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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. INDEX TERMS Big data analysis, data acquisition network, carbon emission, carbon efficiency evaluation, discrete manufacturing workshops.

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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%.

The interconnection among heterogeneous sensors and data acquisition equipment in cyber-physical systems have profound significance in achieving adaptability, flexibility, and transparency. Various middlewares have been developed in cyber-physical systems to collect, aggregate, correlate, and translate system monitoring data. Existing middleware solutions are normally highly customized, which face several challenges due to the highly dynamic and harsh production environments. The data generated by sensors can only be shared by specific applications, which prevents the reusability of sensors. Moreover, the lack of uniform access to sensors causes high cost and low efficiency in application development. To address these issues, a resource-oriented middleware architecture called ROMiddleware was proposed, and three key enabling technologies including heterogeneous sensor modeling and grouping, open application programming interfaces development, and token-based access right control mechanism have been developed. Under the guidance of the key enabling technologies, a prototype of ROMiddleware was implemented and its performance was evaluated. Finally, two applications were developed to stress the significance of ROMiddleware. The results show that ROMiddleware can meet the requirements of data acquisition in cyber-physical systems.

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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. INDEX TERMS Edge computing; production anomalies detection; energy-efficient; long short-term memory network; discrete manufacturing workshops.