Search Results (49)

Sustainable manufacturing performance is increasingly influenced by the quality of decisions embedded in Quality Management System (QMS) activities, particularly those related to problem analysis and preventive action. In industrial environments such as welded bus chassis production, recurring quality defects—although involving small components—can generate sustainability impacts through rework, inspection effort, and energy consumption. Although artificial intelligence (AI) is increasingly adopted to support quality-related tasks, its contribution is often assessed in terms of automation rather than its effect on decision quality. This study presents an AI-supported, prompt-driven decision framework designed to strengthen preventive performance within QMS. The framework is implemented through a deterministic software application that formalizes prompt engineering as a rule-based process, transforming informal human problem descriptions into structured prompts suitable for external AI reasoning tools. The application itself does not embed AI and does not generate decisions; instead, it functions as a transparent decision interface that reduces variability in problem formulation and supports methodological consistency. The framework was validated through an industrial case study conducted in a bus chassis manufacturing plant experiencing recurring defects related to missing or incorrectly positioned welded brackets. Quantitative evaluation using Key Performance Indicators demonstrates reduced analysis cycle time, improved completeness of problem definitions, higher corrective action implementation rates, and lower defect recurrence. Full article

The partial discharge (PD) caused by insulation defects of gas-insulated switchgear (GIS) threatens the secure and stable operation of power systems. Traditional PD pattern recognition methods exhibit limitations due to incomplete information utilization and unresolved correlations among characteristic parameters. Based on the partial discharge mechanisms of GIS, this paper establishes a GIS partial discharge simulation model using the finite element time-domain (FETD) method. The propagation rules and influence factors of ultra-high-frequency (UHF) signals are studied. Furthermore, a PD pattern recognition method based on a deep convolutional neural network (CNN) is proposed. Research results indicate that UHF signals generated by GIS partial discharge are significantly influenced by pulse current waveforms and discharge quantity. The peak-to-peak amplitude of the electric field (Epp) increases linearly with the current amplitude, while it decreases nonlinearly with increasing pulse width. The UHF signal remains a certain value while the pulse width exceeds a critical threshold (4 ns). The proposed CNN-based approach, utilizing full-wave UHF signals, overcomes the shortcomings of traditional methods reliant on manually extracted discrete feature parameters. Compared to other network architectures and optimization algorithms, the ConvNeXt-AdamW model demonstrates superior performance, achieving an average PD pattern recognition accuracy exceeding 96%. Full article

In this study, we present an extension of the Takagi–Sugeno fuzzy inference system (TS-FIS) framework based on prototypical fuzzy similarity (PFS) for defect detection in concrete. The key novelty lies in integrating the PFS mechanism into the TS-FIS+ANFIS architecture, thus enabling a hybrid rule–activation mechanism, bringing together fuzzy interpretability with data-driven similarity learning. To describe the ultrasonic concrete defect scenario, a high-fidelity finite element method (FEM) model that combines solid mechanics with fluid acoustics has been developed. From this numerical model, a synthetic dataset of about 36.8 million samples has been generated. The performance of the proposed TS-FIS+ANFIS+PFS classification system has been compared with that of a conventional FIS+ANFIS model, its particle-swarm-optimized (PSO) version and a Decision Tree (DT) classifier. The proposed model achieved the best performance, with a classification accuracy of 85.4% and an inference time of approximately 0.2 ms per sample. In contrast, the conventional, the PSO and the DT classifiers yielded accuracies of 60.5%, 62.0%, and 76.0%, respectively. These results confirm that PFS improves sensitivity and alleviates the computational effort, representing a potential candidate toward the realization of a defect abacus for concrete, an atlas conceived as a systematic collection of defect configurations associated with specific ultrasonic responses. Full article

To address the limited defect-detection capability of existing performance testing methods for switching power supplies under varying operating conditions, this paper proposes a defect identification approach based on an enhanced Dung Beetle Optimizer. The algorithm integrates multi-strategy improvements—including piecewise chaotic mapping, Lévy flight perturbation, hybrid sine–cosine updating, and an alert sparrow mechanism—to refine the initial population generation, position update rules, and late-stage exploration. These enhancements strengthen its spatial search ability and computational performance. The experimental results show that the method accurately identifies the predefined defect intervals with a precision of 94.79%, covering 91.3% of the operating conditions. Comparisons with existing mainstream methods confirm the superior performance, effectiveness, and feasibility of the proposed method. Full article

