Digital Twin Data Modeling with AutomationML and a Communication Methodology for Data Exchange

To solve the problems of product data exchange and sharing between CAD, CAPP, CAM and CNC systems, a CAD/CAPP/CAM/CNC integrated system model is established according to STEP-NC standards. STEP-NC files are used to represent product data in form of neutral file, by which data exchange and sharing can be realized in the integrated system. Furthermore, the key integration technologies including integrated system data modeling, feature conversion are discussed in this paper.

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Unified Systems on the Public Good Data and the Geospatial Data in Korea - Digital Twin Data Strategy for the National Infrastructure -

Journal of the Korean Cadastre Information Association ◽

10.46416/jkcia.2020.12.22.3.98 ◽

2020 ◽

Vol 22 (3) ◽

pp. 98-116

Author(s):

Young Jin Lee

Keyword(s):

Public Good ◽

Geospatial Data ◽

Twin Data ◽

The Public ◽

Good Data ◽

Digital Twin ◽

National Infrastructure

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Model-Based Feature Information Network (MFIN): A Digital Twin Framework to Integrate Location-Specific Material Behavior Within Component Design, Manufacturing, and Performance Analysis

Integrating Materials and Manufacturing Innovation ◽

10.1007/s40192-020-00190-4 ◽

2020 ◽

Vol 9 (4) ◽

pp. 394-409

Author(s):

Saikiran Gopalakrishnan ◽

Nathan W. Hartman ◽

Michael D. Sangid

Keyword(s):

Information Exchange ◽

Computer Aided Design ◽

Data Exchange ◽

Relevant Information ◽

Material Behavior ◽

Information Network ◽

Digital Twin ◽

Model Based ◽

Feature Information ◽

Damage Tolerant

AbstractThe digital transformation of manufacturing requires digitalization, including automatic and efficient data exchange. Model-based definitions (MBDs) capture digital product definitions, in order to eliminate error-prone information exchange associated with traditional paper-based drawings and to provide contextual information through additional metadata. The flow of MBDs extends throughout the product lifecycle (including the design, analysis, manufacturing, in service life, and retirement stages) and can be extended beyond the typical geometry and tolerance information within a computer-aided design. In this paper, the MBDs are extended to include materials information, via dynamic linkages. To this end, a model-based feature information network (MFIN) is created to provide a comprehensive framework that facilitates storing, updating, searching, and retrieving of relevant information across a product’s lifecycle. The use case of a damage tolerant analysis for a compressor bladed-disk (blisk) is demonstrated, in Ti-6Al-4V blade(s) linear friction welded to the Ti-6Al-4V disk, creating well-defined regions exhibiting grain refinement and high residuals stresses. By capturing the location-specific microstructure and residual stress values at the weld regions, this information is accessed within the MFIN and used for downstream damage tolerant analysis. The introduction of the MFIN framework facilitates access to dynamically evolving data for use within physics-based models (resulting in the opportunity to reduce uncertainty in subsequent prognosis analyses), thereby enabling a digital twin description of the component or system.

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Development of Digital Twin for Load Center on the Example of Distribution Network of an Urban District

E3S Web of Conferences ◽

10.1051/e3sconf/202020902029 ◽

2020 ◽

Vol 209 ◽

pp. 02029

Author(s):

Nikita Tomin ◽

Victor Kurbatsky ◽

Vadim Borisov ◽

Sergey Musalev

Keyword(s):

Data Exchange ◽

Physical Object ◽

Electrical Network ◽

Urban District ◽

Automatic Data ◽

Electrical Grid ◽

Digital Twin ◽

Node Network ◽

Optimization And Control ◽

And Control

The paper proposes a concept of building a digital twin based on the reinforcement learning method. This concept allows implementing an accurate digital model of an electrical network with bidirectional automatic data exchange, used for modeling, optimization, and control. The core of such a model is an agent (potential digital twin). The agent, while constantly interacting with a physical object (electrical grid), searches for an optimal strategy for active network management, which involves short-term strategies capable of controlling the power supplied by generators and/ or consumed by the load to avoid overload or voltage problems. Such an agent can verify its training with the initial default policy, which can be considered as a teacher’s advice. The effectiveness of this approach is demonstrated on a test 77-node scheme and a real 17-node network diagram of the Akademgorodok microdistrict (Irkutsk) according to the data from smart electricity meters.

