Journal of Industrial Information Integration最新文献

{"title":"Intelligent optimization of particle size distribution in unscreened recycled coarse aggregates using 3D surface analysis","authors":"Cheng Chang ,&nbsp;Francesco Di Maio ,&nbsp;Rajeev Bheemireddy ,&nbsp;Perry Posthoorn ,&nbsp;Abraham T. Gebremariam ,&nbsp;Peter Rem","doi":"10.1016/j.jii.2025.100864","DOIUrl":"10.1016/j.jii.2025.100864","url":null,"abstract":"<div><div>The efficient measurement and optimization of the particle size distribution (PSD) of recycled coarse aggregates (RCA) is critical to ensuring consistent quality in high-performance concrete production. Unlike primary aggregates, which typically demonstrate minimal variability over extended periods and require only occasional testing, RCA often exhibit substantial fluctuations in quality over short timeframes. This variability necessitates a precise, automated, and real-time quality assessment approach, which is lacking in conventional aggregate processing. In this study, a rapid, automated, and non-contact 3D surface analysis method is proposed to assess and optimize the PSD of unscreened RCA during continuous transport on a conveyor belt. A custom-designed conical feeder and splitter facilitate the formation of continuous, symmetric triangular RCA piles, ranging from 4.0 to 16.0 mm in size. Representative PSD measurements are obtained by analyzing a designated strip located at one-third of the pile's height. High-resolution 3D point cloud data are processed using a watershed segmentation algorithm that leverages gradient-based path tracing for efficient topographical mapping. This enables parallel data processing, thereby reducing computational time. The proposed method enables real-time and accurate PSD analysis at industrial throughput levels (≥50 tons per hour) without interrupting conveyor operation, achieving a Root Mean Square Error (RMSE) between 4.69 % and 6.09 %. Furthermore, an optimization strategy based on cumulative percentage retained curves enhances RCA quality and supports continuous process control. The integration of these techniques contributes to improved RCA management and promotes sustainable resource utilization and waste reduction in the construction sector.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"46 ","pages":"Article 100864"},"PeriodicalIF":10.4,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143947502","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":"Opportunities and challenges of increased digitalization during new product introduction","authors":"Paraskeva Wlazlak,&nbsp;Edris Safavi,&nbsp;Kerstin Johansen","doi":"10.1016/j.jii.2025.100862","DOIUrl":"10.1016/j.jii.2025.100862","url":null,"abstract":"<div><div>This study addresses a critical gap in the literature by providing a comprehensive analysis of the organizational opportunities and challenges linked to increasing digitalization within the context of New Product Introduction (NPI), with a particular focus on large organizations within the manufacturing industry. The study introduces a framework that integrates opportunities, challenges, and tentative mechanisms associated with digitalization, employing a sociotechnical perspective that considers the interdependencies among tools/technology, processes, and people. This holistic approach highlights the multifaceted nature of digitalization and emphasizes the necessity of balancing these dimensions to achieve successful NPI outcomes. By adopting a sociotechnical perspective on increasing digitalization during NPI, the study underscores the complexity of digitalization challenges, which span technological, process-related, and human factors. The framework extends existing research and offers valuable insights for academics and practitioners, facilitating a deeper understanding of digitalization's complexities in large manufacturing organizations.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"46 ","pages":"Article 100862"},"PeriodicalIF":10.4,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943123","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":"Collaborative and trustworthy fault diagnosis for mechanical systems based on probabilistic neural network with decision-level information fusion","authors":"Zifei Xu ,&nbsp;Kaicheng Zhao ,&nbsp;Wanfu Zhang ,&nbsp;Weipao Miao ,&nbsp;Kang Sun ,&nbsp;Jin Wang ,&nbsp;Musa Bashir","doi":"10.1016/j.jii.2025.100854","DOIUrl":"10.1016/j.jii.2025.100854","url":null,"abstract":"<div><div>Fault diagnosis is a critical component of prognostics and health management, enhancing machinery reliability and ensuring operational efficiency by enabling proactive maintenance strategies. However, achieving this requires high data fidelity to accurately predict the full spectrum of faults and structural degradation for reliable assessments. AI-driven fault diagnostics based on machine learning often face challenges in reliability due to uncertainties arising from variations in data distribution, caused by changing operating conditions and noise interference. These factors undermine the trustworthiness of such methods. To address these challenges in accuracy and reliability for mechanical systems, such as gearboxes, this study proposes a Trustworthy Intelligent Diagnostic (TID) model. The TID model incorporates a multi-scale probabilistic neural network, and a decision fusion module based on uncertainty quantification (UQ). Specifically, three UQ-based decision fusion strategies are introduced to enhance diagnostic reliability by effectively managing uncertainty in fault diagnosis. Building upon the TID model, a cooperative fault diagnosis framework is further proposed to facilitate fault knowledge sharing and alleviate the limitations posed by data scarcity. The proposed approach is validated using both experimental data and real-world wind turbine gearbox failure datasets, demonstrating significant improvements in diagnostic accuracy and a notable reduction in false alarm rates. These results highlight the effectiveness, reliability, and superiority of the proposed method.