Equipment reconfigurability and equipment degradation endow scheduling in Matrix Manufacturing System (MMS) with enhanced physical significance and more complex scheduling constraints. Equipment reconfigurability introduces setup time as the third temporal parameter in addition to traditional machining duration and logistics time, while equipment degradation-induced dynamic variability and real-time characteristics of real processing time supersede theoretical processing durations. This study innovatively addresses equipment degradation in MMS by proposing a Scheduling Problem in MMS with Real Processing Time (SPMMS-RPT). First, a mathematical model for SPMMS-RPT is constructed, comprehensively considering the impacts of equipment degradation and predictive maintenance (PM) on temporal parameters. Subsequently, a Double Deep Q Network (DDQN)-based solving algorithm designed, featuring a degradation-aware state space, a discrete scheduling rulebased action space, and a reward function incorporating virtual feedback. Experimental validation demonstrates the effectiveness of the proposed model and algorithm. Results indicate that the DDQN-based SPMMS-RPT solution achieves superior makespan and shorter computation time compared to conventional methods.

Bolted joints are critical components in wind turbine structures, particularly at the interface between the hub and pitch bearings. Loosening or failure of these joints can lead to bolt fatigue fractures, posing serious safety risks and causing significant economic losses. To address this issue, a real-time online monitoring system was developed to visualize and track the preload status of multiple bolted joints in wind turbines. The system integrates an ultrasonic acquisition module, data transmission module, preload calculation module, and data processing module. The ultrasonic module measures the time-of-flight (TOF) variation within each bolt, which correlates with preload changes. To ensure reliable performance under fluctuating outdoor temperatures, a temperature compensation model was introduced in the preload calculation module. The system's accuracy and reliability were first validated in laboratory conditions. Subsequently, it was deployed on a utility-scale wind turbine in Shanxi Province, China, where it continuously monitored the preload of bolts connecting the hub and pitch bearings over a seven-month period. The results confirmed that the system could reliably detect preload variations in real time. Future improvements will focus on enhancing ultrasonic signal strength in corroded bolts, simplifying the calibration process, and reducing system cost.

Three-Dimensional (3D) hybrid carbon/aramid fiber composites have the potential to overcome the interlaminar weaknesses of traditional two-dimensional composites, but their low-velocity impact behavior remains insufficiently understood. This study aims to elucidate the damage mechanisms and impact resistance of 3D hybrid composites under low-velocity impact. To achieve this, composite samples with various Carbon Fiber (CF) and Aramid Fiber (AF) hybrid architectures were fabricated using controlled compaction techniques. Low-velocity impact tests were conducted to assess dynamic mechanical responses, energy absorption, and damage patterns. The results indicate that hybridization significantly improves impact resistance, with twisted CF/AF configurations exhibiting the highest energy absorption and damage tolerance. Incorporating aramid fibers in z-direction was found to effectively reduce delamination and enhance structural integrity. These findings provide guidance for the design and optimization of 3D hybrid composites, offering new insights for high-performance applications in aerospace and defense requiring superior impact durability.

With the emergence of Industry 5.0, human–robot collaboration (HRC) has attracted increasing attention in intelligent manufacturing. In assembly scenarios, HRC aims to combine the flexibility, experiential knowledge, and contextual judgment of human operators with the high precision, payload capacity, and consistent operational performance of collaborative robots, thereby improving assembly efficiency, quality, and adaptability. In recent years, a growing number of review studies on human–robot collaborative assembly (HRCA) have been published. However, existing reviews have largely focused on specific technologies, application scenarios, or localized issues, leaving further room for discussion on the system-level mechanisms among the key stages of perception, decision-making, and execution in the process of HRCA. To further extend the perspectives offered by existing reviews, this paper provides a systematic review of HRCA. First, it clarifies the definition, main characteristics, core research themes, and evolutionary trajectory of HRCA within assembly systems. Then, following the “perception–decision–execution” technical chain, it systematically reviews and analyzes the key enabling technologies and representative methods for HRCA. Finally, it identifies the major challenges currently facing HRCA and discusses future research directions toward symbiotic HRCA. This paper aims to provide a structured reference for researchers and engineering practitioners and to support the large-scale deployment and value realization of HRC technologies in assembly scenarios.