Problems such as unreasonable processability or model defects generated in the design stage will lead to continuous rework during the manufacturing process, which greatly increases the manufacturing cost of the product. Through manufacturability analysis, the process designer can find design defects that are difficult to manufacture, impossible to manufacture, or have high manufacturing costs as early as possible, so as to reduce the number of round trips between design and process, and shorten the product development cycle. However, it is difficult for the current rule-based manufacturability analysis method to obtain professional knowledge and construct a complete manufacturability analysis rule repository. Therefore, a manufacturability analysis method based on a graph neural network is proposed. First, the attribute adjacency graph and UV gridding are combined to characterize the part model data, which can effectively represent the topological information and geometric information on the part model. At the same time, parameter information on the spherical coordinate system is used to ensure rotation and translation invariance; then, based on the graph representation of the part model, a hierarchical graph neural network is constructed, which is divided into three levels, edge, node, and graph, for encoding, information transmission and updating, and expanding the receptive field for better node classification to support manufacturability analysis. Finally, graph contrastive learning is used as a regularization technique in the pre-training stage to maximize the similarity of graph representations between different views to improve prediction performance. Manufacturability analysis tests were carried out on the constructed part model dataset, and the experimental results showed that the method has good performance. Full article

Polar patterns and topological defects are ubiquitous in active matter. In this paper, we study a paradigmatic polar active dumbbell system through numerical simulations, to clarify how polar patterns and defects emerge and shape evolution. We focus on the interplay between these patterns and morphology, domain growth, irreversibility, and compressibility, tuned by dumbbell rigidity and interaction strength. Our results show that, when separated through MIPS, dumbbells with softer interactions can slide one relative to each other and compress more easily, producing blurred hexatic patterns, polarization patterns extended across entire hexatically varied domains, and stronger compression effects. Analysis of isolated domains reveals the consistent presence of inward-pointing topological defects that drive cluster compression and generate non-trivial density profiles, whose magnitude and extension are ruled by the rigidity of the pairwise potential. Investigation of entropy production reveals instead that clusters hosting an aster/spiral defect are characterized by a flat/increasing entropy profile mirroring the underlying polarization structure, thus suggesting an alternative avenue to distinguish topological defects on thermodynamical grounds. Overall, our study highlights how interaction strength and defect–compression interplay affect cluster evolution in particle-based active models, and also provides connections with recent studies of continuum polar active field models. Full article

Mass customisation in the automotive industry has exploded the number of wiring harness variants that must be assembled, tested and repaired on the shop floor. Existing CAD or schematic formats are too heavy and too coarse-grained to drive in-line, per-VIN validation, while supplier documentation is heterogeneous and often incomplete. This paper presents a pin-centric, two-tier graph model that converts raw harness tables into a machine-readable, wiring-aware digital twin suitable for real-time use in manufacturing plants. All physical and logical artefacts—pins, wires, connections, paths and circuits—are represented as nodes, and a dual-store persistence strategy separates attribute-rich JSON documents from a lightweight NetworkX property graph. The architecture supports dozens of vehicle models and engineering releases without duplicating data, and a decentralised validation pipeline enforces both object-level and contextual rules, reducing initial domain violations from eight to zero and eliminating fifty-two circuit errors in three iterations. The resulting platform graph is generated in 0.7 s and delivers 100% path-finding accuracy. Deployed at Ford’s Almussafes plant, the model already underpins launch-phase workload mitigation, interactive visualisation and early design error detection. Although currently implemented in Python 3.11 and lacking quantified production KPIs, the approach establishes a vendor-agnostic data standard and lays the groundwork for self-aware manufacturing: future work will embed real-time validators on the line, stream defect events back into the graph and couple the wiring layer with IoT frameworks for autonomous repair and optimisation. Full article

This paper presents the results of determining the parameters of tritium transfer processes in lithium ceramics Li₂TiO₃ under reactor irradiation conditions. Analysis of sections with a short-term decrease in reactor power allowed numerical determination of the Arrhenius parameters of tritium diffusion (pre-exponential factor and activation energy) based on comparison with in situ experimental data. The obtained values of activation energy (70.2–74.7 kJ/mol) and pre-exponential factor (0.9–2.1 × 10⁻⁸m²/s) demonstrate growth with increasing fluence, which is explained by the accumulation of radiation defects in ceramics. A linear dependence was established between D₀ and E_a, corresponding to the Mayer–Noldel rule. Unlike previously conducted studies based on a phenomenological approach to assessing only the activation energy of diffusion, in this study, a complex model that takes into account temperature gradients, tritium generation, its diffusion, and release from the surface was used. The applicability of such an integrated approach to the analysis of in situ reactor experiments with lithium ceramics was confirmed, and allowed us to estimate changes in the tritium transfer parameters in lithium ceramics Li₂TiO₃ depending on the irradiation time. Full article