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Multidimensional Data Modeling and Model Validation for Digital Twin Workshop

Journal of Computing and Information Science in Engineering ◽

10.1115/1.4049634 ◽

2021 ◽

Vol 21 (3) ◽

Author(s):

Weiwei Qian ◽

Yu Guo ◽

Kai Cui ◽

Pengxing Wu ◽

Weiguang Fang ◽

...

Keyword(s):

Model Validation ◽

Production Control ◽

Data Modeling ◽

Intelligent Manufacturing ◽

Multidimensional Data ◽

Actual Behavior ◽

Digital Twin ◽

Twin Model ◽

Tensor Models ◽

Control And Management

Abstract Digital twin workshop (DTW) is an important embodiment of intelligent manufacturing in the workshop level, which enables the smart production control and management of the workshop. However, there still exist problems including data modeling and verification of digital model in the process of DTW construction. To solve these problem, multidimensional data modeling and model validation methods of DTW are proposed in this article. First, five-order tensor models for representing manufacturing elements are established to unify the data from physical workshop (PW) and virtual workshop (VW). Then, the mathematical method for verifying DTW twin model is proposed from the recessive and explicit perspective. Finally, a case study of an aerospace machining workshop is carried out to verify the operability and effectiveness of the proposed method. The case analysis shows that the proposed methods can effectively evaluate whether the twin model accurately provides the description of the actual behavior process of physical workshop, and the proposed methods have good performance.

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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.

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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.

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Differentiating Digital Twin from Digital Shadow: Elucidating a Paradigm Shift to Expedite a Smart, Sustainable Built Environment

Buildings ◽

10.3390/buildings11040151 ◽

2021 ◽

Vol 11 (4) ◽

pp. 151

Author(s):

Samad M. E. Sepasgozar

Keyword(s):

Energy Consumption ◽

Real Time ◽

Industry 4.0 ◽

Construction Projects ◽

Data Exchange ◽

Smart Cities ◽

Information Modeling ◽

Digital Data ◽

Digital Twin ◽

Industrial Sectors

Construction projects and cities account for over 50% of carbon emissions and energy consumption. Industry 4.0 and digital transformation may increase productivity and reduce energy consumption. A digital twin (DT) is a key enabler in implementing Industry 4.0 in the areas of construction and smart cities. It is an emerging technology that connects different objects by utilising the advanced Internet of Things (IoT). As a technology, it is in high demand in various industries, and its literature is growing exponentially. Previous digital modeling practices, the use of data acquisition tools, human–computer–machine interfaces, programmable cities, and infrastructure, as well as Building Information Modeling (BIM), have provided digital data for construction, monitoring, or controlling physical objects. However, a DT is supposed to offer much more than digital representation. Characteristics such as bi-directional data exchange and real-time self-management (e.g., self-awareness or self-optimisation) distinguish a DT from other information modeling systems. The need to develop and implement DT is rising because it could be a core technology in many industrial sectors post-COVID-19. This paper aims to clarify the DT concept and differentiate it from other advanced 3D modeling technologies, digital shadows, and information systems. It also intends to review the state of play in DT development and offer research directions for future investigation. It recommends the development of DT applications that offer rapid and accurate data analysis platforms for real-time decisions, self-operation, and remote supervision requirements post-COVID-19. The discussion in this paper mainly focuses on the Smart City, Engineering and Construction (SCEC) sectors.

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Improving Building Energy Footprint and Asset Performance Using Digital Twin Technology

Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction ◽

10.1680/jsmic.21.00001 ◽

2021 ◽

pp. 1-8

Author(s):

Mohsen A Jafari ◽

Ali Ghofrani ◽

Esmat Zaidan ◽

Ammar Abulibdeh

Keyword(s):

Asset Management ◽

Data Exchange ◽

Building Performance ◽

Digital Twin ◽

Business Objective ◽

Control Schemes ◽

Operations And Maintenance ◽

Value Model ◽

Fully Connected ◽

New Buildings

This article presents a novel architecture by integrating the existing asset management theory with building simulation technology for effective maintenance strategies and operational control schemes. Building performance, value and energy usage collectively define the criteria for optimization. Building assets are partially or fully connected with building Internet of Things (IoT) and their real time conditions are accessible at all times. An asset’s value is derived from the functional contributions of that asset to the overall business objective of the system that it is part of. The architecture consists of digital twin, analytics and Business Value Model (BVM) engines and in-between gateways for data exchange. The paper provides illustrative examples for how the platform can serve operations and maintenance (O&M) objectives of existing and new buildings.

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