Smart manufacturing is the core idea of the fourth industrial evolution. For a smart manufacturing shop floor, real-time monitoring, simulation and prediction of manufacturing operations are vital to improve the production efficiency and flexibility. In this paper, the Cyber-Physical System (CPS) and Digital Twin technologies are introduced to build the interconnection and interoperability of a physical shop floor and corresponding cybershop floor. A Digital Twin-based Cyber-Physical Production System (DT-CPPS) is further established, and the configuring mechanism, operating mechanism and real-time data-driven operations control of DT-CPPS are discussed in detail. It is expected that DT-CPPS will provide the basis for shop floors to march towards smart manufacturing.

Rapid advances in new generation information technologies, such as big data analytics, internet of things (IoT), edge computing and artificial intelligence, have nowadays driven traditional manufacturing all the way to intelligent manufacturing. Intelligent manufacturing is characterised by autonomy and self-optimisation, which proposes new demands such as learning and cognitive capacities for manufacturing cell, known as the minimum implementation unit for intelligent manufacturing. Consequently, this paper proposes a general framework for knowledge-driven digital twin manufacturing cell (KDTMC) towards intelligent manufacturing, which could support autonomous manufacturing by an intelligent perceiving, simulating, understanding, predicting, optimising and controlling strategy. Three key enabling technologies including digital twin model, dynamic knowledge bases and knowledge-based intelligent skills for supporting the above strategy are analysed, which equip KDTMC with the capacities of self-thinking, self-decision-making, self-execution and self-improving. The implementing methods of KDTMC are also introduced by a thus constructed test bed. Three application examples about intelligent process planning, intelligent production scheduling and production process analysis and dynamic regulation demonstrate the feasibility of KDTMC, which provides a practical insight into the intelligent manufacturing paradigm.

With the development of sensing and communications technology, some new features have emerged in manufacturing processes, such as highly correlated, deeply integrated, dynamically integrated, and a huge volume of data. There is a strong need to deeply excavate information from manufacturing Big Data, especially the energy consumption data, for energy-efficient manufacturing operations management and analysis. However, relevant data reduction and association analysis to support energy-efficient manufacturing are still ineffective and error-prone, especially for discrete manufacturing workshops. In this paper, an energy-aware Cyber Physical System (E-CPS) is proposed for energy Big Data analysis and recessive production anomalies detection. Firstly, E-CPS is introduced to acquire manufacturing Big Data. Then, a Big Data analysis method, including data reduction and data association analysis, is proposed to analyse the manufacturing data in the E-CPS. Considering the complexity and dynamics of manufacturing processes, an energy Big Data-driven recessive production anomalies analysis method is proposed based on deep belief networks. The proposed method in this paper realises the integrated utilisation of production Big Data and energy Big Data in the E-CPS. Further, the efficiency evaluation and recessive anomalies detection methods can be used in existing production information systems.