Using Digital Twin Data for the Attribute-Based Usage Control of Value-Added Networks

Abstract Organizing manufacturing in dynamic networks instead of inflexible production lines is one of the key aspects of Industry 4.0. This should serve to realize automation and effectiveness to a higher degree than previously achievable. For this modernization, Cyber-Physical Systems should be utilized, where a Digital Twin mirrors the behavior of its Physical Twin and makes the data during manufacturing externally available via communication interfaces. This Digital Twin should be an instantiation of a Digital Master, which must meet the requirements for communication in dynamically changing value-added networks. The networking capability of objects requires semantic information. This information is associated with rules for decision making within a value-added network. This paper addresses the need for research on how to add networking capabilities during the development of Digital Masters. With these added capabilities, the communication between Digital Masters and Twins in terms of a single part manufacturing simulation should be verifiable in a Digital Factory. For this purpose, the concept of this paper aims to outline guidelines on how to add networking capabilities to the single part, machines and other resources needed during manufacturing.

Download Full-text

Digital twin data: methods and key technologies

Digital Twin ◽

10.12688/digitaltwin.17467.1 ◽

2021 ◽

Vol 1 ◽

pp. 2

Author(s):

Meng Zhang ◽

Fei Tao ◽

Biqing Huang ◽

Ang Liu ◽

Lihui Wang ◽

...

Keyword(s):

Development Process ◽

Data Gathering ◽

Twin Data ◽

Theoretical Underpinning ◽

Basic Principles ◽

Digital Twin ◽

Related Data ◽

On Demand ◽

Key Enabling Technologies ◽

Traditional Industry

As a promising technology to converge the traditional industry with the digital economy, digital twin (DT) is being investigated by researchers and practitioners across many different fields. The importance of data to DT cannot be overstated. Data plays critical roles in constructing virtual models, building cyber-physical connections, and executing intelligent operations. The unique characteristics of DT put forward a set of new requirements on data. Against this background, this paper discusses the emerging requirements on DT-related data with respect to data gathering, mining, fusion, interaction, iterative optimization, universality, and on-demand usage. A new notion, namely digital twin data (DTD), is introduced. This paper explores some basic principles and methods for DTD gathering, storage, interaction, association, fusion, evolution and servitization, as well as the key enabling technologies. Based on the theoretical underpinning provided in this paper, it is expected that more DT researchers and practitioners can incorporate DTD into their DT development process.

Download Full-text

Quality Prediction and Control of Assembly and Welding Process for Ship Group Product Based on Digital Twin

Scanning ◽

10.1155/2020/3758730 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Lei Li ◽

Di Liu ◽

Jinfeng Liu ◽

Hong-gen Zhou ◽

Jiasheng Zhou

Keyword(s):

Quality Control ◽

Welding Process ◽

Quality Prediction ◽

Twin Data ◽

Time Data ◽

Digital Twin ◽

Twin Model ◽

Prediction And Control ◽

And Control ◽

Group Product

In view of the problems of lagging and poor predictability for ship assembly and welding quality control, the digital twin technology is applied to realize the quality prediction and control of ship group product. Based on the analysis of internal and external quality factors, a digital twin-based quality prediction and control process was proposed. Furthermore, the digital twin model of quality prediction and control was established, including physical assembly and welding entity, virtual assembly and welding model, the quality prediction and control system, and twin data. Next, the real-time data collection based on the Internet of Things and the twin data organization based on XML were used to create a virtual-real mapping mechanism. Then, the machine learning technology is applied to predict the process quality of ship group products. Finally, a small group is taken as an example to verify the proposed method. The results show that the established prediction model can accurately evaluate the welding angular deformation of group products and also provide a new idea for the quality control of shipbuilding.

Download Full-text