This paper presents a lossless compression and recovery method for locally dense sparse photomask (LDSP) for ultraviolet lithography using irregular block-compressed row storage (IBCRS), which achieves an equilibrium reinforcement design for both storage amount and storage space. According to Moore’s Law, in laser three dimension printing (3DP), as well as in the semiconductor and chip manufacturing fields, the fabrication resolution is continuously refined for ultra-high-resolution manufacturing of complex curved surface components, resulting in an exponential increase in the generated metadata in terms of storage amount and storage space. As the first step, a photomask, which is a large-scale sparse matrix (LSM), is generated from variously sized irregular 3D manifolds using adaptive multilayer slicing strategies. Based on the typical lossless block compressed row storage (BCRS) method, irregular rules are proposed to tackle LSM, whose domain is locally dense via a neighborhood topology. The Hough transform of LSM was employed to determine the optimal transpose for the IBCRS. Using the IBCRS, the photomask of the LSM can be converted into lossless equivalent vectors. Large-scale matrix computations can be directly performed using compressed vectors. Original native photomasks can be recovered or reconstructed solely from compression vectors. The numerical example proves that in terms of the storage amount, which reflects the time complexity of the algorithm, the proposed IBCRS method performs better than the BCRS. Finally, a physical experiment was conducted to validate the IBCRS via the digital light processing (DLP) technique. The experimental results proved that the IBCRS had important applications in manufacturing variable-size devices with high precision, on the millimeter, micron, nano, and even atomic scales, by image-like lossless compression in the case of wireless encryption for space 3D printing, extremely high resolution manufacturing, etc.

The trade-off between resolution and the number of measurements, along with the time-consuming and memory-intensive demands of traditional Fourier ptychography (FP) systems, has long posed challenges for the efficient and widespread application of this highly scalable quantitative phase imaging (QPI) technique. While recent approaches aim to improve imaging speed and reduce data sampling, they often struggle to maintain a high resolution. This is particularly true for images of thick samples. Consequently, these methods typically fail to simultaneously achieve reduced data sampling and high-resolution measurements. In this study, a resolution and efficiency enhancement scheme is introduced that integrates Fourier light-field microscopy (FLFM), FP, and multi-slice neural network (MSNN) to overcome these limitations. This system significantly reduces the amount of image data required for rapid three-dimensional (3D) imaging while preserving lateral measurement accuracy by replacing traditional microscopes with FLFM and incorporating encoded multiplexed illumination and aperture synthesis from FP. The refocusing capability of FLFM combined with an MSNN improves the vertical resolution, enabling more effective imaging of thick samples. This FLFM-based FP imaging system achieves a six-fold increase in data acquisition speed and a two-fold resolution improvement in both the lateral and longitudinal directions, compared to those of traditional FP systems. These improvements enable faster and more precise acquisition of structural information, particularly for thick samples. This study offers a practical and cost-effective solution to the limitations of traditional FP systems by providing a superior 3D measurement approach.

Powder bed fusion (PBF) is a leading metal additive manufacturing technique capable of producing complex, high-precision components with superior material efficiency. However, the technology’s widespread adoption is limited by the presence of microstructural defects such as porosity, cracks, and textured columnar grains. These defects, originating from both feedstock characteristics and process instabilities, significantly undermine the structural integrity, fatigue resistance, and reliability of manufactured parts. To systematically address these challenges, this review classifies microstructural defects into feedstock-induced and process-induced categories, examining their formation mechanisms and impact on mechanical properties. Feedstock-induced defects, including satellite formation, internal porosity, and surface contamination, stem from irregularities in feedstock morphology and handling conditions. Process-induced defects, such as lack of fusion pores, keyhole porosity, and cracking, are strongly correlated to thermal gradients, melt pool dynamics, and energy density variations. Furthermore, this work examines defect mitigation strategies, including feedstock optimization (powder atomization, particle morphology control), process parameter refinement (energy density regulation, scan strategy optimization), and post-processing techniques (hot isostatic pressing, surface treatments). A summarized classification of defects, their mechanisms, effects on mechanical properties, and mitigation strategies is provided as a comprehensive reference for researchers and practitioners. Through the integration of experimental findings, multi-physics modeling, and advanced in-situ diagnostics, this review establishes a framework for understanding defect formation and its influence on process-structure-property relationships. The insights provided address current limitations in defect modeling and control, guiding future research toward achieving higher repeatability and scalability in industrial applications.

