Model-based systems engineering (MBSE) allows system models to formalize end-to-end systems engineering implementation while developing complex engineering system. The evolution of MBSE models, including changes and conflicts, provides important historical knowledge to support design decisions. Model versioning is an efficient approach to manage the evolution of MBSE models. However, the heterogeneous data structure and semantics used in MBSE practices hinder the tool interoperability that is required in model versioning, which also decreases the effectiveness and efficiency of system development. This paper proposes a tool-chain for model versioning of MBSE models based on a cognitive digital thread (CDT). In this tool-chain, the graph–object–point–property-relationship-role-extension (GOPPRR-E) modeling approach is adopted because it is compatible with heterogeneous modeling languages used in model versioning. To promote tool interoperability, this tool-chain adopts the Open Services for Lifecycle Collaboration to support conflict detection or resolution during model versioning. In particular, knowledge graphs are generated along with the model versioning workflow to develop a CDT, which provides the cognitive reasoning ability required for model versioning behaviors. A case study of landing gear system development is used to evaluate the feasibility of the proposed tool-chain through qualitative and quantitative analyses. The results demonstrate that the proposed tool-chain has better efficiency than traditional model versioning using Git tools.

Cyber-physical systems (CPSs) integrate heterogeneous systems and process sensor data using digital services. As the complexity of CPS increases, it becomes more challenging to efficiently formalize the integrated multidomain views with flexible automated verification across the entire lifecycle. This article illustrates a model-based systems engineering tool-chain to support CPS development with an emphasis on automated parameter value selection for co-simulation. First, a domain-specific modeling approach is introduced to support the formalizations of CPS artifacts, development processes, and simulation configurations. The domain-specific models are used as the basis to generate a Web-based process management system for automated parameter value selections, which coordinates Open Services for Lifecycle Collaboration services of development information and technical resources (models, data, and tools) in order to support automated co-simulation. The services are deployed by a service orchestrator based on a decision-making algorithm for parameter value selection. Finally, developers make use of the WPMS to implement simulations and to select system parameter values for co-simulation automatically. The approach is illustrated by a case study on auto-braking system development and we evaluate the efficiency of this tool-chain by both qualitative and quantitative methods. The results show that parameter values are selected more efficiently and effectively when implementing co-simulations using our tool-chain.

Model-based systems engineering (MBSE) enables to verify the system performance using system behavior models, which can identify design faults that do not meet the stakeholders’ requirements as early as possible, thus reducing the R&D cost and error risks. Currently, different domain engineers make use of different modeling languages to create their own behavior models. Different behavior models are verified by different approaches. It is difficult to adopt a unified integrated platform to support the modeling and verification of heterogeneous behavior models during the conceptual design phase. This paper proposes a unified modeling and verification approach supporting system formalisms and verification. The KARMA language is used to support the unified formalisms across MBSE models and dynamic simulations for different domain specific models. In order to describe the behavior model more precisely and to facilitate verification, the syntax of hybrid automata is integrated into KARMA. We implemented behavior models and their verification in MetaGraph, a multi-architecture modeling tool. Finally, the effectiveness of the proposed approach is validated by two cases: 1) the scenario of booking railway tickets using BPMN models; 2) the behavior performance simulation of unmanned vehicles using a SysML state machine diagram.

Toward industry 4.0, modern manufacturing companies are aiming at building digital twins to manage physical assets, processes, people, and places. Since in this environment, massive amounts of data have been generated and collected, integration and management of various data sources is of paramount importance. In this context, cloud computing as a crucial part of Industry 4.0 facilitates distribution of computer resources without direct active management by users. Accordingly, an ontology enables efficient integration and management of data as a reference data model through representation of knowledge. Besides, data mining from massive data is very important to identify significant meaning of data, and to avoid unexpected errors through predictions from experiences described in data replica. This research addresses problems towards efficient integration and management of data for predictive maintenance.

Prodromos Kolyvakis, Alexandros Kalousis, Dimitris Kiritsis. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.

Knowledge modelling at industrial level consists an importunate activity nowadays due to the ceaseless advances in technologies and standards applied as well as the extensive amount of unrelated real-time and historical data at shop-floor level. A Common Interface Data Exchange Model (CIDEM) is hereby introduced towards unifying continuously produced data from heterogeneous and distributed information sources - on different levels and granularities - into a shared vocabulary that can unobtrusively communicate with industrial standards and protocols (i.e. B2MML, gbXML, MIMOSA). For further enhancing the information model a conceptual definition is employed leading to a semantically enriched model which enables more understandable high level knowledge diffusion. This model has been applied to various industrial applications, one of the most important being industrial safety, through incident recognition.

In this paper, we present the main concepts and the aim of the ManuSkills FoF project which is to study the use of enhanced ICT-based technologies and training methodologies to facilitate an increase of young talent interest in manufacturing and to support their training of new manufacturing skills. The project will experiment with a wide range of innovative delivery mechanisms such as serious games and teaching factory, supported by the use of social media augmented by gamification and leveraging the distribution channels preferred by young talent. In addition, the project will explore the pedagogical frameworks best suited to the personalization of individual learning needs taking into account the industrial demand. ManuSkills will address all three stages of the young talent pipeline (i.e. children, teenagers, young people), where in the early stages the focus will be to make manufacturing education more attractive to young talent, whilst in the later stages the focus will be to facilitate transformative deep learning of individuals, with reduced time-to-competence.

Increasing attention to the environment and resource sustainability has extended the traditional forward supply chain (SC) into closed-loop supply chain (CLSC). Meanwhile, with the fast development and application of ICTs, especially like wireless technology, product related information embedded in different phases of the SC is strongly supposed to be obtained and integrated so as to realize the closed-loop product information (CLPI) tracking and feedback (TAF). Based on an EU project, models of three levels of abstraction for the CLPI TAF using wireless technology are proposed to elucidate and decompose the whole scenario, which acts as a systematic approach to meet the requirements in this area.

The whole product life cycle consists of three phases: beginning of life (BOL), middle of life (MOL), and end of life (EOL). Although large amounts of product life-cycle data are generated over the whole product life cycle, data flows are rather vague after BOL. Over the last decade, however, emerging Internet, wireless mobile telecommunications, and product identification technologies have created the potential of making the whole product life cycle visible. As a result, the scope of data to be managed has expanded over the whole product life cycle. Hence, it becomes important to describe product life-cycle metadata in a systematic manner. Although much attention has been paid to data modeling over several objects such as products and processes, modeling methodology for product life-cycle metadata is not well developed. To cope with this limitation, we develop a modeling method for product life-cycle metadata by using the resource description framework (RDF). We define an RDF data model and its schema for describing and managing product life-cycle metadata. In addition, we describe how the proposed RDF model can be usefully applied to track, trace, and infer product life-cycle data with an RDF query language.