Journal of Industrial Information Integration最新文献

The fourth industrial revolution advocates the reformulation of industrial processes to achieve the end-to-end (provider-customer) digitalisation of the industrial sector. As is well known, the industrial environment is very complex, where legacy systems must interoperate and integrate with modern devices and sensors. Communication among them requires specific and costly developments, so architectures based on data sharing and services implementation are considered one of the most flexible and appropriate technological solutions to gradually achieve the desired horizontal and vertical integration of the value chain. The design and deployment of data-intensive applications is not straightforward, therefore this paper proposes a model-based tool to characterise the different elements to be configured in an application and to make its deployment easier by generating configuration, orchestration and deployment files and sending them to the corresponding nodes for their execution. In few words, this article highlights the advantages of distributed and data-centric architectures to face the challenge of integration and interoperability in data-intensive complex systems and presents the extension of the RAI4 metamodel proposed in Martínez et al. (2021) that now allows specifying how, containerised or not, and where, on the cloud, fog, edge or on-premise, each service can be hosted according to its functional and non-functional requirements, mainly issues related with real-time, security and cyber physical hardware dependencies. For the sake of comprehension, a pseudo-real use case addressed to pre-process and store pollution data from environmental sensors installed in a smart city is described in detail, including different deployment settings.

The advent of Industry 5.0 envisages production systems that are more resilient, embrace human–machine collaboration and promote sustainability driven by technological research. The development of supervision solutions for industrial equipment fills in this picture as a basis for more proactive Condition-Based Maintenance strategies. The goal of this paper is to provide a self-contained set of guidelines to design such supervision solutions. With respect to existing literature on the topic, we provide a design process with a strong focus on experimental data collection and failure reproduction activities. Moreover, the connections between the steps of the proposed process are clearly highlighted to guide the user. First, the paper provides a set of tools to select the critical items and the methodological approaches for supervision. Then, these tools are used and referenced in the proposed design process. Finally, the proposed process is exemplified on two industrial case studies to show its effectiveness. Considerations, hints, and a user guidelines are given at the end of most sections.

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.

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.

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.

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.

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.

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.

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 $R^2$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.

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.

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.

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 CNNCOA 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.

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.