Anisotropic pitting mechanisms were investigated during electropolishing (EP) of Zr-based bulk metallic glass (BMG) surfaces in a chloride-containing alcohol-based electrolyte. Electrically discharge-machined (EDMed) and mechanically polished (MPed) BMG surfaces were selected as the typical preprocessing surfaces. NaCl-ethylene glycol solution was selected as the electrolyte because of its ability to avoid solvent decomposition and form high-viscosity complexes on surfaces to lower the surface roughness. The surface roughness and morphology of both specimens under varying EP parameters were examined and compared. With the optimized parameters, the recast layer of the EDMed surface was removed, and a decrease of more than 50% in the roughness values was achieved for both types of surfaces. However, anisotropic pitting also occurred on the surfaces subjected to EP. Further findings showed that pitting was induced by the notches distributed in the Cu-rich film covering the EPed surfaces, which resulted from the distinctive dissolution rates of different elements. For the EDMed surface, the notches were related to the Zr-rich crystals observed beneath the recast layer, whereas for the MPed surfaces, such notches appeared directly during the gradual dissolution of the surfaces. The Cu₂O product serving as a protective layer for the covered region was detected at the pitting boundary on both types of surfaces. The region without such protection gradually dissolves at high voltages. In addition, deep slots were formed at the boundaries of the pits on the MPed surfaces under the corrosion of chloride ions, which were concentrated at the boundaries. Finally, dissolution-pitting models were proposed to describe the anisotropic pitting mechanisms on BMG surfaces in non-aqueous solutions.

Internal defect is a general problem in cross wedge rolling, which usually diminishes mechanical properties and even leads to the failure of rolled workpieces. In this study, variation of characteristics (stress state, strain path, etc.) in rolled workpieces during deformation was investigated through numerical simulation. Then, microstructural evolution of internal defects was clarified by quasi-in-situ experiments. Finally, process parameter intervals forming rolled workpieces with internal defect-free and good mechanical properties were confirmed. Results show that rolled workpieces with good surface quality can be produced although they experience complex strain paths and stress states during deformation. Further, quasi-in-situ experiments revealed that evolution process of internal defects. Namely, defect nucleates at interface between inclusion and matrix, and grows into a micro-pore. As section shrinkage increases, micro-pores gradually expand and coalesce, forming larger micro-holes and potentially leading to macro-cracks. Among them, propagation process is accelerated by fine carbide particles served as expansion channels. In addition, rolled workpieces with good internal and external quality can be formed at a heating temperature of 970–1 020 °C and a rolling velocity of 400 mm/s. Specially, area proportion of defects was counted quantitatively to characterize damage degree. At a fixed velocity of 400 mm/s, as temperature increased from 950 °C to 1 050 °C, area proportion changed from 1.4% (950 °C) to 1.1% (1 000 °C) and 1.3% (1 050 °C). When temperature is settled at 1 000 °C, changes in velocity (300–500 mm/s) can also cause similar trends in area fraction variations. Consequently, rolled workpiece obtained through process optimization can provide a 5.1% increase in tensile strength while maintaining hardness of central region.

NiTiNb is a wide-hysteresis shape memory alloy (SMA) with great application potential in many industries, such as the aerospace and biomedical fields. However, a series of hot working processes, from casting to subsequent hot working processes, such as forging, heat treatment, and machining, are required owing to the limitations of long cycles, high energy consumption, and limited product structure. Meanwhile, during traditional equilibrium solidification, many eutectic structures composed of matrix and β-Nb phase are formed. The plastic deformation of the large β-Nb phase in the eutectic region may seriously deteriorate the shape memory performance. However, the eutectic structure is inherited, and its influence on shape memory properties cannot be eliminated by subsequent traditional hot workings. In this study, considering non-equilibrium solidification in 3D printing, laser powder bed fusion (LPBF) was attempted to rapidly fabricate NiTiNb alloys. Excellent shape memory performance with a divorced eutectic structure could be tailored using suitable LPBF parameters. It was observed that, in terms of the recovery rate, recovery stress, and thermomechanical cycle stability, the LPBF-NiTiNb alloys were superior to the traditional cast, forged, and extruded NiTiNb alloys. The recovery rate of the LPBF-NiTiNb alloy after 6% pre-strain at low temperatures reached approximately 98.8%, which was 21.5% higher than that of the as-cast alloy. The maximum recovery stress of LPBF-NiTiNb alloy reached 420 MPa, which was 55.9% and 15.4% higher than traditional forged and extruded alloys, respectively. The improved shape memory performance was essentially caused by the solidification difference between the non-equilibrium rapidly solidifying additive alloys and equilibrium solidifying as-cast alloys. To reduce costs and shorten fabrication processes using additive manufacturing, this finding provides a new perspective for tailoring the microstructure and improving the shape memory performance of NiTiNb alloys.