The design of robots is highly dependent on their applications. For agricultural robots, terrain, weather, and crop diversity need to be considered, and work efficiency, cost, and reliability must be evaluated. These factors are important to determine the design of agricultural robots. In this study, we identified the constraint factors of agricultural robots from the perspectives of navigation, movement, control, cost, and reliability. The orthogonal defect classification (ODC) method was used to classify and grade these factors and explore the relationships among these factors. Based on the results, the design rules of agricultural robots were created, and an automatic production knowledge base of agricultural robot design was constructed. The results contribute to the automatic generation of the design framework of agricultural robots under specific environments to effectively improve the design level and quality of agricultural robots and popularize agricultural robots. Full article

Considered one of the most severe types of trauma with a high impact upon patient survival, burns are the leading cause of disability-adjusted life-years (DALYs), and are responsible for high morbidity, prolonged hospitalization, disfigurement and social stigma. Of particular interest are injuries that affect the functional areas: face, neck, hand and fingers, joints, feet and soles and perineum. Burns to these regions highly influence the day-to-day activities of patients due to the formation of vicious scars and contractures, which may affect both quality of life and functional capacity. One of the primary challenges in the management of burn patients is the effective coverage of tissue defects resulting from such injuries. Cases that have a large area of burned surface also have a limited amount of total available skin. As such, the importance of skin substitutes increases, particularly in the treatment of these areas. Skin substitutes are widely utilized in plastic surgery due to their ability to promote wound healing by providing an extracellular matrix. Consequently, ongoing research has focused on developing skin substitutes that can serve as alternatives to autografts, addressing the challenges associated with large-scale tissue loss. This article aims to present and compare the most used skin substitutes, highlighting their respective advantages and limitations. This topic continues to be a subject of significant debate, as an ideal substitute has yet to be created. The cost–efficiency ratio is a practical consideration that must be tailored to each specific medical system. The available data in the literature usually present general guidelines, not rules, and as such, they need to be adapted to each patient’s necessities. Full article

Gas polyethylene (PE) pipes have an become essential component of the urban gas pipeline network due to their long service life and corrosion resistance. To prevent safety incidents, regular monitoring of gas pipelines is crucial. Traditional inspection methods face significant challenges, including low efficiency, high costs, and limited applicability. Machine vision-based inspection methods have emerged as a key solution to these issues. Despite this, the method also encounters the problem of scarcity of defect samples and uneven data distribution in gas pipeline defect detection. For this reason, an improved Deep Convolutional Generative Adversarial Network (DCGAN) is proposed. By integrating the Minibatch Discrimination (MD), Spectral Normalization (SN), Self-Attention Mechanism (SAM) and Two-Timescale Update Rule (TTUR), the proposed approach overcomes the original DCGAN’s limitations, including mode collapse, low resolution of generated images, and unstable training, the data augmentation of defective images inside the pipeline is realized. Experimental results demonstrate the superiority of the improved algorithm in terms of image generation quality and diversity, while the ablation study validates the positive impact of the improvement in each part. Additionally, the relationship between the number of augmented images and classification accuracy, showing that classifier performance improved across all scenarios when generated defect images were included. The findings indicate that the images produced by the improved model significantly enhance defect detection accuracy and hold considerable potential for practical application. Full article

Human–machine pair inspection refers to a technique that supports programmers and machines working together as a “pair” in source code inspection tasks. The machine provides guidance, while the programmer performs the inspection based on this guidance. Although programmers are often best suited to inspect their own code due to familiarity, overconfidence may lead them to overlook important details. This study introduces a novel mutation-based human–machine pair inspection method, which is designed to direct the programmer’s attention to specific code components by applying targeted mutations. We assess the effectiveness of code inspections by analyzing the programmer’s corrections of these mutations. Our approach involves defining mutation operators for each keyword in the program based on historical defects, developing mutation rules based on program keywords and a strategy for automatically generating mutants, and designing a code comparison strategy to quantitatively evaluate code inspection quality. Through a controlled experiment, we demonstrate the effectiveness of mutation-based human–machine pair inspection in aiding programmers during the inspection process. Full article

