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

Zhang, Chaoyang; Jiang, Pingyu

doi:10.1109/tase.2019.2909043

Cited by 21 publications

(9 citation statements)

References 21 publications

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“…In this work, the control operations of proposed PoC can be modelled to address a minimum-time control problem [51], considering the state switching procedure of the Studer S33 (i.e., State 0: OFF ⇐⇒ State 1: ON, as shown in Figure 5) effected by the proposed PoC. Note that a full-fledged system should accommodate more states in the state switching procedure of the Studer S33; for example, the downtime, standby, idle, warm up, and operational states in the typical state switching procedure of a CNC machine tool [52]. However, these states are not within the scope of our work due to the control operations configured on the proposed PoC.…”

Section: Analysis and Performance Evaluationmentioning

confidence: 99%

Ubiquitous Control of a CNC Machine: Proof of Concept for Industrial IoT Applications

et al. 2021

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In this paper, an integrated system to control and manage a state-of-the-art industrial computer numerical control (CNC) machine (Studer S33) using a commercially available tablet (Samsung Galaxy Tablet S2) is presented as a proof of concept (PoC) for the ubiquitous control of industrial machines. As a PoC, the proposed system provides useful insights to support the further development of full-fledged systems for Industrial Internet of Things (IIoT) applications. The proposed system allows for the quasi-decentralisation of the control architecture of conventional programmable logic controller (PLC)-based industrial control systems (ICSs) through data and information exchange over the transmission control protocol and the internet protocol (TCP/IP) suite using multiple agents. Based on the TCP/IP suite, a network device (Samsung Galaxy Tablet S2) and a process field net (PROFINET) device (Siemens Simatic S7-1200) are interfaced using a single-board computer (Raspberry Pi 4). An override system mainly comprising emergency stop and acknowledge buttons is also configured using the single-board computer. The input signals from the override system are transmitted to the PROFINET device (i.e., the industrial control unit (ICU)) over TCP/IP. A fully functional working prototype is realised as a PoC for an integrated system designated for the wireless and ubiquitous control of the CNC machine. The working prototype as an entity mainly comprises a mobile (handheld) touch-sensitive human-machine interface (HMI), a shielded single-board computer, and an override system, all fitted into a compact case with physical dimensions of 300 mm by 180 mm by 175 mm. To avert potential cyber attacks or threats to a reasonable extent and to guarantee the security of the PoC, a multi-factor authentication (MFA) including an administrative password and an IP address is implemented to control the access to the web-based ubiquitous HMI proffered by the PoC.

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Section: Analysis and Performance Evaluationmentioning

confidence: 99%

Ubiquitous Control of a CNC Machine: Proof of Concept for Industrial IoT Applications

et al. 2021

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“…In addition, according to the energy consumption curve of a machining process in Fig. 3, it can be seen that a process mainly contains five states: downtime, standby, idle, air cutting and cutting [26]. Since the difference among energy consumption data of different states is obvious, the energy data of each state needs to be obtained for production anomalies analysis.…”

Section: ) Energy Consumption Data Cleansingmentioning

confidence: 99%

Edge Computing Enabled Production Anomalies Detection and Energy-Efficient Production Decision Approach for Discrete Manufacturing Workshops

Zhang

2020

IEEE Access

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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.

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“…In this study, two key technologies of the proposed framework, namely energy Big Data acquisition and energy Big Data mining, are utilized to implement energy Big Data analytics. A deep learning methodology for energyefficient strategies selection of CNC machine tools using deep belief networks is established to realize the real-time and accurate control of machine tools [31]. Ding et al proposed a manufacturing data processing to realize real-time datadriven operations control of digital twin-based cyber-physical production system, which includes two phases, i.e., local data processing and global data processing [32].…”

Section: B Manufacturing Big Data Analysis and Processingmentioning

confidence: 99%

Big Data Analysis Approach for Real-Time Carbon Efficiency Evaluation of Discrete Manufacturing Workshops

Zhang

2019

IEEE Access

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

Cited by 21 publications

References 21 publications

Ubiquitous Control of a CNC Machine: Proof of Concept for Industrial IoT Applications

Ubiquitous Control of a CNC Machine: Proof of Concept for Industrial IoT Applications

Edge Computing Enabled Production Anomalies Detection and Energy-Efficient Production Decision Approach for Discrete Manufacturing Workshops

Big Data Analysis Approach for Real-Time Carbon Efficiency Evaluation of Discrete Manufacturing Workshops

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