Journal of Industrial Information Integration最新文献

{"title":"Retraction notice to “An edge-computing based industrial gateway for industry 4.0 using ARM TrustZone technology” [Journal of Industrial Information Integration 33 (2023) 100441]","authors":"Sandeep Gupta","doi":"10.1016/j.jii.2024.100669","DOIUrl":"10.1016/j.jii.2024.100669","url":null,"abstract":"","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"41 ","pages":"Article 100669"},"PeriodicalIF":10.4,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2452414X24001146/pdfft?md5=8697d208f9d4ca611e206351caf52d78&pid=1-s2.0-S2452414X24001146-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141915183","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 critical review of global best practices elements in digital technologies: Advancing a theoretical architecture for quality engineering","authors":"Lungelo Ntobongwana,&nbsp;Arnesh Telukdarie","doi":"10.1016/j.jii.2024.100665","DOIUrl":"10.1016/j.jii.2024.100665","url":null,"abstract":"<div><p>Technological advancements and changes in local, regional, and global markets have prompted organizations operating in the quality assurance environments to develop competitive strategies that enhance operational excellence and business sustainability. The current global norm is digital technologies as a key enabler of competitive strategies. The ability to develop and integrate digital into quality assurance environments is a challenge with considerable focus on the development of a theoretical architecture.</p><p>This study aims at identifying elements influencing the adoption of digital technologies, through a review of global best practices applicable to the integration of digital technologies in quality assurance environments. This study further aims at the consolidation of all elements into the development of an contemporary theoretical architecture specific to the quality assurance environment.</p><p>This qualitative study utilised a systematic literature review (SLR) of papers published from 2012 to 2022 to assess the elements influencing the adoption of digital technologies and global best practices in quality assurance environments. In addition, to ensure that the most relevant theoretical architecture was developed in this study, a Resource-Based View, together with Dynamic Capabilities theory, and Technology Acceptance Modelling are adopted to ground the study.</p><p>Key elements influencing the adoption of integrated digital technologies are identified from the results obtained from the SLR. Furthermore, the SLR identifies several theoretical frameworks that combine business sustainability and quality assurance environments principles, with Digital Technologies. Based on the review outcome, a theoretical architecture is developed for adoption in a quality assurance environment for organizations operating in the service sector.</p><p>This study provides a new perspective on the elements influencing the adoption of integrated digital technologies in a quality assurance environment advancing business sustainability. Further this study also exhumes the Implications with regards to a theoretical architecture for quality assurance organizations that seek to adopt integrated digital technologies and operate within a service.</p></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"41 ","pages":"Article 100665"},"PeriodicalIF":10.4,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2452414X24001092/pdfft?md5=cb5c2e2db50bcb2b7bd1bbe5fc94306a&pid=1-s2.0-S2452414X24001092-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141842375","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":"Knowledge-based expert system to drive an informationally interoperable manufacturing system: An experimental application in the Aerospace Industry","authors":"Anderson Luis Szejka ,&nbsp;Osiris Canciglieri Junior ,&nbsp;Fernando Mas","doi":"10.1016/j.jii.2024.100661","DOIUrl":"10.1016/j.jii.2024.100661","url":null,"abstract":"<div><p>The industrial revolutions have challenged organisations to rethink their product design and manufacturing processes, making them faster and more connected to market demands and changes. Digital technologies have emerged with solutions to virtual represent physical objects, processes, systems, or assets to simulate and analyse the impact of manufacturing changes before actual implementation. However, the challenge is to deal with thousands of heterogeneous information sets which must be shared simultaneously by different groups within and across institutional boundaries. Each manufacturing industry has its format and model to represent the product in the development, manufacturing process, material features, etc. In this context, this paper explores a knowledge-based expert system to support the information exchange and inconsistency detection across the manufacturing process, specifically in an experimental application in the Aerospace Industry. The proposed framework was based on knowledge formalisation and semantic rules through ontologies, semantic reconciliation strategies and connectivity interfaces to manage information and knowledge and identify inconsistencies across the manufacturing system. It was mainly evaluated across the product and manufacturing design of sheet metal forming aluminium thin wall parts for the aerospace industry. Results demonstrate the capability of the approach to enhance data accuracy, coherence, and efficiency throughout the manufacturing of complex products. However, the solution presents challenges such as interdisciplinary collaboration in product design, specific information requirements for manufacturing planning, and the impact of production planning on manufacturing capacities.