RFID-Driven Energy-Efficient Control Approach of CNC Machine Tools Using Deep Belief Networks

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. Note to Practitioners—It is significant but challenging work to realize the control of manufacturing processes based on real-time production data. Thus, this paper integrates RFID data of jobs with energy-efficient control of CNC machine tools, and proposes a deep learning methodology of processing the real-time production data in an RFID-enabled ubiquitous environment. Considering the relationship between different jobs, five energy-efficient control strategies for multiple machine tools are put forward to reduce the carbon emission of the machining processes. Then, an RFID-driven process progress evaluation is carried out to quantify the real-time progress, and two RFID data preprocessing algorithms are developed to cleanse and extract the original data. DBNs are adopted to realize the energy-efficient strategies selection of CNC machine tools. The experiments from an actual printing machine manufacturing enterprise indicate that the proposed method is effective and efficient for the energy-efficient control problem of machine tools. The implementation requires RFID-enabled manufacturing environment deployment, RFID data capturing and prepressing, and deep learning model establishment based on historical production data. Beyond the energy conservation of machine tools, the proposed method can also be applied to other industrial problems, e.g., self-perception and self-adjustment of smart machine tools, production rescheduling decisions, and logistics routing optimization.