Demystifying Digital Twin Buzzword: A Novel Generic Evaluation Model

Despite the growing popularity of digital twin (DT) developments, there is a lack of common understanding and definition for important concepts of DT. It is needed to address this gap by building a shared understanding of DT before it becomes an obstacle for future work. With this challenge in view, the objective of our study is to assess the existing DT from various domains on a common basis and to unify the knowledge and understanding of DT developers and stakeholders before practice. To achieve this goal, we conducted a systematic literature review and analyzed 25 selected papers to identify and discuss the characteristics of existing DT's. The review shows an inconsistency and case-specific choices of dimensions in assessing DT. Therefore, this article proposes a four-dimensional evaluation framework to assess the maturity of digital twins across different domains, focusing on the characteristics of digital models. The four identified dimensions in this model are Capability, Cooperability, Coverage, and Lifecycle. Additionally, a weight mechanism is implemented inside the model to adapt the importance of each dimension for different application requirements. Several case studies are devised to validate the proposed model in general, industrial and scientific cases.Comment: This is a draft of the article that subject to future change and correctio

Review and Alignment of Domain-Level Ontologies for Materials Science

The growing complexity and interdisciplinary nature of Materials Science research demand efficient data management and exchange through structured knowledge representation. Domain-Level Ontologies (DLOs) for Materials Science have emerged as a valuable tool for describing materials properties, processes, and structures, enabling effective data integration, interoperability, and knowledge discovery. However, the harmonization of DLOs, and, more generally, the establishment of fully interoperable multi-level ecosystems, remains a challenge due to various factors, including the diverse landscape of existing ontologies. This work provides, for the first time in literature, a comprehensive overview of the state-of-the-art of DLOs for Materials Science, reviewing more than 40 DLOs and highlighting their main features and purposes. Furthermore, an alignment methodology including both manual and automated steps, making use of Top-Level Ontologies’ (TLO) capability of promoting interoperability, and revolving around the engineering of FAIR standalone entities acting as minimal data pipelines (“bridge concepts”), is presented. A proof of concept is also provided. The primary aspiration of this undertaking is to make a meaningful contribution towards the establishment of a unified ontology framework for Materials Science, facilitating more effective data integration and fostering interoperability across Materials Science subdomains

CHAIKMAT 4.0 - Commonsense Knowledge and Hybrid Artificial Intelligence for Trusted Flexible Manufacturing

International audienceFlexible manufacturing plays an important role in Industry 4.0 for developing the factory of the future and requires enhanced planning, scheduling, and control. The quick and effective adaptation in the production line in response to customers’ requirements or face of unwanted situations will promote considerable flexibility in manufacturing. CHAIKMAT is a research project funded by the French National Agency of research that aims to add flexibility and transparency to manufacturing through trustful automatic decision-making. The project proposes a human-centric AI approach that investigates whether an available set of machines can perform a specific production process and then provides human experts with meaningful explanations of how the decision process is conducted. A hybrid predictive model, comprising of both semantic reasoning and machine learning system will help in real-time decision making through the automated analysis of two sources of information: a stream of machine-monitoring data describing the current state of the production line and a common-sense knowledge graph (MCSKG) that is modelled based on machine capability and process planning ontology model. Furthermore, this hybrid predictive model will also be able to explain its prediction so that the user can fully comprehend the rationale behind such a decision. In this paper, we will describe the architecture of the proposed system along with a detailed plan for verification. The paper also presents the state-of-the-art of AI applications in flexible manufacturing to establish how CHAIKMAT project aims to apply some of the novel AI methodologies to circumvent the existing gaps

Evaluation of Feedback among Multiple Scheduler Profiles in Fuzzy Genetic Scheduling

AbstractThis paper extends the earlier studies conducted on multiple scheduler profile in fuzzy genetic scheduling. Multiple schedulers can set up individual fuzzy membership bounds which results in different evaluation of multi-objective problem of single machine scheduling. A new software application enables feedback among schedulers by applying seeding of individual scheduler's population by best chromosomes from other scheduler's population. Few experiments are performed on the aforementioned software application to evaluate the performance of the multi objective single machine scheduling problem by varying the level and frequency of feedback. More improvement is observed as the frequency of the feedback is increased but no significant improvement is observed when the level is increased

The Translator in Knowledge Management for Innovation – A Semantic Vocation of Value to Industry

<p>This white paper has been written by members of the European Union H2020 project OntoCommons and external experts to introduce the concept of a Knowledge Management (KM) Translator - a new and essential function in the within knowledge management. This role bridges the gap between knowledge engineering, semantics, and the needs of materials and manufacturing industries.</p><p>Within industries, in which knowledge management plays a vital role or needs to be introduced, it is important to empower individuals to take on the KM Translator role and provide them with the necessary tools and skills to ensure success. This paper discusses the KM Translator role, requirements, and approaches for the successful implementation of knowledge management frameworks in the workplace.</p&gt