Computers in Industry最新文献

The cotter pin (CP) is a vital fastener for the catenary support components (CSCs) of high-speed electrified railways. Due to the vibration and excitation caused by the passing of railway vehicles, some CPs may be broken or fallen off over time, which poses a significant safety hazard to the railway systems. Currently, the CP defect detection is primarily conducted by humans, which is inefficient and inconsistent. Therefore, there is an urgent need for automatic CP defect detection to ensure railway safety. However, this task is very challenging as it requires covering hundreds or thousands of miles in limited times when the railway stops running. To this end, we first design a traffic track intelligent imaging device to capture catenary images at various angles at high speed. Then, inspired by the success of deep learning-based object detection, we develop a CP detection model based on an improved Faster R-CNN with a multi-scale region proposal network (MS-RPN) and propose the positive sample adaptive loss function (PSALF) to enhance detection accuracy. Finally, we propose a module to recognize the CP defect based on dilated convolution. The experimental results show that our method can effectively detect the CP defect in the catenary image, achieving 99.05 % precision and 98.40 % recall rate on CP defect detection. Furthermore, CP detection method and CP defect detection are significantly faster than baseline method, with FPS improvements of 2.76 and 24.67, respectively, thus making it more suitable for real-time applications in railway systems.

This article presents a systematic literature review (SLR) of empirical studies concerning Artificial Intelligence (AI) in the field of Supply Chain Management (SCM). Over the past decade, technologies belonging to AI have developed rapidly, reaching a sufficient level of maturity to catalyze transformative changes in business and society. Within the SCM community, there are high expectations about disruptive impacts on current practices. However, this is not the first instance where AI has sparked business excitement, often falling short of the hype. It is thus important to examine both opportunities and challenges emerging from its actual implementation. Our analysis clarifies the current technological approaches and application areas, while expounding research themes around four key categories: data and system requirements, technology deployment processes, (inter)organizational integration, and performance implications. We also present the contextual factors identified in the literature. This review lays a solid foundation for future research on AI in SCM. By exclusively considering empirical contributions, our analysis minimizes the current buzz and underscores relevant opportunities for future studies intersecting AI, organizations, and supply chains (SCs). Our effort is also meant to consolidate existing research insights for a managerial audience.

The radiographic inspection plays a crucial role in ensuring the casting quality for improving the service life under harsh environments. However, due to the low-contrast between the defects and the image background, the random spatial position distribution, random shapes and aspect ratios of the defects, the development of an accurate defect automatic detection system is still challenging. To address these issues, this paper proposes a novel framework for low-contrast and random multi-scale casting defect detection, which is referred to as adaptive global dynamic detection transformer (AGD-DETR). A novel defect-aware data augmentation method is first proposed to adaptively highlight the feature of the low-contrast defect boundary. A multi-attentional pyramid feature refinement (MPFR) module is then established to refine and fuse the multi-scale defect features of random sizes. Afterwards, a novel global dynamic receptive fusion-transformer (GDRF-Transformer) detection scheme is designed to perform the global perception and feature dynamic extraction of complex internal casting defects. It includes 4D-anchor query and cross-layer box update strategy, query rectification by prior information of defect aspect ratio, and global adaptive-feed forward network (GA-FFN). A dataset comprising turbine blade casting defect radiographic (TBCDR) images, is used to demonstrate the high efficiency of the proposed AGD-DETR. The obtained results show that the proposed method can accurately capture the spatial position distributions and complex defect shapes. Furthermore, it outperforms existing state-of-the-art defect detection methods.

The benefits of Digital Twins are widely recognized across various use cases. However, to ensure efficient utilization of Digital Twins, it is crucial to minimize the effort required for their creation. This is particularly relevant for behavior models, which play a significant role in many Digital Twin use cases. While there are existing approaches for creating these models efficiently, they rely on having access to the asset's structure in a digitally usable format. This requirement also applies to the field of fluidics. The paper presents a method for the automated digitalization of information from fluid circuit diagrams, which contain information about the fluid structure of the asset. The method is implemented on the example of pneumatic vacuum ejectors, and using the test data set as an example, a large part of the information could be digitalized fully automatically. This was also demonstrated for an exemplary circuit diagram with poorer image quality.