</p></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"41 ","pages":"Article 100661"},"PeriodicalIF":10.4,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141851248","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":"Enhancing SMEs digital transformation through machine learning: A framework for adaptive quality prediction","authors":"Ming-Chuan Chiu,&nbsp;Yu-Jui Huang,&nbsp;Chia-Jung Wei","doi":"10.1016/j.jii.2024.100666","DOIUrl":"10.1016/j.jii.2024.100666","url":null,"abstract":"<div><p>As smart manufacturing expands, businesses see the importance of digital transformation, especially for small and medium-sized enterprises (SMEs). Unlike larger companies, SMEs face greater challenges when undergoing digital transformation due to technological questions. However, recent advancements in high-performance computing and reduced hardware costs have made deep learning-based digital transformation more financially feasible for SMEs. While previous research utilized machine learning for product quality prediction, there remains a lack of comprehensive in adaptive quality prediction specifically designed for SMEs. This study presents a systematic framework utilizing various machine learning methods and validates research cases using CRISP-DM (Cross-Industry Standard Process for Data Mining). The first step involves applying XGBoost(eXtreme Gradient Boosting)for feature selection, the second step utilizes GRU for parameter prediction. Finally, in the third step, SVM (Support Vector Machine) is employed for quality classification. The integrated framework achieves high accuracy, with <span><math><msup><mi>R</mi><mn>2</mn></msup></math></span>reaching 90 % for predicted parameters and nearly 95 % for classification indicators. Moreover, this research addresses the research gap in quality prediction and adaptability and provide an effective digital transformation solution for SMEs without substantial investment. The proposed research framework can be applied SMEs of other domains, such as the machining and traditional manufacturing industry.</p></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"41 ","pages":"Article 100666"},"PeriodicalIF":10.4,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141841659","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 systematic solution of distributed and trusted chain-network integration","authors":"Yansheng Chen ,&nbsp;Pu Jian ,&nbsp;Yin Zhang ,&nbsp;Jie Li ,&nbsp;Zhongkun Wu ,&nbsp;Zhonghao Liu","doi":"10.1016/j.jii.2024.100664","DOIUrl":"10.1016/j.jii.2024.100664","url":null,"abstract":"<div><p>Blockchain, with its characteristics of decentralization, transparency, openness, and intangibility, has become the preferred choice for enhancing the credibility of the industrial cluster network platform. Industrial clusters are an important organizational form for developing small and medium-sized enterprises, and the information service platform plays a key role. This paper constructs a trusted paradigm model and a new trusted framework for the industrial cluster network platform and proposes an adaptive distributed network scheme. Specifically, it includes a decentralized and non-repudiable solution for the autonomous scenario of the industrial cluster Industrial Internet, promoting the transformation of the network into a self-adjusting and self-managing distributed network, enhancing the credibility of the platform, and reducing the negative impact of distrust and information asymmetry; it proposes three models (private chain, alliance chain, and public chain) for the integration of blockchain and the Industrial Internet, providing a secure and reliable support platform for the development of industrial clusters, solving the problem of information asymmetry, promoting trust and synergy, and maximizing the synergistic effect; from the perspective of security and reliability, it deeply analyzes the industrial cluster network platform, proposes a trusted framework, and realizes the data layer and application layer of the network with the help of blockchain technology. The experimental results show that these models meet the requirements of the industrial cluster in terms of data privacy, control, and credibility and have a positive significance for promoting the development and digital transformation of the industrial cluster.