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.

Due to the complexity and dynamics of manufacturing processes, there are various production anomalies in a discrete manufacturing workshop, which have a strong impact on manufacturing quality and productivity. Meanwhile, with the rapid development of Internet of Things technology and communication technology, data store and timely response become new challenges for production anomalies detection. Thus, an edge computing enabled production anomalies detection and energy-efficient production decision approach is proposed in this study. Firstly, an architecture of edge computing enabled production anomalies detection and energy-efficient rescheduling approach is introduced. Then, considering that the raw energy data are always large, isolated and messy, an energy consumption data preprocessing algorithm is established, and production anomaly analysis model is constructed based on long short-term memory network. When an anomaly occurs, an energy-efficient production decision making will be triggered. Finally, through a case analysis of a milling manufacturing system, the results show that the anomaly detection error of the proposed method is only 3.5%. This method realizes the combination of energy consumption data and manufacturing system anomalies detection, and can further assist production process monitoring and energy conservation.

Due to the huge consumption of materials and energy during machining processes, reduction of manufacturing carbon emission is an essential key to decrease the environmental burden of various manufacturing systems. To achieve this target, one critical step is to calculate and evaluate the carbon emissions of machining processes. However, this step is a little difficult for discrete manufacturing processes, because they are always complex and the data sources are diverse. Considering the complexity of discrete manufacturing workshops, a Big Data analysis approach for real-time carbon efficiency evaluation of discrete manufacturing workshops is proposed in an internet of things-enabled ubiquitous environment. Firstly, the deployment of data acquisition devices is introduced to create a ubiquitous manufacturing workshop, and data modeling of production state and carbon emission is described to realize data acquisition and storage. Then, a data-driven multi-level carbon efficiency evaluation of manufacturing workshop is established based on Big Data analysis approaches. Finally, an auto parts manufacturing workshop is studied to verify the feasibility and applicability of the proposed methods. This method realizes the combination of manufacturing Big Data and low-carbon production. Meanwhile, the evaluation method can be used in other production information systems and then assist the production decision-making.

Under the consideration of massive energy consumption of machine tools, many approaches have been proposed, and state control method of machine tools has proved its effectiveness. In order to satisfy the demand of real-time production control, a deep learning methodology for energy-efficient control of CNC machine tools is proposed in RFID-enabled ubiquitous environment. First, the energy-efficient control strategies for multiple machine tools are proposed to reduce the carbon emission of the machining process. Then, through evaluating the process progress in the RFID-enabled environment, a deep learning methodology for energy-efficient strategies selection of CNC machine tools using deep belief networks (DBNs) is established to realize the real-time and accurate control of machine tools. Finally, comparisons between the proposed approach and some state-of-the-art ones are given, and the experiment results indicate that the proposed method is effective and efficient for the energy-efficient control problem of machine tools. The proposed method can realize the real-time control of CNC machine tools based on the interaction information in Industrial 4.0. Furthermore, the machine tools will be converted to smart machines, which can complete self-perception and self-adjustment automatically.

As an important part of industrialized society, manufacturing consumes a large amount of raw materials and energy, which motivates decision-makers to tackle this problem in different manners. Process planning is an important optimization method to realize the object, and energy consumption, carbon emission, or sustainability evaluation is the basis for the optimization stage. Although the evaluation research has drawn a great deal of attention, most of it neglects the influence of state control of machine tools on the energy consumption of machining processes. To address the above issue, a sustainability evaluation method of process planning for single computer numerical control (CNC) machine tool considering energy-efficient control strategies has been developed. First, four energy-efficient control strategies of CNC machine tools are constructed to reduce their energy consumption. Second, a bi-level energy-efficient decision-making mechanism using random forests is established to select appropriate control strategies for different occasions. Then, three indicators are adopted to evaluate the sustainability of process planning under the consideration of energy-efficient control strategies, i.e., energy consumption, relative delay time, and machining costs. Finally, a pedestal part machined by a 3-axis vertical milling machine tool is used to verify the proposed methods. The results show that the reduction in energy consumption considering energy-efficient control strategies reaches 25%.