Magnetic pulse crimping (MPC) addresses the limitations of conventional hydraulic crimping in cable joint applications. However, the lack of dependable detection methods presents a significant challenge in MPC manufacturing. This study proposed a novel approach integrating 3D vision and ensemble learning to achieve a non-destructive quality assessment of MPC joints. By analyzing the geometric characteristics of crimping products, a specialized 3D vision algorithm was devised to extract geometric features. The random sample consensus (RANSAC) ensured low measurement errors: 0.5 % for terminals and 1.1 % for cables. Coordinate transformation simplified the feature calculation, resulting in an 18.6 % improvement in computational efficiency. To enhance dataset quality, a preprocessing pipeline was designed, incorporating correlation analysis, boxplots, principal component analysis (PCA), and density-based spatial clustering of applications with noise (DBSCAN). It handled irrelevant, redundant, and outlier information effectively. Compared to the original dataset, the training mean squared error (MSE) decreased from 1.790 to 0.290. Additionally, four high-accuracy candidate models were identified via thorough model selection and hyperparameter fine-tuning. Among them, for the design challenge of multilayer perceptron (MLP), a strategy was developed to find an optimal architecture, resulting in a configuration of 3 hidden layers with 16 nodes each. This strategy reduced design variability by constraining hidden layers and ensured stable gradient updates through full-batch training. The candidate models were further integrated using ensemble learning, specifically stacking. The final model achieved a mean absolute error (MAE) of 0.348 kN, and its mean absolute percentage error (MAPE) was 5 %, demonstrating higher accuracy. The results demonstrate the significant potential of the proposed approach in crimping quality prediction, enhancing manufacturing efficiency and reliability.

The use of Digital Intelligent Assistants (DIAs) in manufacturing aims to enhance performance and reduce cognitive workload. By leveraging the advanced capabilities of Large Language Models (LLMs), the research aims to understand the impact of DIAs on assembly processes, emphasizing human-centric design and operational efficiency. The study is novel in considering the three primary objectives: evaluating the technical robustness of DIAs, assessing their effect on operators' cognitive workload and user experience, and determining the overall performance improvement of the assembly process. Methodologically, the research employs a laboratory experiment, incorporating a controlled setting to meticulously assess the DIA's performance. The experiment used a between-subjects design comparing a group of participants using the DIA against a control group relying on traditional manual methods across a series of assembly tasks. Findings reveal a significant enhancement in the operators' experience, a reduction in cognitive load, and an improvement in the quality of process outputs when the DIA is employed. The article contributes to the study of the DIA's potential and AI integration in manufacturing, offering insights into the design, development, and evaluation of DIAs in industrial settings.

A Dimensional Variational Prototypical Network (DVPN) is proposed to learn transferable knowledge from a largescale dataset containing sufficient samples of diverse faults, enabling few-shot diagnosis on new faults that are unseen in the dataset. The network includes a multiscale feature fusion module with shared weights to extract fault features, followed by a dimensional variational prototypical module that uses variational inference to determine metric scaling parameters. This adaptive approach accurately measures feature similarity between samples and fault prototypes. To enhance discriminability, a representation learning loss is employed, distinguishing between the least similar samples within the same class (hard positive samples) and the most similar samples across different classes (hard negative samples). The network combines representation learning and prototypical learning through the joint representation learning (JRL) module, acquiring both task-level and feature-level knowledge for a more discriminative metric space and improved classification accuracy on unseen faults. Experimental evaluations on datasets from the Tennessee Eastman process and a real-world polyester esterification process show that the proposed DVPN achieves high diagnostic performance and is comparable to state-of-the-art methods for few-shot fault diagnosis (FSFD).

