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. Deep Q -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.

Product development should cover product design, validation, and manufacturing. In traditional product development, physical validation based on physical trial manufacturing is the key step to confirm the design scheme before physical manufacturing. However, physical validation is costly and inefficient, which could be the main obstacle to achieving rapid product development. The emergence of digital twin provides an opportunity to accelerate product development by eliminating physical validation toward digital validation in the smart manufacturing era. Therefore, a framework of rapid product development based on digital twin is proposed in this paper. During product development, the new product is designed according to the new requirements in the virtual space, in which the existing digital twins of products can be referenced. Then, an ultrahigh-fidelity virtual manufacturing system is constructed for digital trial manufacturing based on the digital twin of the manufacturing system and the design scheme of the new product. An ultrahigh-fidelity digital prototype can be obtained from digital trial manufacturing for digital validation. The new product validation is executed on the digital prototype to test its performance. The digital validation results can be used to improve the design scheme of the new product and boost the corresponding manufacturing processes. In addition, the core characteristics and key technologies of rapid product development based on digital twin are discussed. Finally, a case study is presented to implement the proposed framework and to show the effectiveness of accelerating product development.

Currently, the fundamental tenets of systems engineering are supported by a model-based approach to minimize risks and avoid design changes in late development stages. The models are used to formalize, analyze, design, optimize, and verify system development and artifacts, helping developers integrate engineering development across domains. Although model-based development is well established in specific domains, such as software, mechanical systems, and electrical systems, its role in integrated development from a system perspective is still a challenge for industry. The model-based systems engineering (MBSE) tool-chain is an emerging technique in the area of systems engineering and is expected to become a next-generation approach for supporting model integration across domains. This article presents a literature review to highlight the usage and state of the art to generally specify the current understanding of MBSE tool-chain concepts. Moreover, the results are used for identifying the usage, advantages, barriers, concerns, and trends of tool-chain development from an MBSE perspective.

Model-based systems engineering (MBSE) provides an important capability for managing the complexities of system development. MBSE empowers the formalism of system architectures for supporting model-based requirement elicitation, specification, design, development, testing, fielding, etc. However, the modeling languages and techniques are heterogeneous, even within the same enterprise system, which leads to difficulties for data interoperability. The discrepancies among data structures and language syntaxes make information exchange among MBSE models more difficult, resulting in considerable information deviations when connecting data flows across the enterprise. Therefore, this article presents an ontology based upon graphs, objects, points, properties, roles, and relationships with extensions (GOPPRRE), providing metamodels that support the various MBSE formalisms across lifecycle stages. In particular, knowledge graph models are developed to support unified model representations to further implement ontological data integration based on GOPPRRE throughout the entire lifecycle. The applicability of the MBSE formalism is verified using quantitative and qualitative approaches. Moreover, the GOPPRRE ontologies are used to create the MBSE formalisms in a domain-specific modeling tool, MetaGraph, for evaluating its availability. The results demonstrate that the proposed ontology supports the formal structures and descriptive logic of the systems engineering lifecycle.

To address the problem of how to build quantitative evaluation index models that reflect the essential characteristics of reconfigurable manufacturing system (RMS) and rank alternative reconfiguration schemes, which possess both advantages and disadvantages, an evaluation method based on the preference ranking organization method for enrichment evaluation (PROMETHEE) is proposed. Based on a consideration of the reconfiguration of the reconfigurable machine components and manufacturing cells, quantitative models of the key characteristics of an RMS (scalability, convertibility, diagnosability, modularity, integrability, and customization) are established, after which the quantitative models are used as the basis for constructing an RMS evaluation index system. The analytic hierarchy process (AHP) is used to assign the weights for these indices. During the evaluation process, PROMETHEE I is first applied to analyze the advantages and disadvantages of each alternative scheme. Then, PROMETHEE II is adopted to analyze the net advantages of the schemes. Finally, all of the alternative configurations are ranked according to the analysis results above. The workshop of an institute that has both research and production capabilities was used as an example to validate the effectiveness and practicability of the proposed method. The example contains 10 alternative reconfiguration schemes, and each scheme consists of six evaluation indices. The computation result shows that quantitative models of six key RMS characteristics are equipped with the ability of quantitative description of the RMS reconfiguration scheme, which gives intuitive decision-making information combined with PROMETHEE, including advantage and disadvantage between alternative schemes, for a decision-maker to select the satisfactory configuration. In addition, only a 7.2 % data loss during the evaluation data processing means the rationality of the selected evaluation index and evaluation algorithm.

Reconfigurable machine tool (RMT) is the core facility of the reconfigurable manufacturing system (RMS), which can provide customised flexibility for RMS through reconfiguration. The reconfiguration of RMT is complicated due to unpredictable changes in demand and the flexibility of RMT, where new RMT should be designed to satisfy the new demand. The concept of digital twin of RMT is introduced to solve complex reconfiguration problems by executing reconfiguration experiments on high-fidelity virtual RMT. Considering the design processes of RMT during reconfiguration, three building blocks for digital twin of RMT should be studied thoroughly, including structure design, configuration generation, and configuration evaluation. First, the structure design of RMT for multi-part families is studied, including the design principles, module division, and design method. Second, the configuration generation process of RMT based on the results of the structure design is analysed, where quantitative description of configuration is proposed to facilitate the generation process. Third, configuration evaluation is presented to confirm the performance of each configuration based on kinematics analysis. Finally, a case study is provided to demonstrate the effectiveness of the proposed three building blocks for digital twin of RMT during reconfiguration to obtain suitable design scheme of RMT.