A Reconfigurable Modeling Approach for Digital Twin-based Manufacturing System

Zhang, Chenyuan; Xu, Wenjun; Liu, Jiayi; Liu, Zhihao; Zhou, Zude; Pham, Duc Truong

doi:10.1016/j.procir.2019.03.141

Cited by 77 publications

(38 citation statements)

References 11 publications

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“…In the manufacturing fields, DTs [14]- [17] are used not only for allowing damage forecast and self-healing mechanisms through virtualization of manufacturing machines, but also for optimizing manufacturing processes or even factories [18]- [22].…”

Section: Research Background a Literature Review 1) Digital Twinmentioning

confidence: 99%

Human Digital Twin for Fitness Management

et al. 2020

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Our research work describes a team of human Digital Twins (DTs), each tracking fitness-related measurements describing an athlete's behavior in consecutive days (e.g. food income, activity, sleep). After collecting enough measurements, the DT firstly predicts the physical twin performance during training and, in case of non-optimal result, it suggests modifications in the athlete's behavior. The athlete's team is integrated into SmartFit, a software framework for supporting trainers and coaches in monitoring and manage athletes' fitness activity and results. Through IoT sensors embedded in wearable devices and applications for manual logging (e.g. mood, food income), SmartFit continuously captures measurements, initially treated as the dynamic data describing the current physical twins' status. Dynamic data allows adapting each DT's status and triggering the DT's predictions and suggestions. The analyzed measurements are stored as the historical data, further processed by the DT to update (increase) its knowledge and ability to provide reliable predictions. Results show that, thanks to the team of DTs, SmartFit computes trustable predictions of the physical twins' conditions and produces understandable suggestions which can be used by trainers to trigger optimization actions in the athletes' behavior. Though applied in the sport context, SmartFit can be easily adapted to other monitoring tasks. INDEX TERMS Counterfactual explanations, digital twins, Internet of Things, machine learning, smart health, sociotechnical design, wearables.

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Section: Research Background a Literature Review 1) Digital Twinmentioning

confidence: 99%

Human Digital Twin for Fitness Management

et al. 2020

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“…e concept of the digital twin was first proposed in the Product Lifecycle Management (PLM) course taught by American scholar Grieves in 2003, and the digital twin was defined as a combination of a set of virtual models and can depict potential or real physical manufacturing products completely from micro to macro. It already includes all the elements of digital twinning: real space, virtual space, and information interaction between real space and virtual space data streams [6].…”

Section: Digital Twin Technology Concept and Application In Cpsmentioning

confidence: 99%

Job Shop Scheduling Based on Digital Twin Technology: A Survey and an Intelligent Platform

Han

Yang

et al. 2021

Complexity

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The concept of digital twinning has become a hot topic in the manufacturing industry in recent years. The emerging digital twin technology is an intelligent technology that makes full use of multimodels, big data, and interdisciplinary knowledge, which provides some new approaches for the field of the intelligent manufacturing industry. The job shop scheduling problem has been an important research field in the discrete manufacturing industry. Digital twin technology is adopted to solve the problem of job shop scheduling, which provides the possibility for the intelligent development of workshops. Based on digital twin technology and combined with the actual problem of production line scheduling, we propose a new intelligent scheduling platform to solve the shop scheduling problems above. Meanwhile, based on the prediction and diagnosis of multisource dynamic interference in the workshop production process by big data analysis technology, the corresponding interference strategy is formulated in advance by the scheduling cloud platform. The model simulation experiment of intelligent dispatching cloud platform was carried out, and some enterprises in intelligent manufacturing workshop were taken as examples to verify the superiority of the dispatching cloud platform. Finally, we look forward to the future research direction of intelligent manufacturing based on digital twin technology.

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“…Besides of the optimization algorithm, discrete event system simulation has been also paid more and more attention for much research. Zhang et al (2019b) proposed a framework of simulation-based approach, and it integrated mathematical algorithms and heuristic methods when applying simulation to balance the operation performance and the planning cost. MOON et al (2006) explain a case study of a body shop design using 3D simulation in a Korean automotive factory.…”

Section: Related Workmentioning

confidence: 99%

“…With the integration and application of new generation information technologies and digital manufacturing, the digital twin (DT) enables digital models to truly reflect physical entities (Leng et al , 2019). Using a digital model can evaluate performance of multiple designs earlier, reduce reliance on expensive and multiple physical testing, optimize for maximum performance and reduce design cycle (Zhang et al , 2019a). Later inherited of digital model could establish real-time synchronization with physical AAL in execution phase and thus enable real-time monitor and dynamic optimization (Zhang et al , 2020).…”

Section: Introductionmentioning

confidence: 99%

Knowledge-enabled digital twin for smart designing of aircraft assembly line

Chang

Jia

et al. 2021

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Purpose The purpose of this paper is to provide a knowledge-enabled digital twin for smart design (KDT-SD) of aircraft assembly line (AAL) to enhance the AAL efficiency, performance and visibility. Modern AALs usually need to have capabilities such as digital-physical interaction and self-evaluation that brings significant challenges to traditional design method for AAL. The digital twin (DT) combining with reusable knowledge, as the key technologies in this framework, is introduced to promote the design process by configuring, understanding and evaluating design scheme. Design/methodology/approach The proposed KDT-SD framework is designed with the introduction of DT and knowledge. First, dynamic design knowledge library (DDK-Lib) is established which could support the various activities of DT in the entire design process. Then, the knowledge-driven digital AAL modeling method is proposed. At last, knowledge-based smart evaluation is used to understand and identify the design flaws, which could further improvement of the design scheme. Findings By means of the KDT-SD framework proposed, it is possible to apply DT to reduce the complexity and discover design flaws in AAL design. Moreover, the knowledge equips DT with the capacities of rapid modeling and smart evaluation that improve design efficiency and quality. Originality/value The proposed KDT-SD framework can provide efficient design of AAL and evaluate the design performance in advance so that the feasibility of design scheme can be improved as much as possible.

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A Reconfigurable Modeling Approach for Digital Twin-based Manufacturing System

Cited by 77 publications

References 11 publications

Human Digital Twin for Fitness Management

Human Digital Twin for Fitness Management

Job Shop Scheduling Based on Digital Twin Technology: A Survey and an Intelligent Platform

Knowledge-enabled digital twin for smart designing of aircraft assembly line

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