Compared to meta-aramid honeycombs (MAHs), para-aramid honeycombs (PAHs) exhibit superior mechanical and physical properties and can therefore be used in more demanding environments. However, the excellent properties of PAH also bring great challenges to the traditional machining methods, often resulting in terms of large burrs, delamination, tearing, and cell collapse of the honeycomb, as well as severe tool wear problems. These challenges severely limit the application of the PAH. Therefore, investigating effective machining methods for PAH and optimizing the machining quality by determining the influence laws of the process parameters are crucial. In this paper, series of single factor orthogonal experiments were performed to study the effects of machining parameters on ultrasonic cutting characteristics between PAH and MAH in terms of cutting force, cutting temperature and surface quality by disc cutter. Furthermore, this study compared the microscopic ultrasonic cutting processes of PAH and MAH, analyzed the impact of aramid fiber differences on the honeycomb walls fracture process, and revealed the reasons for the differences in experimental results between PAH and MAH. Results proved that PAH exhibited poorer cutting performance compared to MAH: the cutting force and cutting temperature of PAH were both higher than those of MAH, and the machined surface was rougher. The processed surface damage of PAH predominantly manifests as clusters of long uncut fibers, whereas MAH is mostly short burrs. Increasing the ultrasonic amplitude can reduce the cutting forces for PAH and MAH, with a particularly greater reduction in force for PAH. The present study can be used as basis for comprehensive understanding of the differences in the cutting performance and mechanisms of PAH and MAH and optimization of machining parameters for ultrasonic cutting by disc cutter.

Marble is a typical hard and brittle material, widely used in construction, home decoration, and other fields due to its high strength, hardness, and excellent wear resistance. Diamond wire sawing is an innovative technology applied in the stone cutting industry, significantly reducing processing noise and kerf width, minimizing material cutting loss, and lowering dust pollution, thereby promoting the green development of the industry. This study conducted both single-factor and orthogonal experiments on the diamond wire sawing of marble to investigate the influences of workpiece feed speed, diamond wire speed, and cut workpiece length on cutting force, surface roughness, and waviness. The results showed that as the workpiece feed speed and cut workpiece length increased, diamond wire speed decreased; the cutting force increased; and the size and number of brittle pits and cracks on the cut surface increased, leading to a gradual increase in surface roughness and waviness. The influence of each factor on cutting force, roughness, and waviness ranked from the largest to the smallest as follows: workpiece feed speed, cut workpiece length and diamond wire speed. The findings provide experimental evidence for understanding the diamond wire sawing process of marble.

Bone cutting is a common procedure in surgery, during which conventional rotary cutting causes extensive damage to the bone while single ultrasonic cutting, as a newer tool, results in minimally invasive injury but has obviously low efficiency. This study aimed to achieve low-damage, high-efficiency bone cutting using a newly developed surgical rotary ultrasonic (RU) handpiece coupled with ultrasonic vibration and rotary cutting. To solve the clinical miniature size and power limits, a quarter-wave barbell ultrasonic horn with a mid-reduction structure was designed based on the vibration theory. It enhanced the vibration amplitude output by 22% compared with a common stepped horn. A new non-contact rotary transformer with “T+U” shaped cores was developed with a higher coupling coefficient of 0.95 and transmission efficiency of 94% compared with common industrial transformers. Handpiece performance was evaluated in terms of vibration responses and cutting characteristics during cortical bone cutting. The results demonstrated that the new tool had good vibration characteristics with the expected amplitude and frequency, even at a low power of approximately 1 W, which was less than 1/100 of that used in industrial RU tools. Compared with conventional rotary cutting, the new tool significantly reduced cutting forces by 32%–44% without losing cutting efficiency and diminished surface chipping damage in the bone, which was especially important for low-trauma surgery. This study advances the bone cutting processes for high-quality surgery by developing a new RU surgical tool.

Ultrasonic cutting is an advanced machining technology applied to Nomex honeycomb cores. However, during the finishing process, machining errors exceeding the allowable tolerances occur owing to the lack of an effective path planning method. This study addresses this issue by examining tool path planning for finishing off various typical honeycomb core features. Initially, a theoretical and experimental comparison of the residual heights of the honeycomb core plane features revealed that machining accuracy was superior along the double-wall direction to that along the perpendicular double-wall direction. Thereafter, the theoretical model of the plane features was used to predict the residual heights of the lead angle in the range of 0°–0.5° along the double-wall direction. In addition, theoretical models for residual height were derived for convex and concave surface features using transverse and longitudinal cutting methods, respectively. This study introduces algorithms for tool interference checking and tool posture adjustment to determine the critical lead angle preventing interference. By integrating these algorithms, a honeycomb core finishing path planning processor was developed on the MATLAB platform. Finally, simulations of the solid model were performed using VERICUT software to verify the feasibility of the proposed algorithms. Actual machining of honeycomb core feature parts demonstrated that the developed finishing processor had the ability to achieve high-precision results.

