A resource-oriented middleware in a prototype cyber-physical manufacturing system

Liu, Chao; Jiang, Pingyu; Zhang, Chaoyang

doi:10.1177/0954405417716494

Cited by 13 publications

(6 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the deployment of manufacturing processes of a part, the corresponding digital twin can cover data from multiple sources, including enterprise resource planning (ERP) system, CAD platform, manufacturing execution system (MES), inspection planning system, on-site sensors in different scenes, logistics system, assembly planning and so on. Benefit from the extensive research of CyberManufacturing Systems 44 and middleware, 45 the data with certain semantics could perform orderly in forming digital twins. However, with the needs of data for some specific analysis in assembly, the type of data sources can be extended with the type of data being extended.…”

Section: Assembly-oriented Part Digital Twin Frameworkmentioning

confidence: 99%

Ontology-based modeling of part digital twin oriented to assembly

Bao

Zhao

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part B:

View full text Add to dashboard Cite

The digital twin concept, as a widely accepted philosophy for a new generation of digital manufacturing research, plays a significant role in the Industry 4.0 era. As the prerequisite for in-depth application of digital manufacturing in assembly, the collecting, modeling and utilizing approach for historical data of machining process and inspection of part appear to be important to provide data support for integral manufacturing scenarios. However, the modeling approach to part digital twin is not comprehensive due to the structural heterogeneity of data, which hampers the real-time simulation and adjustment in assembly process. Therefore, a method of part digital twin modeling oriented to assembly is proposed in this article. The designing information of a part is obtained from a three-dimensional model with the model-based definition approach, while machining features are pre-defined and identified. Moreover, the assembly constrain relationships in the assembly unit that the part participate in are obtained, on account of which deviation transfer analysis can be accomplished, and key assembly features are filtered to be the carrier of processing and inspection data. An assembly-oriented part digital twin framework is constructed to demonstrate the main components and dataflow in creating a digital twin with information filtering and subsequent management. In addition, a case study is illustrated to show the entire process of part digital twin modeling and proves the practicality and efficiency of the method proposed.

show abstract

Section: Assembly-oriented Part Digital Twin Frameworkmentioning

confidence: 99%

Ontology-based modeling of part digital twin oriented to assembly

Bao

Zhao

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part B:

View full text Add to dashboard Cite

show abstract

“…Besides, Lopez et al 18 researched the categorization of anomalies in machines, controllers, and networks in SMSs to support the selection of detection mechanisms. Liu et al 19 presented a resource-oriented middleware in a prototype cyber-physical manufacturing system to collect, aggregate, correlate, and translate system monitoring data. Song and Moon 20 clarified that new ITs enable intelligent behaviors of manufacturing components and systems such as self-awareness, self-prediction, self-optimization, and self-configuration, and they proposed the architecture and functions of CyberManufacturing System and analyzed the performance.…”

Section: Related Workmentioning

confidence: 99%

Analyzing the cyber-physical system–based autonomous collaborations among smart manufacturing resources in a smart shop floor

Ding

Lei

Zhang

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part B:

View full text Add to dashboard Cite

Industry 4.0 focuses on the realization of smart manufacturing from shop floors to factories and to the whole supply chain. As a key technology of smart manufacturing, cyber-physical system has been widely discussed in the aspects of system design, data collection and processing, and cyber-physical synchronization. In a smart shop floor, manufacturing resources with intelligence and autonomy are abstracted as cyber-physical system units. They can communicate with each other autonomously to make optimal production decisions according to the real-time status of the shop floor. In this article, an autonomous collaboration network comprised of cyber-physical system–based smart manufacturing resources is modeled by using complex network theory. The collaboration activities among them are further analyzed, from which the information of key cyber-physical system units and key collaboration relationships are excavated. A demonstrative case is studied to verify the feasibility of the proposed model. From the case, it can be seen that (1) autonomous collaboration network has a small-world feature; (2) cyber-physical system units with bigger degrees and the collaborative relationships with bigger tightness are more important; (3) the workload of cyber-physical system units needs to be balanced because some cyber-physical system units have exceeded their capacities; and (4) cyber-physical system units with larger collaboration clustering coefficients will attract other nodes to form communities centered by them. Based on these results, the autonomous production control and management of smart shop floor will become more accurate, efficient, and balanced.

show abstract

“…Two such technologies that are addressing these manufacturing challenges are the CPS [3][4][5] and the DT. 6,7 Each have similar attributes such that they seek to connect the physical with the cyber world using a variety of communication methods.…”

Section: Introductionmentioning

confidence: 99%

Real-time vision-based multiple object tracking of a production process: Industrial digital twin case study

Ward

Soulatiantork

Finneran

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part B:

View full text Add to dashboard Cite

The adoption of Industry 4.0 technologies within the manufacturing and process industries is widely accepted to have benefits for production cycles, increase system flexibility and give production managers more options on the production line through reconfigurable systems. A key enabler in Industry 4.0 technology is the rise in Cyber-Physical Systems (CPS) and Digital Twins (DTs). Both technologies connect the physical to the cyber world in order to generate smart manufacturing capabilities. State of the art research accurately describes the frameworks, challenges and advantages surrounding these technologies but fails to deliver on testbeds and case studies that can be used for development and validation. This research demonstrates a novel proof of concept Industry 4.0 production system which lays the foundations for future research in DT technologies, process optimisation and manufacturing data analytics. Using a connected system of commercial off-the-shelf cameras to retrofit a standard programmable logic controlled production process, a digital simulation is updated in real time to create the DT. The system can identify and accurately track the product through the production cycle whilst updating the DT in real-time. The implemented system is a lightweight, low cost, customable and scalable design solution which provides a testbed for practical Industry 4.0 research both for academic and industrial research purposes.

show abstract

A resource-oriented middleware in a prototype cyber-physical manufacturing system

Cited by 13 publications

References 31 publications

Ontology-based modeling of part digital twin oriented to assembly

Ontology-based modeling of part digital twin oriented to assembly

Analyzing the cyber-physical system–based autonomous collaborations among smart manufacturing resources in a smart shop floor

Real-time vision-based multiple object tracking of a production process: Industrial digital twin case study

Contact Info

Product

Resources

About