Architecture and use cases of digital twins towards smart manufacturing

2021 ◽  
Vol 8 (3) ◽  
pp. 264
Author(s):  
Nibhrita Tiwari ◽  
Maninder Jeet Kaur ◽  
Ved Prakash Mishra
Keyword(s):  
Use Cases ◽  
Smart Manufacturing ◽  
Digital Twins

Architecture and use cases of digital twins towards smart manufacturing

2021 ◽  
Vol 8 (3) ◽  
pp. 264
Author(s):  
Ved Prakash Mishra ◽  
Nibhrita Tiwari ◽  
Maninder Jeet Kaur
Keyword(s):  
Use Cases ◽  
Smart Manufacturing ◽  
Digital Twins

scholarly journals Digital Twins Collaboration for Automatic Erratic Operational Data Detection in Industry 4.0

2021 ◽  
Vol 11 (7) ◽  
pp. 3186
Author(s):  
Radhya Sahal ◽  
Saeed H. Alsamhi ◽  
John G. Breslin ◽  
Kenneth N. Brown ◽  
Muhammad Intizar Ali
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Interaction Mechanism ◽  
Semantic Knowledge ◽  
Smart Manufacturing ◽  
Data Detection ◽  
Workflow Model ◽  
Digital Twins ◽  
Potential Failure ◽  
Operational Data

Digital twin (DT) plays a pivotal role in the vision of Industry 4.0. The idea is that the real product and its virtual counterpart are twins that travel a parallel journey from design and development to production and service life. The intelligence that comes from DTs’ operational data supports the interactions between the DTs to pave the way for the cyber-physical integration of smart manufacturing. This paper presents a conceptual framework for digital twins collaboration to provide an auto-detection of erratic operational data by utilizing operational data intelligence in the manufacturing systems. The proposed framework provide an interaction mechanism to understand the DT status, interact with other DTs, learn from each other DTs, and share common semantic knowledge. In addition, it can detect the anomalies and understand the overall picture and conditions of the operational environments. Furthermore, the proposed framework is described in the workflow model, which breaks down into four phases: information extraction, change detection, synchronization, and notification. A use case of Energy 4.0 fault diagnosis for wind turbines is described to present the use of the proposed framework and DTs collaboration to identify and diagnose the potential failure, e.g., malfunctioning nodes within the energy industry.

Digital Twin for Smart Manufacturing: The Practitioner’s Perspective

2020 ◽  
Author(s):  
Maja Bärring ◽  
Björn Johansson ◽  
Goudong Shao
Keyword(s):  
Conceptual Development ◽  
Production Systems ◽  
Manufacturing Sector ◽  
Smart Manufacturing ◽  
Digital Twin ◽  
Adoption And Implementation ◽  
New Information ◽  
Digital Twins ◽  
Discrete Manufacturing ◽  
Design Production

Abstract The manufacturing sector is experiencing a technological paradigm shift, where new information technology (IT) concepts can help digitize product design, production systems, and manufacturing processes. One of such concepts is Digital Twin and researchers have made some advancement on both its conceptual development and technological implementations. However, in practice, there are many different definitions of the digital-twin concept. These different definitions have created a lot of confusion for practitioners, especially small- and medium-sized enterprises (SMEs). Therefore, the adoption and implementation of the digital-twin concept in manufacturing have been difficult and slow. In this paper, we report our findings from a survey of companies (both large and small) regarding their understanding and acceptance of the digital-twin concept. Five supply-chain companies from discrete manufacturing and one trade organization representing suppliers in the automotive business were interviewed. Their operations have been studied to understand their current digital maturity levels and articulate their needs for digital solutions to stay competitive. This paper presents the results of the research including the viewpoints of these companies in terms of opportunities and challenges for implementing digital twins.