The data dependence of deep learning models for wind power forecasting presents a significant challenge for newly built wind farms, which may lack sufficient data for effective model training. Overcoming this limitation by amalgamating data from other wind turbines introduces an inherent risk of privacy leakage. To address this issue, we introduced a novel hybrid model for wind power forecasting, specifically designed to address data privacy preservation. This model integrates a temporal convolutional network with a transformer encoder, harnessing the strengths of both components. The temporal convolutional network extracts local temporal patterns, whereas the transformer encoder captures the intricate time dependencies between these patterns. To address the data privacy risks caused by traditional centralized forecasting methods, a federated training strategy is implemented to train a collaborative knowledge-sharing model, ensuring that the original data from the source domains remain confidential and undisclosed. The experimental results validate the effectiveness of the proposed model, showing significant improvements in forecasting performance. When federated with other wind turbines, the average mean absolute error and the root mean square error of six wind turbines are 4.624 6 and 6.245 7, respectively. In contrast to traditional centralized training methods, the proposed federated learning method, which was applied to inland wind turbines, achieved almost no increase in losses.

To elucidate the cryogenic deformation behavior and its effect on microstructural evolution on the mechanical properties of titanium alloys, this study selects the Ti-6Cr-5Mo-5V-4Al (Ti6554) alloy as the research object, carrying out cryogenic tensile and impact tests, to obtain the stress strain curves and mechanical indicators and develop a modified Johnson Cook constitutive model. Scanning electron microscope (SEM), electron backscattered diffraction (EBSD) and transmission electron microscope (TEM) are utilized to analyze the morphological evolution, fracture mechanism, texture components, Schimidt factor (SF), and grain orientation spread (GOS). The results indicate that the {0001}<11-20> slip system remains activated for the α while these {110}<111>, {112}<111>, and {123}<111> systems remain the primary deforming components at lower temperatures. The increase of critical resolved shear stress (CRSS), extensive pile-up of dislocations, and geometrically necessary dislocations (GNDs) resulted from the inhomogeneous deformation can improve strength, while deterioration of ductility may be caused by the reduced number of activated slip systems and retarded dislocation mobility, incompatible deformation between the α and β phases, and dislocation accumulation and stress concentration. This work can provide a deep insight into understanding deformation behaviors and microstructural mechanism of Ti6554 alloy subjected to low-temperature tension.

The degradation or failure of spring performance due to stress relaxation can significantly impact the normal operation of the entire system. Physical models are often used to fit the degradation law of spring stress relaxation. However, the error in the prediction process has not yet been emphasized. Therefore, an error compensation model based on PSO-TCN-Attention is proposed in this study. PSO is applied to optimize the TCN-Attention hyperparameters for data at different temperatures. The prediction model is optimized to learn the difference between the empirical equations and the true value, and the prediction error is then compensated for in the empirical equations to achieve a more accurate prediction of spring degradation. The results indicate that the PSO-TCN-Attention model predicts optimally at four temperatures compared to the TCN, RNN, and LSTM models as well as the empirical equation and delay function models. Based on this, the spring’s life at a failure threshold of 0.95 is extrapolated, and the life distribution is obtained. Additionally, the relaxation mechanism of the spring is analyzed using EBSD and TEM at different temperatures before and after pressurization. The results indicate that the relaxation mechanism of the spring is mainly due to dislocation and slip. The proposed algorithmic model effectively describes the spring degradation trend, suggesting its potential for predicting spring performance degradation.

The abilities to describe the fracture behavior and calibrate the relevant parameters are essential factors in evaluating ductile fracture criteria of titanium alloys. In this study, 14 different shapes and notched specimens were designed for uniaxial tensile and compression experiments to characterize their ductile fracture behaviors. Based on the analysis of plastic behavior and fracture mechanism, a mixed hardening model, the Von Mises yield criterion and DF2016 fracture criterion were established, respectively. A parameter-identification method based on machine learning was proposed to improve the parameter calibration of the ductile fracture model. The results showed that the DF2016 fracture model accurately predicted the damage initiation and fracture process of the forged TC4 titanium alloy during the forming process. The machine-learning method avoided extracting different stress state evolution processes and large amounts of data from the numerical model of the calibrated specimens. The combination of the semi-coupled fracture model and parameter-identification method provides a new method that alleviates the difficulty of balancing parameter calibration and the ability to characterize the ductile fracture criteria.