</p></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"41 ","pages":"Article 100664"},"PeriodicalIF":10.4,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141850054","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":"Quantum financing system: A survey on quantum algorithms, potential scenarios and open research issues","authors":"Yang Lu,&nbsp;Jiaxian Yang","doi":"10.1016/j.jii.2024.100663","DOIUrl":"10.1016/j.jii.2024.100663","url":null,"abstract":"<div><p>Quantum financing system is a foreseeable future of finance, which will demonstrate more agile, accurate, and secured performance. Financial markets and related activities are changing dynamically, and quantum computing is one of the emerging technologies responding to financial developments. We attempt to outline a theoretical framework to illustrate a quantum financing system, including quantum financing algorithms, quantum financing circuits, and potential financial scenarios. Since quantum computer is still in the early stage, challenges and future directions are also discussed from a technological and operational perspective. This study provides a resource for researchers, practitioners and policymakers interested in empowering quantum computing to revolutionize financial services and systems. The research is one of the foundational studies that describe future ecology of quantum financing system.</p></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"41 ","pages":"Article 100663"},"PeriodicalIF":10.4,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141708798","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":"Combined deep-learning optimization predictive models for determining carbon dioxide solubility in ionic liquids","authors":"Shadfar Davoodi ,&nbsp;Hung Vo Thanh ,&nbsp;David A. Wood ,&nbsp;Mohammad Mehrad ,&nbsp;Mohammad Reza Hajsaeedi ,&nbsp;Valeriy S. Rukavishnikov","doi":"10.1016/j.jii.2024.100662","DOIUrl":"10.1016/j.jii.2024.100662","url":null,"abstract":"<div><p>This study explores the development of predictive models for carbon dioxide (CO₂) solubility in ionic liquids based on a compiled dataset of 10,116 experimentally measured data points involving four input variables: pressure (P), temperature (T), cation type, and anion type. The deep-learning (DL) predictive models evaluated are standalone and hybrid versions of convolutional neural network (CNN) and long short-term memory (LSTM) algorithms with cuckoo optimization algorithm (COA) and gradient-based optimization (GBO). The laboratory-measured data was separated into training and test categories, and each category was normalized separately to improve the performance of the deep learning algorithms. The Mahalanobis distance-based quantile method was utilized to identify any outliers in the training data. Once identified, the outlier data points were eliminated from the training dataset. The control parameters of the deep learning algorithms were optimized using COA to enhance their efficiency, and the algorithms were hybridized with optimization algorithms to further improve their performance. The resulting models were analyzed to assess their accuracy, degree of overfitting, and the importance of input features. The study found that using 80% of the data for training and 20% for testing results in more accurate and generalizable models. Using the outlier detection method on the training data led to 307 data points being eliminated as outliers. Developing CO₂-solubility predictive model showed that, the CNN<img>COA model had the lowest RMSE and highest R² among the developed models, indicating high generalizability for data unseen by the trained model. The analysis revealed that using optimization algorithms increased the CO₂-solubility prediction performance of DL algorithms and reduced overfitting. T and cation type were the most and least important input features, respectively. Simultaneous changes in cation and anion type on CO₂-solubility predictions displayed no systematic pattern. For increases in T, CO₂ solubility typically decreased, whereas for increases in P CO₂ solubility always increased but at variable rates. The results of this study can be used to develop accurate and generalizable CO₂-solubility predictive models for various applications.</p></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"41 ","pages":"Article 100662"},"PeriodicalIF":10.4,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141639257","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":"Group decision making method for third-party logistics management: An interval rough cloud optimization model","authors":"Musavarah Sarwar ,&nbsp;Muhammad Akram ,&nbsp;Wajeeha Gulzar ,&nbsp;Muhammet Deveci","doi":"10.1016/j.jii.2024.100658","DOIUrl":"10.1016/j.jii.2024.100658","url":null,"abstract":"<div><p>Group decision making in third-party logistics service selection plays an essential role for improving service quality, increasing efficiency and reducing the net cost. Fuzzy and uncertain linguistic variables are commonly used to represent experts‘rankings in optimization problems. To recognize the limits of human cognition and subjectivity of human evaluations, several optimization approaches have been studied to select remanufacturing alternatives in decision making processes, however these methods have certain deficiencies such as lacking manipulation tools of diverse information, randomness, use of predefined parameters increasing uncertainty, interpersonal relations among evaluation criteria. The integration of interval numbers, rough approximations, and cloud model theory plays a significant role to model incomplete and inadequate information occurring in decision making problems. This research paper focuses on the integration of