Computers in Industry最新文献

{"title":"A method for the automated digitalization of fluid circuit diagrams","authors":"Valentin Stegmaier ,&nbsp;Nasser Jazdi ,&nbsp;Michael Weyrich","doi":"10.1016/j.compind.2024.104139","DOIUrl":"10.1016/j.compind.2024.104139","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"162 ","pages":"Article 104139"},"PeriodicalIF":8.2,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0166361524000678/pdfft?md5=4540886a389df5a90d3f034e5046ddde&pid=1-s2.0-S0166361524000678-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141910597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quality prediction for magnetic pulse crimping cable joints based on 3D vision and ensemble learning","authors":"Ming Lai ,&nbsp;Shaoluo Wang ,&nbsp;Hao Jiang ,&nbsp;Junjia Cui ,&nbsp;Guangyao Li","doi":"10.1016/j.compind.2024.104137","DOIUrl":"10.1016/j.compind.2024.104137","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"162 ","pages":"Article 104137"},"PeriodicalIF":8.2,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141891662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of a large language model based digital intelligent assistant in assembly manufacturing","authors":"Silvia Colabianchi,&nbsp;Francesco Costantino,&nbsp;Nicolò Sabetta","doi":"10.1016/j.compind.2024.104129","DOIUrl":"10.1016/j.compind.2024.104129","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"162 ","pages":"Article 104129"},"PeriodicalIF":8.2,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0166361524000575/pdfft?md5=6b6a9937873da49d70deade15d2919a2&pid=1-s2.0-S0166361524000575-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141891589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel dimensional variational prototypical network for industrial few-shot fault diagnosis with unseen faults","authors":"Chuang Peng,&nbsp;Lei Chen,&nbsp;Kuangrong Hao,&nbsp;Shuaijie Chen,&nbsp;Xin Cai,&nbsp;Bing Wei","doi":"10.1016/j.compind.2024.104133","DOIUrl":"10.1016/j.compind.2024.104133","url":null,"abstract":"<div><p>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).</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"162 ","pages":"Article 104133"},"PeriodicalIF":8.2,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141891660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance-driven closed-loop optimization and control for smart manufacturing processes in the cloud-edge-device collaborative architecture: A review and new perspectives","authors":"Chi Zhang ,&nbsp;Yilin Wang ,&nbsp;Ziyan Zhao ,&nbsp;Xiaolu Chen ,&nbsp;Hao Ye ,&nbsp;Shixin Liu ,&nbsp;Ying Yang ,&nbsp;Kaixiang Peng","doi":"10.1016/j.compind.2024.104131","DOIUrl":"10.1016/j.compind.2024.104131","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"162 ","pages":"Article 104131"},"PeriodicalIF":8.2,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141891663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital Twin Stakeholder Communication: Characteristics, Challenges, and Best Practices","authors":"Christian Kober ,&nbsp;Francisco Gomez Medina ,&nbsp;Martin Benfer ,&nbsp;Jens Peter Wulfsberg ,&nbsp;Veronica Martinez ,&nbsp;Gisela Lanza","doi":"10.1016/j.compind.2024.104135","DOIUrl":"10.1016/j.compind.2024.104135","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"161 ","pages":"Article 104135"},"PeriodicalIF":8.2,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0166361524000630/pdfft?md5=196895a764a41e49cf035f9b707616ba&pid=1-s2.0-S0166361524000630-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141891664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An offset-transformer hierarchical model for point cloud-based resistance spot welding quality classification","authors":"Bo Yang ,&nbsp;Qing Peng ,&nbsp;Zhengping Zhang ,&nbsp;Yucheng Zhang ,&nbsp;Yufeng Li ,&nbsp;Zerui Xi","doi":"10.1016/j.compind.2024.104134","DOIUrl":"10.1016/j.compind.2024.104134","url":null,"abstract":"<div><p>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 <span><math><mi>MLP_MA</mi></math></span> 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.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"161 ","pages":"Article 104134"},"PeriodicalIF":8.2,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141910598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DMWMNet: A novel dual-branch multi-level convolutional network for high-performance mixed-type wafer map defect detection in semiconductor manufacturing","authors":"Xiangyan Zhang ,&nbsp;Zhong Jiang ,&nbsp;Hong Yang ,&nbsp;Yadong Mo ,&nbsp;Linkun Zhou ,&nbsp;Ying Zhang ,&nbsp;Jian Li ,&nbsp;Shimin Wei","doi":"10.1016/j.compind.2024.104136","DOIUrl":"10.1016/j.compind.2024.104136","url":null,"abstract":"<div><p>Wafer map defect detection plays an important role in semiconductor manufacturing by identifying root causes and accelerating process adjustments to ensure product quality and reduce unnecessary expenditures. However, existing methods have some limitations, such as low accuracy in mixed-type defect detection and poor recognition of similar defects and weak features. In this article, a novel dual-branch multi-level convolutional network (DMWMNet) is proposed for high-performance mixed-type wafer map defect detection. By fully considering the interrelationships between basic defects, defect number, and defect type, the network is designed to include two efficient parallel Branches and a Fusion classifier. Detecting defect types using basic defect discrimination and defect number detection is helpful for ameliorating problems with high complexity and low accuracy caused by multiple defect categories and feature overlaps. Furthermore, a composite loss function based on focal loss is employed to improve the network’s capacity to recognize weak features and similar defects. Experimental results on the MixedWM38 dataset show that DMWMNet has favorable mixed-type defect detection performance compared to other methods, with accuracy, precision, recall, F1 score, and MCC of 98.99%, 98.94%, 99.03%, 98.98%, and 98.97%, respectively.