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"46 ","pages":"Article 100854"},"PeriodicalIF":10.4,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144066061","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":"A multi-scenario model fusion and verification method for digital twin machine tool","authors":"Haochen Li,&nbsp;Ping Yan,&nbsp;Han Zhou,&nbsp;Jie Pei,&nbsp;Bochen Wang","doi":"10.1016/j.jii.2025.100859","DOIUrl":"10.1016/j.jii.2025.100859","url":null,"abstract":"<div><div>High-fidelity digital twin modeling is the core of digital twin machine tool (DTMT) to achieve accurate mapping and deliver functional services. Model fusion is a key modeling technology to promote the integrity and system connectivity of DTMT. However, current model fusion lacks attention to the multi-scenario characteristics of DTMT, which hinders the effective application of DTMT. Therefore, this paper proposes a multi-scenario model fusion and verification method for DTMT to eliminate information islands, improve model collaboration and respond to dynamic application requirements. Firstly, an S3C2 architecture is proposed to guide the multi-scenario model fusion of DTMT. The S3C2 architecture helps clarify the structural relationships of multi-scenario models and mask their heterogeneity, thus enabling DTMT to fuse the right models at the right time and provide the desired digital twin service. In addition, the fusion mechanism with different topologies is also considered to support the information exchange in the multi-scenario model fusion process of DTMT. Then, a method combining SysML and π-calculus is proposed to describe the fusion behavior and verify the fusion process. Verifying the correctness of interactive behaviors and semantic consistency in the model fusion process is helpful to ensure the stability of the digital twin system and improve the utilization rate of resources. Finally, the effectiveness and operability of the proposed method is proved by a case study.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"46 ","pages":"Article 100859"},"PeriodicalIF":10.4,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143928759","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":"A computer vision-based approach for identification of non-metallic inclusions in the steel industry products","authors":"Surya Prakash Mishra ,&nbsp;Ashok Kamaraj ,&nbsp;V Rajinikanth ,&nbsp;M R Rahul","doi":"10.1016/j.jii.2025.100860","DOIUrl":"10.1016/j.jii.2025.100860","url":null,"abstract":"<div><div>Identification of microstructures is the core of materials engineering. Artificial intelligence's application in materials engineering has recently shown the possibility of realizing complicated tasks. Identifying elemental distribution in microstructure requires experimentation or computationally intensive modeling techniques. The current work focuses on the question, can artificial intelligence predict elemental distribution in a microstructure? The case study was selected from the steel industry. Making steel will cause different inclusions; identifying them is essential for qualifying the steel for applications. The current study develops a unique computer vision-based architecture by integrating Swin Transformer and U-Net architecture to identify the inclusions. The developed model can predict the type of inclusion in the steel by generating the elemental distribution images. The model is compared with the possible available architectures in the literature. The new model shows the lowest mean absolute error of 0.0529, root mean squared error of 0.0902, mean squared error of 0.0081, and the highest structural similarity (SSim) value of 0.68965 and an intersection over union (IoU) of 1 when images are binarised.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"46 ","pages":"Article 100860"},"PeriodicalIF":10.4,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143923717","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":"Exploring the applicability of industry 4.0 technologies in oil and gas pipeline leakage monitoring: Results from an empirical study","authors":"Ismail W.R. Taifa,&nbsp;Rehema Adam Mahundi,&nbsp;Victoria Mahabi","doi":"10.1016/j.jii.2025.100857","DOIUrl":"10.1016/j.jii.2025.100857","url":null,"abstract":"<div><div>This study explored the applicability of Industry 4.0 (I4.0) technologies in oil and gas (O&amp;G) pipeline leakage monitoring (PLM) in Tanzania. Specific objectives identified factors affecting the adoption of I4.0 technologies in the O&amp;G PLM, evaluated the maturity of I4.0 within the industry, and proposed strategies to enhance the adoption of I4.0 technologies for PLM. A mixed-methods design gathered qualitative and quantitative data. One hundred and seven (107) experts purposively selected were engaged in exploring the applicability of I4.0 technologies. IBM SPSS 26 and AMOS 23 software analysed the gathered data. The analysis revealed six pillars of I4.0 technologies applicable for monitoring O&amp;G pipelines. Those pillars