dual interval rough integrated cloud model with best-worst optimization technique, Multi-Attributive Border Approximation area Comparison (MABAC) and Weighted Aggregated Sum Product Assessment (WASPAS) approaches. A novel min–max optimization model based dual interval rough integrated cloud values is designed to compute the weight coefficients and consistency ratio for each criteria. The consistency of proposed optimization model is checked using a consistency ratio test. Secondly, the alternatives are ranked using the proposed DIRI cloud based MABAC and WASPAS approaches using interval clouds based weighted sum and weighted product coefficients, approximation area values and distance formulae. The significance of the proposed model is highlighted with a case study of third-party logistics service management of an electronic firm. The rationality and out-performance of the proposed methodology is studied by a comparative analysis with existing approaches and detailed sensitivity analysis on different variations of criteria weights and parameter.</p></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"41 ","pages":"Article 100658"},"PeriodicalIF":10.4,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141622302","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 Cost-effective approach for quality control in PLA-based material extrusion 3D printing using 3D scanning","authors":"Andrea Montalti,&nbsp;Patrich Ferretti,&nbsp;Gian Maria Santi","doi":"10.1016/j.jii.2024.100660","DOIUrl":"10.1016/j.jii.2024.100660","url":null,"abstract":"<div><p>In this article, our aim is to underscore the importance of verifying that components produced through material extrusion additive manufacturing exhibit geometric and dimensional conformity with the STL (Standard Tessellation Language) model. Currently, the business world is heavily investing in additive technologies, but it is crucial to obtain feedback on the accuracy of the printed component without excessive economic expenditure. For this reason, we have opted to utilize a mid-range 3D scanner (Revopoint Mini with an accuracy of 0.02 mm) to investigate any disparities in print results using PLA material. Each model has been scanned and compared with the initial mesh to qualitatively and quantitatively assess the present errors. The analysis has revealed that the majority of features can be effectively controlled, while the remaining ones either fall within the tool's precision or necessitate a higher-quality scan. Particularly in the analysed case, flat surfaces, profiles of complex geometries, and holes have demonstrated dimensional and geometric controllability. However, details of reduced dimensions or those difficult to reach by the scanner do not allow for adequate comparison due to excessive standard deviation in the error. The analysed layer heights do not exhibit a significant impact on component accuracy.</p></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"41 ","pages":"Article 100660"},"PeriodicalIF":10.4,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2452414X24001043/pdfft?md5=756cdcd5a4e6ebe2047139e04b1b1f85&pid=1-s2.0-S2452414X24001043-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141703209","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":"Knowledge reuse for ontology modelling and application of maintenance motion state sequence","authors":"Qidi Zhou ,&nbsp;Dong Zhou ,&nbsp;Yan Wang ,&nbsp;Ziyue Guo ,&nbsp;Chao Dai","doi":"10.1016/j.jii.2024.100659","DOIUrl":"10.1016/j.jii.2024.100659","url":null,"abstract":"<div><p>With the current digital transformation and the development of complex manufacturing systems, advanced maintenance is proposed to improve the competitiveness of complex products, generating a large amount of heterogeneous maintenance data and information. There is a lack of standardized representations of motion-centred maintenance knowledge which leads to semantic ambiguity and poor intertranslatability. In addition, it causes subjective deviations and human resource investments in related time prediction applications. Therefore, a knowledge reuse method for ontology modelling and the application of maintenance motion state sequences is proposed. First, a framework for reusing maintenance motion state sequence (MMSS) knowledge is established, which is defined as the state sets of time-sequence maintenance motion. Second, maintenance motion state sequence ontology (MMSSO) is constructed to standardize the definition of MMSS, as a supplement to the current maintenance ontologies. Third, an MMSSO application for automatic maintenance time prediction is proposed by incorporating the standardized specifications of MMSSO and improving the MODAPTS method. Finally, using aviation equipment as an example, the rationality and superiority of MMSSO in real applications are verified. MMSSO is a new practice of integrating multi-source information in advanced maintenance. It can also provide predicted time as an iterative reference for industrial practitioners in the digital design stage.</p></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"41 ","pages":"Article 100659"},"PeriodicalIF":10.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141708363","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}