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"161 ","pages":"Article 104136"},"PeriodicalIF":8.2,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141910629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Addressing challenges in industrial pick and place: A deep learning-based 6 Degrees-of-Freedom pose estimation solution","authors":"Elena Govi ,&nbsp;Davide Sapienza ,&nbsp;Samuele Toscani ,&nbsp;Ivan Cotti ,&nbsp;Giorgia Franchini ,&nbsp;Marko Bertogna","doi":"10.1016/j.compind.2024.104130","DOIUrl":"10.1016/j.compind.2024.104130","url":null,"abstract":"<div><p>Object picking is a fundamental, long-lasting, and yet unsolved problem in industrial applications. To complete it, 6 Degrees-of-Freedom pose estimation can be crucial. This task, easy for humans, is a challenge for machines as it involves multiple intelligent processes (for example object detection, recognition, pose prediction). Pose estimation has recently made huge steps forward, due to the advent of Deep Learning. However, in real-world applications it is not trivial to compute it: each use-case needs an annotated dataset and a model robust enough to face its specific challenges. In this paper, we present a comprehensive investigation focused on a specific use-case: the picking of four industrial objects by a collaborative robot’s arm, addressing challenges related to reflective textures and pose ambiguities of heterogeneous shapes. Thus, Artificial Intelligence is crucial in this process, utilizing Convolutional Neural Networks to discern an object’s pose by extracting hierarchical features from a single image. In detail, we propose a new synthetic dataset of industrial objects and a fine-tuning method to close the sim-to-real domain gap. In addition, we improved an existing pipeline for pose estimation and introduced a new version of an existing method, based on Convolutional Neural Networks. Finally, extensive experiments were conducted with a Universal Robot UR5e. Results show our strategy achieves good performances with an average successful picking rate of 75% on these new objects. Considering the lack of available datasets for pose estimation, coupled with the significant time and labor required for annotating new images, we contribute to the scientific community by providing a comprehensive dataset, and the associated generation and estimation pipelines.<span><span>¹</span></span></p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"161 ","pages":"Article 104130"},"PeriodicalIF":8.2,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0166361524000587/pdfft?md5=5784c120c97e9ce7f729edd31cc45d22&pid=1-s2.0-S0166361524000587-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141910630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of generative artificial intelligence models on the performance of citizen data scientists in retail firms","authors":"Rabab Ali Abumalloh ,&nbsp;Mehrbakhsh Nilashi ,&nbsp;Keng Boon Ooi ,&nbsp;Garry Wei Han Tan ,&nbsp;Hing Kai Chan","doi":"10.1016/j.compind.2024.104128","DOIUrl":"10.1016/j.compind.2024.104128","url":null,"abstract":"<div><p>Generative Artificial Intelligence (AI) models serve as powerful tools for organizations aiming to integrate advanced data analysis and automation into their applications and services. Citizen data scientists—individuals without formal training but skilled in data analysis—combine domain expertise with analytical skills, making them invaluable assets in the retail sector. Generative AI models can further enhance their performance, offering a cost-effective alternative to hiring professional data scientists. However, it is unclear how AI models can effectively contribute to this development and what challenges may arise. This study explores the impact of generative AI models on citizen data scientists in retail firms. We investigate the strengths, weaknesses, opportunities, and threats of these models. Survey data from 268 retail companies is used to develop and validate a new model. Findings highlight that misinformation, lack of explainability, biased content generation, and data security and privacy concerns in generative AI models are major factors affecting citizen data scientists’ performance. Practical implications suggest that generative AI can empower retail firms by enabling advanced data science techniques and real-time decision-making. However, firms must address drawbacks and threats in generative AI models through robust policies and collaboration between domain experts and AI developers.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"161 ","pages":"Article 104128"},"PeriodicalIF":8.2,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141736569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}