Journal of Industrial Information Integration最新文献

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.

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.

This paper provides a perspective on performance-based decision support. The chosen approach is based on the principles of “Physics of Decision”, which considers the performance of a system as a physical trajectory within the boundaries of its performance indicators that might be deviated through variation of system parameters. According to the overall premise of employing the state-space method to simulate physical systems, this work presents a decision aggregation method in dynamic systems. The core contribution is to propose a multi-criteria performance framework to manage multi-input-multi-output (MIMO) system performance with a combination of affordable decisions. A nonlinear inventory-workforce management model has been used to demonstrate the proposed approach.

Data collected in Industry 4.0 applications must be converted into tabular datasets before they can be processed by analysis algorithms, as in any data analysis system. To perform this transformation, data scientists have to write complex and long scripts, which can be a cumbersome process. To overcome this limitation, a language called Lavoisier was recently created to facilitate the creation of datasets. This language provides high-level primitives to select data from an object-oriented data model describing data available in a context. However, industrial engineers might not be used to deal with this kind of model. So, this work introduces a new set of languages that adapt Lavoisier to work with fishbone diagrams, which might be more suitable in industrial settings. These new languages keep the benefits of Lavoisier, reducing dataset creation complexity by 40% and up to 80%, and outperforming Lavoisier in some cases.

In semiconductor manufacturing, photo-etching and acid-etching processes play crucial roles in precisely crafting intricate patterns on storage chip wafers. However, these processes generate significant hazardous waste, leading to high disposal costs. Consequently, there has been increased research into cost-effective and environmentally friendly solutions. This study introduces a custom green scheduling framework inspired by sustainable practices in storage chip wafer fabrication. The framework optimizes the coordination of photo-etching and acid-etching processes, taking into account wait time limits and capacity constraints. Additionally, we propose an efficient hybrid meta-heuristic algorithm designed to reduce energy consumption, minimize makespan, and optimize the quantity of acid-etching baths. This approach identifies the most efficient number of acid-etching tanks for various scenarios, significantly reducing waste production. Empirical results demonstrate the algorithm's effectiveness, highlighting its potential for substantial cost savings and waste reduction. By exploring the relationship between the number of acid-etching tanks and makespan, we identify the optimal tank quantity for diverse scenarios. These insights pave the way for enhanced resource efficiency and sustainable practices in semiconductor manufacturing.

The Industrial Internet of Things (IIoT) plays a pivotal role in steering enterprises towards comprehensive digital transformation and fostering intelligent production, which serves as a critical pillar of Industry 4.0. Digital twin (DT) emerges as a highly promising technology, enabling the digital transformation of the IIoT by seamlessly bridging physical systems with digital spaces. However, the overall service quality of the IIoT is severely impacted by the resource-limited devices and the massive, heterogeneous and sensitive data in the IIoT. As an innovative distributed machine learning paradigm, federated learning (FL) inherently possesses advantages in handling private and heterogeneous data. In this paper, we propose a novel framework integrating FL with DT-enabled IIoT, termed FDEI, which combines the merits of both to improve service quality while maintaining trustworthiness. To enhance the modeling efficiency, we develop FedOA, an adaptive optimization FL method that dynamically adjusts the local update coefficient and model compression rate in resource-limited IIoT scenarios, to construct the FDEI model. Specifically, leveraging the interdependence between the two variables, we conduct a theoretical analysis of the model convergence rate and derive the associated convergence bounds. Building upon the theoretical analysis, we further propose a joint adaptive adjustment strategy by optimizing the two variables across various clients to minimize runtime differences and accelerate the convergence rate. Numerical results demonstrate that our proposed approach achieves an approximate 68% improvement in convergence speed and a reduction of approximately 66% in traffic consumption compared to the benchmarks (e.g., FedAvg, AFL, and CSFL).

Optical 3D measuring systems serve as indispensable tools for the measurement and quality control of complex objects feeding process chains in industrial information integration. However, the accuracy of 3D measurements is influenced by a multitude of parameters, and the associated measurement uncertainties and influential factors remain insufficiently researched. This study investigates the effects of measurement object properties and software on measurement outcomes. Specifically, we examine seven geometries (diameter, distance, roundness, concentricity, flatness, parallelism and verticality) and four influencing factors (surface roughness, coating, polygonization, and interpolation). Our analysis employs variance analysis and compares the results with those obtained through linear regression using machine learning. In conclusion, the analysis of measurement uncertainty for optical 3D measurement systems in the assessment of seven distinct geometric characteristics provides a framework for determination of process chain suitability of the optical 3D measuring system.

The concept of Digital Twin (DT) is increasingly applied to systems on different levels of abstraction across domains, to support monitoring, analysis, diagnosis, decision making and automated control. Whilst the interest in applying DT is growing, the definition of DT is unclear, neither is there a clear pathway to develop DT to fully realise its capacities. In this paper, we revise the concept of DT and its categorisation. We propose a DT maturity matrix, based on which we propose a model-based DT development methodology. We also discuss how model-based tools can be used to support the methodology and present our own supporting tool. We report our preliminary findings with a discussion on a case study, in which we use our proposed methodology and our supporting tool to develop an extensible DT platform for the assurance of Electrical and Electronics systems of space launch vehicles.

Industrial assets management has a key role in ensuring continuity of industrial production via purchasing, warehousing and maintenance of devices and spare parts used in manufacturing, but it suffers from data and information accessibility, completeness, consistency, interoperability, and timeliness challenges in the customer - equipment vendors interaction and information sharing. Current practices in devices and spare parts purchasing, warehousing and maintenance include a lot of manual information search, validation and update work done at the manufacturing companies to maintain high quality master data records for the production assets management. This article addresses the challenge of improving efficiency and quality of operational production assets information management in industry customer company interaction with their production equipment vendors, with a design science research approach. The research was executed in collaboration between research organizations, forest industry companies, industrial production equipment vendors and information technology (IT) companies on two parallel and complementary research tracks, which are described with their key research results. The contributions are an ex-ante evaluated digital marketplace concept with preliminary requirements, and an ex-post evaluated semi-automated master data harmonization tool, as artefacts for improving efficiency and quality of production assets information management in industrial customer–vendors interaction. As theoretical contribution, the article provides a synthesis of the research results in the framework of information systems (IS) design theory as a nascent and novel design theory on designing IS artifacts for improving efficiency and quality of production assets information management, both in single industrial customer company and ecosystem settings. The contributions provide a starting point for further research and development of a digital marketplace for improving production assets information management in industrial customers – vendors interaction, potentially also applicable for similar ecosystems in other continuous production process -based manufacturing industries.

Interesting opportunities lie in digital technologies, such as blockchain, as enablers of further digital development in maritime logistics processes. Marine companies must assess their digital maturity using appropriate models to gain a competitive advantage through digital transformation. Therefore, it is crucial to present a novel and empirical maturity model for assessing blockchain implementation in maritime logistic organizations. This paper proposes a three-stage digital maturity model that effectively measures digital readiness in maritime logistics industries. The model consists of 5 criteria, 10 dimensions, 45 maturity items, and 5 maturity levels. To exemplify the maturity level within its operations, the model is applied to a case study of Shahid Rajaee Port in Iran. The fuzzy theory and decision-making approaches are used to calculate digital readiness and test the proposed model. The research findings reveal a significant gap in digital maturity studies in the field of shipping and ports. By integrating organizational aspects, our model provides a comprehensive approach to understanding and improving blockchain capabilities within the maritime logistics sector, given that there is no comprehensive maturity model to evaluate the readiness of maritime logistics to adopt blockchain practices.