included autonomous robots, augmented reality, additive manufacturing, the Internet of Things, cloud computing and artificial intelligence. The O&amp;G pipeline's maturity level was 3.1, indicating that the industry has begun integrating some I4.0 technologies into pipeline monitoring or leakage detection. Strategies obtained through experts' responses and 80–20 % analysis that tackle technological, financial, regulatory, and psychological constraints were proposed to enhance I4.0ʼs full adoption in PLM. Strategies developed were building international partnerships, building international cooperation, building a workforce, creating digital platforms, promoting a friendly industry culture and state support for investors. The study only identified applicable I4.0 technologies for pipeline monitoring or leakage detection. Further study can be conducted to analyse to what extent they are utilised and can be utilised in O&amp;G PLM. Furthermore, there has been limited literature on the O&amp;G industry; hence, further studies can explore the industry in the downstream and midstream sections.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"46 ","pages":"Article 100857"},"PeriodicalIF":10.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143916505","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":"Inland waterway autonomous transportation: System architecture, infrastructure and key technologies","authors":"Hualong Chen ,&nbsp;Yuanqiao Wen ,&nbsp;Yamin Huang ,&nbsp;Lihang Song ,&nbsp;Zhongyi Sui","doi":"10.1016/j.jii.2025.100858","DOIUrl":"10.1016/j.jii.2025.100858","url":null,"abstract":"<div><div>With the rapid development of intelligent inland waterway shipping and autonomous vessel, the development of inland waterway autonomous transportation has become a hot topic in the shipbuilding industry and the maritime field. This study comprehensively discusses the development trends and technical challenges of inland waterway autonomous transportation from three aspects: system architecture, advanced infrastructure, and key technologies. Firstly, based on the Cyber-Physical-System theory, we propose a hierarchical architecture for the inland waterway autonomous transportation system. Second, based on the full-life-cycle management and control theory, we put forward the autonomous operation process for the planning, design, construction, operation, management, and control of the inland waterway autonomous transportation system. Then, we discuss the advanced infrastructure of the inland waterway autonomous transportation system, including perception, communication, computing, navigation scene maps, high-precision positioning, and so on. Finally, we summarize the key technologies and development challenges for implementing the inland waterway autonomous transportation system, such as autonomous vessels, the Internet of Ships, cloud-edge collaborative computing, artificial intelligence, communication, and channel geographic information systems. This research provides a potential framework and technical solutions for the inland waterway autonomous transportation system, contributing to the rapid development of the inland waterway shipping economy.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"46 ","pages":"Article 100858"},"PeriodicalIF":10.4,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143894822","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":"Integrating LOPCOW-DOBI method and possibilistic programming for two-stage decision making in resilient food supply chain network","authors":"Pavan Sharma ,&nbsp;B. Nila ,&nbsp;Dragan Pamucar ,&nbsp;Jagannath Roy","doi":"10.1016/j.jii.2025.100847","DOIUrl":"10.1016/j.jii.2025.100847","url":null,"abstract":"<div><div>This study focuses on resilient supplier selection and order allocation — two crucial aspects of modern supply chain management (SCM). Globalization and strategic sourcing expose supply chains to disruptions, making resilient sourcing strategies essential for adapting to fluctuations in supply and demand. This paper proposes a novel integrated hybrid model that combines multi-attribute decision-making (MADM) with a possibilistic multi-objective programming model (PMOPM) to enhance decision-making in supply chain resilience (SCR). In the first stage, we present an MADM model that integrates the LOgarithmic Percentage Change-driven Objective Weighting (LOPCOW) and DOmbi Bonferroni (DOBI) methods. The LOPCOW-DOBI method enables decision-makers (e.g., purchasing team) to evaluate and rank multiple suppliers based on normal business criteria (<span><math><mrow><mi>N</mi><mi>B</mi><mi>C</mi></mrow></math></span>) and resilient pillars (<span><math><mrow><mi>R</mi><mi>P</mi><mi>s</mi></mrow></math></span>). In the second stage, a PMOPM is employed to determine optimal order allocations when supply, demand, and cost parameters are fuzzy in nature. The quantitative and qualitative evaluation of decision-makers’ opinions is integrated into mathematical optimization by combining the MADM output with PMOPM. Using the <span><math><mi>ϵ</mi></math></span>-constraint method, the model was optimized to obtain Pareto solutions, with the final solution identified via the global criteria method. A real-world food industry case study validated the MADM-PMOPM model. Our results show that suppliers with higher resilience performance receive larger orders. Sensitivity analysis confirms that the two-stage model consistently delivers stable and adaptable solutions. Comparative analysis further demonstrates that the proposed approach is effective and reliable for rational decision-making.