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.

Smart manufacturing systems are a new paradigm in Industry 4.0 driven by the emerging information and communication technology and artificial intelligence that converge to digital twin, which are able to perceive, recognize, and handle the changes in demand and production. Reconfigurable machine tools (RMTs) can promote the flexibility of smart manufacturing systems. The fundamental problem lies in dynamically reconfiguring the RMTs in smart manufacturing systems efficiently and accurately by considering the flexibility of production precedence and operation sequences simultaneously. Therefore, in this article, a deep reinforcement learning-based reconfiguration planning method of digital twin-driven smart manufacturing systems with RMT is proposed to seek optimal reconfiguration policy online. The reconfiguration processes of smart manufacturing systems are modeled by considering reconfiguration cost, moving cost, and processing cost. DeepQ-network is adopted to explore the state space and action space to find the optimal reconfiguration scheme with the highest return. An industry case study is presented to demonstrate the effectiveness and efficiency of the proposed method, where the reconfiguration processes of a smart manufacturing system consisting of five RMTs for producing four parts are discussed.

With the extensive application of automated guided vehicles, real-time production scheduling considering logistics services in cloud manufacturing (CM) becomes an urgent problem. Thus, this study focuses on the distributed real-time scheduling (DRTS) of multiple services to respond to dynamic and customized orders. First, a DRTS framework with cloud–edge collaboration is proposed to improve performance and satisfy responsiveness, where distributed actors and one centralized learner are deployed in the edge and cloud layer, respectively. And, the DRTS problem is modeled as a semi-Markov decision process, where the processing services sequencing and logistics services assignment are considered simultaneously. Then, we developed a distributed dueling deep Q network (D3QN) with cloud–edge collaboration to optimize the weighted tardiness of jobs. The experimental results show that the proposed D3QN obtains lower weighted tardiness and shorter flow-time than other state-of-the-art algorithms. It indicates the proposed DRTS method has significant potential to provide efficient real-time decision-making in CM.

Currently, tacit knowledge has attracted increasing research attention. However, the theoretical foundation of tacit knowledge is still not well formulated, because the researches are very disperse. This work provides a review of the current researches. First, the definition of tacit knowledge is discussed by answering several questions. Next, tacit knowledge sharing, tacit knowledge quantization are identified as two research topics in the current research community. Following that, the technical progress of each topic is summarized and analyzed. Finally, we provide a thumbnail of the researches and identify three research consensuses to answer where we are. While, seven research directions are identified to answer where we shall go.