Exploring applicability, interoperability and integrability of Blockchain-based digital twins for asset life cycle management

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Christopher Santi Götz ◽  
Patrik Karlsson ◽  
Ibrahim Yitmen
Keyword(s):  
Life Cycle ◽  
Management Practice ◽  
Life Cycle Management ◽  
Use Cases ◽  
Content Type ◽  
Digital Twin ◽  
Digital Twins ◽  
Current Asset ◽  
User Expectations ◽  
Technological Platform

PurposeThe blockchain-based digital twin has been recognized as a prominent technological ecosystem featuring synergies with both established and emergent information management practice. The purpose of this research is to explore the applicability, interoperability and integrability of a blockchain-based digital twin for asset life cycle management and develop a model of framework which positions the digital twin within a broader context of current management practice and technological availability.Design/methodology/approachA systematic literature review was performed to map use cases of digital twin, IoT, blockchain and smart contract technologies. Surveys of industry professionals and analyses were conducted focussing on the mapped use cases' life cycle–centric applicability, interoperability and integrability with current asset life cycle management practice, exploring decision support capabilities and industry insights. Lastly, a model of framework was developed based on the use case, interoperability and integrability findings.FindingsThe results support approaching digitization initiatives with blockchain-based digital twins and the positioning of the concept as both a strategic tool and a multifunctional on-field support application. Integrability enablers include progression towards BIM level 3, decentralized program hubs, modular cross-technological platform interfaces, as well as mergeable and scalable blockchains.Practical implicationsKnowledge of use cases help highlight the functionality of an integrated technological ecosystem and its connection to comprehensive sets of asset life cycle management aspects. Exploring integrability enablers contribute to the development of management practice and solution development as user expectations and technological prerequisites are interlinked.Originality/valueThe research explores asset life cycle management use cases, interoperability and integrability enablers of blockchain-based digital twins and positions the technological ecosystem within current practice and technological availability.

scholarly journals Modeling of Cloud-Based Digital Twins for Smart Manufacturing with MT Connect

2018 ◽  
Vol 26 ◽  
pp. 1193-1203 ◽  
Author(s):  
Liwen Hu ◽  
Ngoc-Tu Nguyen ◽  
Wenjin Tao ◽  
Ming C. Leu ◽  
Xiaoqing Frank Liu ◽  
...  
Keyword(s):  
Smart Manufacturing ◽  
Digital Twins

scholarly journals Digital Twins and Cyber–Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison

Engineering ◽  
2019 ◽  
Vol 5 (4) ◽  
pp. 653-661 ◽  
Author(s):  
Fei Tao ◽  
Qinglin Qi ◽  
Lihui Wang ◽  
A.Y.C. Nee
Keyword(s):  
Industry 4.0 ◽  
Cyber Physical Systems ◽  
Smart Manufacturing ◽  
Physical Systems ◽  
Digital Twins

A Deep Lifelong Learning Method for Digital-Twin Driven Defect Recognition With Novel Classes

2021 ◽  
Vol 21 (3) ◽  
Author(s):  
Gao Yiping ◽  
Li Xinyu ◽  
Liang Gao
Keyword(s):  
Lifelong Learning ◽  
Real Time ◽  
Learning Strategy ◽  
Development Trend ◽  
Smart Manufacturing ◽  
Learning Method ◽  
Time Data ◽  
Time Efficiency ◽  
Digital Twins ◽  
Defect Recognition

Abstract Recently, digital twins (DTs) have become a research hotspot in smart manufacturing, and using DTs to assist defect recognition has also become a development trend. Real-time data collection is one of the advantages of DTs, and it can help the realization of real-time defect recognition. However, DT-driven defect recognition cannot be realized unless some bottlenecks of the recognition models, such as the time efficiency, have been solved. To improve the time efficiency, novel defect class recognition is an essential problem. Most of the existing methods can only recognize the known defect classes, which are available during training. For new incoming classes, known as novel classes, these models must be rebuilt, which is time-consuming and costly. This greatly impedes the realization of DT-driven defect recognition. To overcome this problem, this paper proposes a deep lifelong learning method for novel class recognition. The proposed method uses a two-level deep learning architecture to detect and recognize novel classes, and uses a lifelong learning strategy, weight imprinting, to upgrade the model. With these improvements, the proposed method can handle novel classes timely. The experimental results indicate that the proposed method achieves good results for the novel classes, and it has almost no delay for production. Compared with the rebuilt methods, the time cost is reduced by at least 200 times. This result suggests that the proposed method has good potential in the realization of DT-driven defect recognition.

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