 29.88% and tool wear by 18.18% during the processing of frozen sand molds. The models proposed for the milling power and tool wear in this study are accurate and reliable. By revealing the mapping relationship among the cutting power, tool wear and various cutting parameters, the proposed model can serve as an excellent platform for the energy-saving manufacturing of frozen sand molds.

Laser-directed energy deposition (LDED) has emerged as a primary technology for the direct additive manufacturing of melt-growth ceramics (MGCs). However, cracking during fabrication severely limits further development of this technology. This study investigated a new holistic high-temperature-assisted method for LDED to solve the cracking problem. This method mitigates the high temperature gradients caused by the low thermal conductivity of the material during fabrication, thereby suppressing crack formation. The LDED of Al₂O₃/ZrO₂ eutectic ceramics was performed at a holistic auxiliary temperature of up to 1 273 K, and the crack-suppressing effectiveness and mechanism were verified by combining numerical simulations and experiments. The results demonstrated that holistic high-temperature assistance significantly mitigated cracking in Al₂O₃/ZrO₂ eutectic ceramics fabricated via LDED. At an auxiliary temperature of 1 273 K, the stress level in the fabricated sample was reduced by an order of magnitude compared to that at room temperature. Consequently, the lengths and densities of the cracks in the fabricated samples decreased by 47.6% and 55.6%, respectively. This study confirmed that the holistic high-temperature-assisted LDED method could play an important role in the additive manufacturing of low-ductility materials.

Ceramic cores play a significant role in determining the cavity structure of hollow turbine blades during precision casting. However, the sintering process in preparing ceramic cores may result in shrinkage and deformation due to high temperature, presenting significant challenges in controlling the dimensional accuracy of ceramic cores. In this work, we develop a framework based on deep learning to predict the three-dimensional deformation of ceramic cores during sintering under varied sintering parameters. A finite element thermo-elasto-viscoplastic model is developed to compute sintering deformation and generate the three-dimensional deformation database. The numerical model is validated using a sintering experiment, and the maximum deviation in the deformation between the numerical and experimental results is 0.383 mm, which is 3.19% relative to the diameter of the largest inscribed circle of the ceramic core section, and satisfies the precision requirement of the third level of dimensional casting tolerance grade (DCTG3). The developed framework slices each of the three-dimensional shapes of the sintered ceramic core in sequence to obtain the two-dimensional image data for training the deep learning network. A parameter-embedded U-net network is established and trained to learn the intricate relationship between sintering parameters and deformation in sliced images. A VTK reconstruction algorithm is applied to the slice sequence to restore the predicted images from the U-net to the three-dimensional shape of the ceramic core. A metric for evaluating the model accuracy based on the error of deformation prediction (EDP) is proposed specific to the image character of ceramic core sintering, and the score of EDP for the developed U-net is 4.31%, indicating a high accuracy in predicting the sintering deformation in sliced images. An unseen combination of process parameters is numerically computed, and the entire three-dimensional deformation is compared to the prediction from the developed framework. The result shows that the relative maximum deviation in deformation is 2.93%, demonstrating the overall good performance of the developed framework in predicting sintering deformation.

Plastic injection molding (IM) is a typical multiple-input multiple-output (MIMO) complex manufacturing process widely used in modern industrial production. Determining the critical process parameters and establishing the MIMO quantitative relationship between them and the plastic product quality are two fundamental problems in IM process decision analytics. Focusing on high-dimensional process parameters and multidimensional quality indicators in the IM process, this study developed a machine learning-assisted surrogate model that integrated joint mutual information (JMI) and multi-output support vector regression (MSVR). Firstly, a JMI-based sequential search algorithm was developed to measure the association relationship between each process parameter and a multidimensional quality indicator set, and automatically select the critical process parameters of the IM process. It can effectively filter redundant information from raw industrial datasets and provide essential input features for the development of surrogate models. The MSVR model was then developed to capture the MIMO quantitative relationship between the selected critical process parameters and multidimensional quality indicator set. The proposed method can preserve complete independent variable information and avoid losing the relevance of data during training. Finally, the effectiveness of the model was verified using a real-world IM process dataset.