Defect detection plays a crucial role in industrial production, and the implementation of this technology has significant implications for improving both product quality and processing efficiency. However, the limited availability of defect samples for training deep-learning-based object detection models within industrial processes poses challenges for model training. In this paper, we propose a novel deep convolutional generative adversarial network with self-attention mechanism for the data augmentation of infrared thermal images for the application of aluminum foil sealing. To further expand its applicability, the proposed method is designed not only to address the specific needs of aluminum foil sealing but also to serve as a robust framework that can be adapted to a wide range of industrial defect detection tasks. To be specific, the proposed approach integrates a self-attention module into the generator, adopts spectral normalization in both the generator and discriminator, and introduces a two time-scale update rule to coordinate the training process of these components. The experimental results validated the superiority of the proposed approach in terms of the synthesized image quality and diversity. The results show that our approach can capture intricate details and distinctive features of defect images of aluminum foil sealing. Furthermore, ablation experiments demonstrated that the combination of self-attention, spectral normalization, and two time-scale update rules significantly enhanced the quality of image generation, while achieving a balance between stability and training efficiency. This innovative framework marks a notable technical breakthrough in the field of industrial defect detection and image synthesis, offering broad application prospects. Full article

Background/Objectives: Inborn errors of immunity (IEI) encompass various congenital disorders, resulting in immunity defects and recurrent infections. Home-based subcutaneous immunoglobulin replacement therapy (scIgRT) is the best treatment option for those with primary antibody deficiency (PAD). However, the lack of standardized procedures in patient training remains a challenge. Our study investigates nurses’ practice and perspectives, aiming to identify areas for improvement in at-home scIgRT practice. Methods: We prepared a structured survey regarding scIgRT, including needle choice experience and perception of adverse events, and distributed it among qualified nurses involved in patient training and scIgRT supervising. Results: We included 56 nurses with a median age of 50 years. Among them, 67.9% represented adult care providers, while 32.1% supervised IgRT in children. Most respondents (83.9%) used the classic or assisted with hyaluronidase scIgRT preparations. Single-channel needles were administered most commonly (85.7%). The needle length was mostly chosen solely by a nurse (57.1%) or in cooperation with the patient (23.2%). Next, 9 mm and 12 mm needles were used most often (92.9% and 78.6%, respectively). As expected, the 6 mm needle was more frequently applied for children compared to adults (n = 16, 88.9% vs. n = 11, 28.9%, p < 0.001), while 12 mm was primarily used in adults (n = 35, 92.1% vs. n = 9, 50.0%, p < 0.001). Visual skin fold assessment was the basis for the needle selection (58.9%), followed by the injection site rule (26.8%) or a choice between two available needle types for thinner or thicker patients (25.0%). Results of this survey indicate that, according to nurses’ opinions presented in this survey, the needle length could be associated with local scIgRT adverse events, such as side needle leakage or local burning. Yet, it was likely unrelated to general adverse signs, such as headaches or dizziness. Most respondents (66.1%) indicated that, even if local adverse events occur, patients are reluctant to change scIgRT preparation or needle length. Most participants (69.6%) reported that the optimal administration technique needs to be discussed with the patient before and during scIgRT. Conclusions: This study sheds light on scIgRT practice in Poland, emphasizing deficiency in needle selection technique. Future research should focus on standardized training and advanced needle selection procedures on patient outcomes, investigating the correlation between needle strategies and adverse events, as well as the effectiveness of scIgRT. Full article

Pores are common defects generated during fabrication, which restrict the application of carbon/carbon (C/C) composites. To quantitatively understand the effects of pores on mechanical strength, this paper proposes a representative volume element model of unidirectional (UD) C/C composites based on the finite element method. The Hashin criterion and exponential degraded rule are used as the failure initiation and evolution of pyrolytic carbon matrices, respectively. Interfacial zones are characterized using the cohesive constitutive. At the same time, periodic boundary conditions are employed to study transverse tensile, compressive, and shear deformations of UD C/C composites. Predicted results are compared with the experimental results, which shows that the proposed model can effectively simulate the transverse mechanical behaviors of UD C/C composites. Based on this model, the effects of microstructural parameters including porosity, pore locations, the distance between two pores, pore clustering, and pore shapes on the mechanical strength are investigated. The results show that porosity markedly reduces the strength as porosity increases. When the porosity increases from 4.59% to 12.5%, the transverse tensile, compressive, and shear strengths decrease by 35.91%, 37.52%, and 30.76%, respectively. Pore locations, the distance between two pores, and pore clustering have little effect on the shear strength of UD C/C composites. For pore shapes, irregular pores more easily lead to stress concentration and matrix failure, which greatly depresses the bearing capacity of UD C/C composites. Full article