Riveted joints are widely used in aerospace, automotive manufacturing, and other fields, but their connection performance is significantly affected by interfacial fretting wear. This study focuses on the riveted joints between Mg alloy and 10B21 steel: first, finite element simulation is adopted to guide the selection of experimental parameters. Then, tests are carried out using a fretting wear device. Finally, the characteristics of interfacial fretting wear and the damage suppression mechanism of MoS2 films are explored. The results indicate that an increase in displacement amplitude exacerbates wear, with the wear mechanism transitioning from single abrasive wear to the synergistic effect of multiple mechanisms. MoS2 films can optimize interfacial performance, reduce wear, and prolong fatigue life, thereby providing theoretical support for improving the connection quality of riveted joints.

Precision dual-reflective optical systems are extensively utilized in spaceborne remote sensing and guided detection applications. Imaging quality, a critical performance parameter, is influenced not only by the fabrication precision of optical components but also, crucially, by the accuracy of system assembly. During operational deployment, these systems are subjected to complex environmental conditions, including temperature fluctuations, vibrational stresses, and microgravity. These factors collectively affect mirror stress distribution, disrupt optical axis alignment, and introduce component positioning errors, all of which contribute to the degradation of imaging performance. This study presents a comprehensive simulation framework that integrates optical assembly modeling with imaging performance analysis, explicitly accounting for mirror surface figure errors and pose deviations. Imaging quality is assessed using energy concentration metrics. A convolutional neural network (CNN) is employed to extract spatial features from imaging distributions, while Latin hypercube sampling (LHS) is utilized to generate pose deviation scenarios. The coupled simulations yield a multiphysics dataset, which supports the development of a multilayer perceptron (MLP) surrogate model incorporating Sigmoid activation functions to map deformation states to imaging performance indicators. Leveraging the outputs of multiphysics simulations, the proposed model systematically evaluates imaging performance under the isolated and combined influences of thermal, vibrational, and microgravity environments. The results indicate that the developed MLP achieves the highest prediction accuracy among the models considered, with an average error of 3.26

Many non-contact clearances always exist within the contact interface when two rough surfaces are compressed. These continuous clearances form three-dimensional leakage channels, which are closely related to static seals. Considering the self-affinity and multi-scale characteristics of rough surfaces, this paper proposes a numerical solution framework for multi-scale three-dimensional leakage channels for the first time. First, an elastoplastic multi-scale contact algorithm is proposed by introducing magnification-based multi-scale contact criteria into semi-analytical contact computation. The rough surface scale is dynamically adjusted according to the degree of contact. Second, a reverse method is further proposed to reconstruct the real distribution of contact clearances on the basis of the principle of equivalent deformation superimposition. Subsequently, a dual-layer search algorithm for three-dimensional leakage channels is developed, which comprehensively accounts for the boundaries of rough surfaces and enables accurate identification of leakage pathways. X-ray computed tomography (CT) is then employed to measure non-contact clearances at metal contact interface for validation. The results indicate that the pore overlap ratio between the numerical simulations and experimental measurements is about 80 %. Given the potential sources of error inherent in the experimental procedure, these results are deemed sufficient to confirm the accuracy and reliability of the proposed method. Overall, the proposed numerical method for modeling multi-scale three-dimensional leakage channels holds significant value for engineering applications such as sealing, lubrication, and microfluidics.

This study utilizes both experimental and numerical methods to investigate the fretting fatigue failure of TC4carbon fiber reinforced plastic (CFRP) riveted single-shear lap joints under varying CFRP thicknesses and cyclic loads. It focuses on analyzing the dynamic response, the evolution of the contact state, and the failure mechanisms of these joints. Findings indicate that the fatigue testing process consists of two distinct stages. In the partial slip state under low loads, the interfacial temperature remains at approximately 30 degrees C, and fretting fatigue failure initiates on the CFRP surface. During the gross slip state under high loads, the interfacial temperature rises to around 58 degrees C, and the combined effects of carbon fiber structural damage and matrix bond breakage result in macroscopic wear of the CFRP. Increasing the thickness of CFRP significantly prolongs its fatigue life. For the same load half-amplitude, the fatigue life is extended by approximately 2000 times. Therefore, enhancing fatigue performance at low loads is achievable by modifying the thickness, but high loads necessitate the use of hightemperature-resistant composite materials.