With the transformation and upgrading of the manufacturing industry, manufacturing systems have become increasingly complex in terms of the structural functionality, process flows, control systems, and performance assessment criteria. Digital representation, performance-related process monitoring, process regulation and control, and comprehensive performance optimization have been viewed as the core competence for future growth. Relevant topics have attracted significant attention and long-term exploration in both the academic and industrial communities. In this paper, focusing on the latest achievements in the context of smart manufacturing, a new performance-driven closed-loop process optimization and control framework with the cloud-edge-device collaboration is proposed. Firstly, in order to fully report the performance optimization and control technologies in manufacturing systems, a comprehensive review of associated topics, including digital representation and information fusion, performance-related process monitoring, dynamic scheduling, and closed-loop control and optimization are provided. Secondly, potential architectures integrating such technologies in manufacturing processes are investigated, and several existing research gaps are summarized. Thirdly, aiming at the hierarchical performance target, we present a roadmap to the cloud-edge-device collaborative closed-loop performance optimization and control for smart manufacturing. The overall architecture, development and deployment, and key technologies are discussed and explored with an actual industrial process scenario. Finally, the challenges and future research focuses are introduced. Through this work, it is hoped to provide new perspectives for the comprehensive performance optimization and control in the transition from Industry 4.0–5.0.

Digital Twins (DT) encompass virtual models interconnected with a physical system through data links. Although DTs hold significant potential for positive organisational impact, their successful adoption in industrial practice remains limited. Whereas existing research predominantly focuses on technical challenges, more recent studies underscore the importance of addressing organisational and human factors to overcome implementation barriers. One central aspect in this context is stakeholder communication, especially given the ambiguous nature of the term DT in academic and industrial discussions. To expand the limited understanding of the factors causing challenging DT stakeholder communications, this article presents findings from an extensive exploratory study. It involves 27 in-depth interviews and two focus groups with highly experienced DT professionals. By employing grounded theory and the Gioia methodology, a grounded model for DT stakeholder communication challenges is derived. This model reveals the complex communication dynamics within DT projects, emphasising the emergence of novel stakeholder communication patterns that heavily rely on multidisciplinary collaboration. In total, 28 communication challenges were identified, grouped into eight theoretical themes and categorised into two aggregate dimensions: human- and organisation-centric challenges. Additionally, the study identified 15 practices, e.g., defining clear objectives, and starting small and building gradually, that organisations are following to mitigate these challenges. As a result, this article provides the theoretical groundwork for a comprehensive understanding of DT stakeholder communication and its associated challenges by revealing distinctive features and offering practical guidance to overcome critical challenges in DT projects.

Resistance spot welding (RSW) is a widely used welding technology in automotive manufacturing, and weld nugget quality is closely related to the quality of the vehicle body. Offline random checks are largely relied on the quality inspection of weld nuggets, but they have low efficiency and high cost. To address this issue, this paper proposes a deep learning model for RSW weld nugget classification, named the offset-transformer hierarchical model (OFTFHC), which is based on the point cloud data of its appearance shape. OFTFHC uses a hierarchical network structure to gradually expand the receptive field. A local feature module is introduced to extract local features from the point cloud, effectively enabling the recognition of the fine structural features of the resistance spot weld point cloud. A residual ratio module, which is based on $\mathrm{MLP\_MA}$ and uses max and average functions for feature enhancement, is designed to adapt to the complex spatial structure of the point cloud. The offset-transformer structure is used to learn global context features, thereby enhancing the global feature extraction capability. Through classification experiments on RSW weld nuggets across 5 categories with a total of 1050 samples, OFTFHC achieved an average accuracy of 80.6 %, outperforming existing models. This demonstrates the effectiveness and superiority of the method, making it highly suitable for weld nugget quality control in automotive automation production lines.