Automation systems and integration. Industrial data. Visualization elements of digital twins

2020 ◽  
Keyword(s):  
Data Visualization ◽  
Industrial Data ◽  
Digital Twins ◽  
Automation Systems

Digital Tools to Capture Manufacturing Tacit Knowledge to Support Next Generation Automation Systems

2019 ◽  
Author(s):  
David A. Guerra-Zubiaga ◽  
Kathy S. Schwaig ◽  
Sabih Nasir ◽  
Alex Bondar
Keyword(s):  
Tacit Knowledge ◽  
Digital Tools ◽  
Automation System ◽  
Digital Manufacturing ◽  
Next Generation ◽  
Knowledge Modeling ◽  
Digital Twin ◽  
Digital Twins ◽  
Automation Systems ◽  
Manufacturing Knowledge

Abstract In today’s complex environment, it is not only important to handle/control digital manufacturing tools, but also essential to capture tacit knowledge from people. Creating a digital twin, it is an extensive effort including different fields and subjects. For example, creating a physical prototype and connecting it with a virtual prototype. From this, two questions arise. What will be the framework used to create the digital twin and what method will be used to capture the experiences to develop Next Generation Automation System (NGAS). This research explores a new method capturing tacit knowledge creating a digital twin for a NGAS, worked at station level connecting machines and humans implementing knowledge modelling and providing guidance in design for manufacturability at NGAS. The motivation of this research is that capturing tacit knowledge is an important aspect in Industry 4.0. According to literature review, different researchers have been exploring digital twins using digital tools. This research proposal explores the effects of automation in the workplace using Digital Manufacturing Tools (DMT). The proposed approach demonstrates how to capture valuable experiences we can transfer or communicate between the digital twins, increasing productivity to fulfill the need to adopt new and emerging technologies in the workplace. The research will talk about capturing Tacit Knowledge in different forms like experiences, analysis, and intuitions etc and how this type of knowledge is processed by DMT and communicated to the other digital twin. Tacit knowledge modeling and sharing is used by implementing the Internet of Things (IoT) to understand the interaction among humans, instruments, controls, and robots. Understanding tacit manufacturing knowledge types is required to create better digital twins.

scholarly journals Realization of AI-enhanced industrial automation systems using intelligent Digital Twins

Procedia CIRP ◽  
2021 ◽  
Vol 97 ◽  
pp. 396-400
Author(s):  
Nasser Jazdi ◽  
Behrang Ashtari Talkhestani ◽  
Benjamin Maschler ◽  
Michael Weyrich
Keyword(s):  
Industrial Automation ◽  
Digital Twins ◽  
Automation Systems ◽  
Industrial Automation Systems

Recent advances on industrial data-driven energy savings: Digital twins and infrastructures

2021 ◽  
Vol 135 ◽  
pp. 110208 ◽  
Author(s):  
Sin Yong Teng ◽  
Michal Touš ◽  
Wei Dong Leong ◽  
Bing Shen How ◽  
Hon Loong Lam ◽  
...  
Keyword(s):  
Energy Savings ◽  
Data Driven ◽  
Industrial Data ◽  
Recent Advances ◽  
Digital Twins

Edge Computing Enhanced Digital Twins for Smart Manufacturing

2021 ◽  
Author(s):  
Huiyue Huang ◽  
Xun Xu
Keyword(s):  
Real Time ◽  
Information And Communication Technologies ◽  
Research Work ◽  
Edge Computing ◽  
Smart Manufacturing ◽  
Data Mapping ◽  
Digital Twin ◽  
Mapping Strategy ◽  
Digital Twins ◽  
Automation Systems

Abstract Digital Twin is one of the key enabling technologies for smart manufacturing in the context of Industry 4.0. The combination with advanced data analytics and information and communication technologies allows Digital Twins to perform real-time simulation, optimization and prediction to their physical counterparts. Efficient bi-directional data exchange is the foundation for Digital Twin implementation. However, the widely mentioned cloud-based architecture has disadvantages, such as high pressure on bandwidth and long latency time, which limit Digital Twins to provide real-time operating responses in dynamic manufacturing processes. Edge computing has the characteristics of low connectivity, the capability of immediate analysis and access to temporal data for real-time analytics, which makes it a fit-for-purpose technology for Digital Twin development. In this paper, the benefits of edge computing to Digital Twin are first explained through the reviews of the two technologies. The Digital Twin functions to be performed at the edge are then elaborated. After that, how the data model will be used in the edge for data mapping to realize the Digital Twin is illustrated and the data mapping strategy based on the EXPRESS schemas is discussed. Finally, a case study is carried out to verify the data mapping strategy based on EXPRESS schema. This research work refers to ISO/DIS 23247 Automation systems and integration — Digital Twin framework for manufacturing.

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