This study investigates the fretting wear behavior of Inconel 718 alloy fabricated by selective laser melting (SLM) with different building orientations. The microstructures of specimens fabricated in two distinct orientations were first characterized using optical microscopy (OM) and scanning electron microscopy (SEM). Subsequently, fretting wear tests were conducted under different slip regimes, namely the partial slip region (PSR), mixed slip region (MSR), and gross slip region (GSR). The results reveal that the horizontally built SLM Inconel 718 alloy consists predominantly of columnar grains inclined at approximately 67 degrees, while the vertically built specimens exhibit a typical fish-scale microstructure. The specimen fabricated along the scan direction (SD) showed better wear resistance than the specimen fabricated along the build direction (BD) in the PSR and GSR. The SD specimen had a wear volume about 1.5 times that of the BD specimen in the MSR, along with significant material transfer inside the contact region. At this point, delamination primarily governed the fretting wear mechanism of the BD specimen, accompanied by adhesive and oxidative wear. In contrast, subsurface crack initiation and propagation were observed in the SD specimen, where the fretting wear mechanism was fatigue crack growth, along with adhesive and oxidative wear. These results indicate that the wear mechanism exhibits significant anisotropy.

The assembly process of complex aerospace products such as satellites involves multi-project parallelism and multi-resource sharing, which can be modeled as a type of resource constrained multi-project scheduling problem (RCMPSP). Existing research on RCMPSP has predominantly prioritized time-based optimization, while frequently overlooking practical considerations such as resource utilization efficiency. This paper investigates a new multi-objective RCMPSP for complex product assembly process considering discrete time-resource leveling optimization. To solve the problem, we propose an enhanced non-dominated sorting genetic algorithm (NSGA II) driven by general variable neighborhood search (GVNS). The novelty of the algorithm lies in its improved initialization method based on the NEH heuristic, which enhances convergence. Additionally, two new neighborhood structures and population evolution mechanisms are designed. Pareto frontier decision-making is performed based on the entropy weight TOPSIS method. Finally, computational experiments are carried out based on MPSPLIB and engineering case. Experimental results show that the proposed GVNS-NSGA II outperforms the standard NSGA II, the improved MOEA/D and JAYA in terms of scheduling results, quality of Pareto solutions, coverage of solution set, and IGD metric. The proposed model and algorithm provide a decision-making tool for enterprises to improve production efficiency and resource stability in complex product assembly.

Crimping lock nuts are widely used in critical structures to resist vibration-induced loosening, but their tribological reliability under high-temperature service remains unclear. In this study, the loosening behaviour and damage evolution of crimping lock nut joints under transverse vibration were systematically investigated at room temperature and 600 degrees C, with normal nut joints used as a reference. Preload relaxation, dynamic response, frictional energy dissipation, and post-test surface and fracture analyses were conducted to clarify the failure mechanisms. The results show that elevated temperature significantly accelerates preload relaxation in both joints. Nevertheless, the crimping lock nut joint retains a clear anti-loosening advantage, showing a two-stage attenuation behaviour and a higher residual preload than the normal nut joint. This improved performance is mainly attributed to the radial elastic constraint provided by the crimped structure, which delays slip accumulation and stabilizes interfacial contact during cyclic loading. However, high temperature also intensifies interfacial oxidation, material transfer, and composite wear, causing damage to shift from the non-locking areas to the locking areas. At 600 degrees C, failure of the crimping lock nut joint is governed by coupled degradation involving oxide film evolution, surface damage accumulation, preload relaxation, and fatigue fracture.

To investigate the effect of temperature on the fretting wear behavior of the silver layer on GH2132 superalloy for aerospace fastener applications, this study examined the fretting characteristics of GH2132-Silver against GH4169 at room temperature, 300 °C, and 600 °C. The results show that the dominant wear mechanisms within the slip region vary with temperature: at room temperature, abrasive wear, delamination, and oxidative wear are prevalent; at 300 °C, adhesive wear and delamination dominate; and at 600 °C, abrasive wear combined with oxidative wear is observed. In the partial slip regime, plastic deformation of the silver layer dominates at room temperature, whereas adhesive wear and delamination dominate at 300 °C and 600 °C. At elevated temperatures, although the silver layer undergoes some degradation, it still mitigates damage to both the substrate and the friction pair. Additionally, oxygen diffusion leads to the formation of a dense oxide layer at the interface between the silver layer and the GH2132 substrate, locally enhancing interfacial bonding. Conversely, oxide expansion causes blistering and regions of weakened adhesion.