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"46 ","pages":"Article 100847"},"PeriodicalIF":10.4,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143906401","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 model for Industry 5.0: A holistic approach to readiness and integration","authors":"Cesar Cuevas-Lopez-de-Baro,&nbsp;Ignacio Mira-Solves,&nbsp;Antonio Verdú-Jover","doi":"10.1016/j.jii.2025.100855","DOIUrl":"10.1016/j.jii.2025.100855","url":null,"abstract":"<div><h3>Purpose</h3><div>This study proposes a novel assessment model tailored to the unique requirements of Industry 5.0 transformation utilizing Socio-Technical Theory as a framework. The model seeks to give organizations actionable insights into navigating the complex socio-technical dynamics of I5.0, evaluating organizational readiness holistically, and guiding their transition to this new industrial paradigm across different perspectives and dimensions. By fostering this holistic approach to Industry 5.0 adoption, our study aims to impact industry, society, and institutions significantly.</div></div><div><h3>Methodology</h3><div>The model was developed using a two-phase research approach. The first phase involved a detailed systematic literature review and a systematic literature network analysis to identify the perspectives, critical dimensions, and guiding questions for the assessment based on Socio-Technical Theory. The second phase refined the preliminary model through expert feedback from industry specialists.</div></div><div><h3>Findings</h3><div>Our research proposed an assessment model for Industry 5.0, confirmed Socio-Technical Theory as a substantive framework, and identified four core perspectives: strategy, sustainability, human-centricity, and resilience. These perspectives encompass 25 critical dimensions, which were further analyzed through 64 guiding questions. Expert feedback validated the need for the model and highlighted its potential for application from both a strategic and a tactical perspective.</div></div><div><h3>Originality</h3><div>This study addresses a crucial gap in the literature by presenting a novel assessment model tailored to the unique challenges and opportunities presented by this emerging industrial paradigm. The model’s emphasis on holistic readiness and the integration of business strategies goes beyond conventional approaches by fostering a more nuanced understanding of the socio-technical dynamics shaping the future of industry and society. By applying Socio-Technical Theory (STT), this study provides a more comprehensive understanding of the socio-technical dynamics underlying I5.0 transformation. This innovative approach provides organizations with actionable insights into navigating the intricate interplay between advanced technologies, human factors, sustainability principles, and resilience strategies in complex situations, thus advancing both theoretical and practical knowledge in the field of Industry 5.0.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"46 ","pages":"Article 100855"},"PeriodicalIF":10.4,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143912551","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":"Hybrid deep learning based threat intelligence framework for Industrial IoT systems","authors":"Jahanzaib Malik ,&nbsp;Adnan Akhunzada ,&nbsp;Ahmad Sami Al-Shamayleh ,&nbsp;Sherali Zeadally ,&nbsp;Ahmad Almogren","doi":"10.1016/j.jii.2025.100846","DOIUrl":"10.1016/j.jii.2025.100846","url":null,"abstract":"<div><div>The exponential growth of Industrial Internet of Things (IIoT) is a major driving force behind Industry 4.0. Besides complete automation and transformation, industrial IoT has so far created plenty of opportunities in several sectors 1.3such as smart manufacturing, energy, healthcare, smart agriculture, retail, supply chain, and transportation. However, the increased pervasiveness, reduced human involvement, resource-constrained nature of underlying IoT devices, dynamic and shared spectrum of 4G/5G communication, and reliance on the cloud for outsourced massive storage and computation bring novel security challenges and concerns. A significant challenge currently confronting the Industrial Internet of Things (IIoT) is the increasing prevalence of sophisticated IoT malware threats and attacks. To address this, the authors propose a hybrid threat intelligence framework that is not only highly scalable but also incorporates self-optimizing capabilities, enabling it to counteract a wide range of persistent cyber threats and attacks targeting IIoT systems. For a comprehensive evaluation, the authors utilized the state-of-the-art TON_IIoT dataset, which includes over 3 million instances representing various adversarial patterns and threat vectors. In addition, both standard and extended performance evaluation metrics were employed to ensure a thorough assessment. The proposed approach was also compared against several contemporary deep learning-based architectures and existing benchmark algorithms. The results indicate that the proposed method achieves superior detection accuracy, with only a minimal compromise in speed efficiency. Finally, a 10-fold cross-validation was conducted to provide an unbiased evaluation of the framework’s performance.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"45 ","pages":"Article 100846"},"PeriodicalIF":10.4,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143847187","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}