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.

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.

To address the problem of how to identify the best time to implement reconfiguration for the reconfigurable manufacturing system (RMS), a dynamic complexity-based RMS reconfiguration point decision method is proposed. This method first identifies factors that affect RMS dynamic complexity (including both positive and negative complexity) at the machine tool and manufacturing cell levels. Next, based on information entropy theory, a quantitative model for RMS dynamic complexity is created, which is solved via state probability analysis for processing capability and the processing function. This model is combined with cusp catastrophe theory to establish an RMS reconfiguration decision model. Both positive and negative complexity are control variables for cusp catastrophe. Cusp catastrophe’s state condition is used to identify RMS state catastrophe at the final stage of production. This catastrophe point is the RMS reconfiguration point. Finally, the case study result shows that this method can effectively identify the RMS state catastrophe moment so that system reconfiguration is implemented promptly to improve RMS’s responsiveness to the market.

To improve the convertibility of reconfigurable manufacturing system (RMS), the concept of delayed reconfigurable manufacturing system (D-RMS) was proposed. RMS and D-RMS are both constructed around part family. However, D-RMS may suffer from ultra-long system problem with unacceptable idle machines using generic RMS part families. Besides, considering the complex basic system structure of D-RMS, machine selection of D-RMS should be addressed, including dedicated machine, flexible machine, and reconfigurable machine. Therefore, a system design method for D-RMS based on part family grouping and machine selection is proposed. Firstly, a part family grouping method is proposed for D-RMS that groups the parts with more former common operations into the same part family. The concept of longest relative position common operation subsequence (LPCS) is proposed. The similarity coefficient among the parts is calculated based on LPCS. The reciprocal value of the operation position of LPCS is adopted as the characteristic value. The average linkage clustering (ALC) algorithm is used to cluster the parts. Secondly, a machine selection method is proposed to complete the system design of D-RMS, including machine selection rules and the dividing point decision model. Finally, a case study is given to implement and verify the proposed system design method for D-RMS. The results show that the proposed system design method is effective, which can group parts with more former common operations into the same part family and select appropriate machine types.