Surface defects are a major cause of railway failures and pose serious safety risks, making accurate defect detection essential. However, existing methods often exhibit limited performance due to the scarcity of defect samples and the complexity of operating environments. To address these challenges, this paper proposes a sample self-generation-based framework for rail surface defect detection in extremely data-scarce scenarios. A multi-mode defect generation model is introduced, enabling effective data augmentation and style transfer using only one or two real samples. By decoupling the generation process into a Feature Learning Network with a sub-discriminator architecture and a Sample Generation Network, the proposed method achieves high sample diversity and quality with low computational cost. The generated samples are used to train YOLO-based detectors. Experiments show that the proposed approach improves mAP@0.5 by over 8 percentage points on YOLOv9, outperforming models trained with twice the amount of real data.

Particularly for curved mirrors, nonplanar illumination with its shape matching the curved mirrors under test in phase measuring deflectometry (PMD) has the advantages of enlarging the measurement range, decreasing the depth range of the reflected virtual screen. In this paper, a monocular deflectometry with nonplanar illumination is proposed, which contains a camera and a curved screen. Firstly, a method of modelling the curved screen is introduced to obtain a high-accuracy model of the screen, making the transformation of the screen free from the displayed feature points. Then, in a system of monocular PMD, vision ray calibration (VRC) is used to calibrate the ray distributions in the PMD with two separate steps and an integrated step. Meanwhile, two models of the screen are introduced to the PMD. Finally, with the calibrated ray distributions and the law of reflection, the slope distributions of the specular surfaces under test (SUT) are obtained to integrally reconstruct the SUT. In simulated measurements, VRC-based mono PMDs with the two screen models are verified, respectively. In actual measurements, two specular balls with different radii and a flat mirror as SUTs are measured to verify the proposed methods. And the measurement results show high accuracy, repeatability, and stability.

Geometrical tolerancing is critical to ensuring the quality and performance of mechanical products. The rise of intelligent manufacturing presents new challenges for accurate and efficient recognition of geometrical tolerancing from engineering drawings. Traditional manual and rule-based Optical Character Recognition (OCR) approaches struggle to effectively interpret the intricate graphical symbols and their semantic relationships. To address these limitations, this paper proposes an intelligent geometrical tolerancing recognition framework that integrates deep learning-based detection with rule-driven structural reasoning. A YOLO-based detector is employed to localize dimension arrows, symbols, values, datums, and textual annotations, supported by targeted data augmentation to enhance robustness against variations in line styles and orientations. The detected primitives are then organized into structured geometrical specification elements through a lightweight post-processing algorithm that combines spatial-proximity matching with geometric constraints derived from drafting standards. This hybrid strategy enables accurate reconstruction of dimensional elements and tolerance relationships while preserving their semantic associations. Beyond recognition, the structured output produced by the proposed method provides a machine-readable representation that can serve as a foundation for Large Language Model (LLM)-driven semantic understanding in intelligent manufacturing workflows.

ABSTRACT In this study, the dynamic response of titanium alloy (TC4)‐carbon fiber–reinforced plastic (CFRP) double‐riveted structures under various loads was investigated through experimental method. Complementary finite element analysis under different loading conditions is conducted to elucidate the evolution of stress distribution and elastic deformation within the joint. Specifically, fatigue tests were conducted at loading angles of 0°, 45°, and 90° to compare the dynamic response and evolution process of fretting damage at the contact interface, with simultaneous monitoring of temperature changes. The results demonstrate that fatigue testing process consists of two distinct stages. In the partial slip state under low loads, the temperature had slight fluctuations around 25°C, and the two plates did not fail. During the gross slip state under high loads, the temperature reached approximately 70°C, leading to fatigue failure of CFRP. Moreover, surface damage patterns were similar across angles, whereas 0° loading was subjected to greater excitation. At a load of 10 kN, the fatigue life under 0° loading exceeded 1 million cycles, and it was approximately 2000 times longer than that under 45° and 90° loadings. Hence, 0° loading was more favorable for double‐riveted structures, even though a period of complete slip appeared in the early fatigue stage under the 0° loading mode.

Following dozens of hours of bench testing, cracking was observed at the R0.2 fillet on the first tenon tooth of a K465 first-stage high-pressure turbine blade. This failure occurred only after replacing the original single glass-bead shot peening with a ceramic-glass composite process. The investigation included fluorescent penetrant inspection, surface observation, fractography, metallography, microhardness mapping, grinding and peening contrast trials as well as finite element analysis. The results revealed that the premature cracking of the fir-tree tenon was identified as fretting fatigue, without metallurgical defects at crack origins. The failure was attributed to oversized AZB300 ceramic shots (nominal diameter 0.3 mm), which exceeded the aviation standard allowable limit of 0.1 mm for R0.2 fillets. During pre-peening, these oversized particles induced irreversible plastic stacking and formed a localized protrusion at the fillet. This geometric distortion transformed the original conformal surface contact into narrow contact, causing severe stress concentration that coincided with the crack origins. The subsequent glass-bead finishing could not eliminate the preformed protrusion, ultimately accelerating subsurface crack initiation and propagation. Based on these findings, restrictive shot-size specifications for small-radius fillets and supplementary profile inspection criteria are proposed to prevent similar batch failures in production.

The resource-constrained multi-project scheduling problem (RCMPSP) often treats resource transfer time as a fixed parameter, neglecting its real-world variability. However, in high-end electronic equipment assembly and testing, resource transfer time is dynamically influenced by factors such as kit completion rates. This paper studies a dynamic RCMPSP with adjustable resource transfer times based on kit completion rates (DRCMPSP-RT&MK). While traditional genetic programming hyper-heuristic (GPHH) algorithms struggle with large-scale problems, we propose an enhanced algorithm, GPHH-WOA, which integrates the whale optimization algorithm (WOA) into GPHH and incorporates dynamic task and resource-transfer attributes into its rule-optimization process. To validate the algorithm’s effectiveness, we first compare the proposed method against six heuristic task-priority rules with static attributes. Second, we benchmark it against two existing GPHH variants and their surrogate-assisted versions. Experiments on three self-generated datasets of varying scales demonstrate that the proposed method significantly improves solution quality, with greater advantages as problem complexity increases. The results confirm the algorithm’s feasibility and effectiveness for large-scale DRCMPSP-RT&MK in dynamic environments.

The tribovoltaic effect, which directly converts mechanical energy into electricity at dynamic semiconductor interfaces, presents a novel pathway for intrinsic mechanical sensing. Here, we materialize the "surface-assensor" paradigm by harnessing this effect to create a self-sensing bearing. This is achieved through a spontaneously formed molybdenum disulfide (MoSS) layer on the bearing races, which establishes dynamic Schottky contacts with the steel balls, effectively turning the bearing surface itself into the sensing element. Mechanical friction at these interfaces directly generates a continuous stream of real-time electrical signals via the tribovoltaic effect. We analyze the underlying mechanism of this effect within the dynamic bearing system and demonstrate that the magnitude and characteristics of the resulting tribovoltaic outputs are intrinsically modulated by operational conditions, serving as highly sensitive and direct fingerprints of rotational speed and load. By employing explainable machine learning and deep learning to decode these signals, we achieve accurate monitoring of operational states, such as rotation speed, with the identification accuracy exceeding 96%, and further demonstrate its capability in early fault diagnosis. This work successfully establishes the tribovoltaic effect as a practical and powerful sensing mechanism, validating the "surface-as-sensor" concept as a pathway to a new generation of embedded, self-sensing intelligent machinery.

Amid the transformation driven by Industry 4.0 and 5.0, manufacturing is rapidly advancing toward greater intelligence and flexibility. Reconfigurable Matrix-structured Manufacturing Systems (RMMS) improve adaptability through dynamic structural and resource reconfiguration, while Integrated Process Planning and Scheduling (IPPS) jointly optimizes process routes and scheduling for optimal resource allocation and responsiveness. This study focuses on Dynamic IPPS with Reconfigurable Manufacturing Cells (DIPPS-RMC) in RMMS, and proposes a real-time scheduling approach based on multi-agent Proximal Policy Optimization (PPO) to reduce average tardiness and enhance system efficiency. A Mixed Integer Linear Programming model is established to address the complexity of process flows and dynamic scheduling, providing a solid theoretical foundation. The scheduling problem is further formulated as a Partially Observable Markov Decision Process to capture the uncertainty and partial observability of real manufacturing environments. To alleviate the credit assignment problem and enhance inter-agent coordination, a delayed reward-sharing mechanism is designed. A multi-agent PPO algorithm with centralized training and decentralized execution is introduced, leveraging parallel environment sampling to improve training efficiency and generalization. Extensive experiments on 270 cases across 27 scenarios show that the proposed method outperforms state-of-the-art multi-agent reinforcement learning algorithms in training speed, generalization, and scheduling performance. Its application to real-world cases further demonstrates effective handling of dynamic job arrivals and RMC breakdowns, validating its robustness and practical utility. These results confirm the method’s effectiveness and applicability in dynamic, complex manufacturing environments, offering an innovative solution for real-time scheduling in RMMS.