A digital twin–driven method for online quality control in process industry

Zhu, Xiaoyang; Ji, Yangjian

doi:10.1007/s00170-021-08369-5

Cited by 24 publications

(7 citation statements)

References 53 publications

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“…Although the precise definition of DT varies depending on the context, it is generally recognized as a digital representation (or model) that simulates a physical system and possesses the capability to autonomously update itself based on data acquired from the corresponding physical system (Liu et al, 2021). Owing to its capacity for interaction with the physical realm, DT has been applied to various engineering scenarios (e.g., manufacturing (Xiang et al, 2019), process industry (Zhu and Ji, 2022)) to support collaboration, information access, and decision-making (Altair Engineering 2023). More detailed DT applications can be found in literature reviews (Lim et al, 2020;Su et al, 2023).…”

Section: Literature Reviewmentioning

confidence: 99%

A Systematic Online Update Method for Reduced-Order-Model-Based Digital Twin

Tang,

Sajadi,

Dehaghani

et al. 2024

Preprint

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A digital twin (DT) is a model that mirrors a physical system and is continuously updated with real-time data from the physical system. Recent implementations of reduced-order-model-based DT (DT-ROM) have been applied in aerodynamics and structural health monitoring, where partial differential equations (PDEs) are utilized to update reduced bases and coefficients. However, these methods are not directly applicable when the PDEs of the system are unknown. This paper addresses the online update challenge for DT-ROM in scenarios lacking known PDEs of the system. To tackle the challenge, a systematic online update and application method is proposed. During the online update, the projection residual of online data on the reduced bases determines the necessity of updating reduced bases, while the prediction residual of online data obtained by the current DT-ROM is used to decide whether to update the coefficient model. By sequentially evaluating both criteria, the method selectively incorporates essential online data for the online DT model update. During the online application, a criterion defined based on online data is adopted to determine whether the offline DT-ROM or the online one is applied to output final predictions. The capability of the proposed method is tested through three numerical and three engineering problems. Results indicate that the proposed online update method consistently reduces both projection and prediction residuals, thereby progressively enhancing the performance of the online DT-ROM on test data. Meanwhile, the online application method provides a prediction performance better than using offline DT-ROM only. Both demonstrate that the proposed work could be applied to online DT update where the PDEs of the system are unknown.

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Section: Literature Reviewmentioning

confidence: 99%

A Systematic Online Update Method for Reduced-Order-Model-Based Digital Twin

Tang,

Sajadi,

Dehaghani

et al. 2024

Preprint

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show abstract

“…Reisch et al (2022) were able to achieve real-time defect detection during the machining of large metal parts through digital twin and make quantitative assessments to improve the sensitivity of defect detection thereby ensuring machining quality. Also, quality control methods driven by digital twin can be used to accurately predict product quality, evaluate processes and optimise process parameters related to product quality (Zhu & Ji, 2022). In addition, improvements in the production are also manifested in optimisation in the machining and assembly processes.…”

Section: Manufacturing Phasementioning

confidence: 99%

Digital twin for product versus project lifecycles’ development in manufacturing and construction industries

Abanda,

Jian,

Adukpo

et al. 2024

J Intell Manuf

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Digital twin, as an important enabling tool for digital transformation, has received increasing attention from researchers and practitioners since its definition was formalised. Especially in the global context and exacerbated by Covid-19, the applications of the digital twin have offered opportunities for many industries. While the digital twin has already been widely used in many sectors such as manufacturing and the construction industry—one of the key engines of economic development, is still lagging behind many other sectors. This study uses the systematic literature review to assess the applications of digital twin in manufacturing and construction respectively, the benefits it brings, and the impediments to its application. Based on this, a comparison is made of digital twin applications in the manufacturing and construction industries to draw lessons. This study concluded that although the use of digital twin in manufacturing is better than construction overall, it is still not reaching its full potential. Despite many benefits brought by the digital twin to construction during the project lifecycle, the construction sector faces even greater challenges than manufacturing in digital twin adoption. By comparison, this study drew five lessons to drive better adoption of the digital twin. The construction industry needs to accelerate the deployment of relevant hardware, promote the standard unification of digital twin, explore the whole lifecycle application of the digital twin, enhance data protection, and embrace changes. This study was limited in the scope of data collection. Future research could focus on gathering information from specific case studies, to produce more comprehensive perspectives.

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“…Increasingly data-intensive artificial intelligence methods are being used for computational intelligence. However, a number of optimization techniques, such as simulated annealing [64], ant colony optimization [65], Genetic Algorithms [66], have also been used in DT literature, as described in subsequent sections. Kennel-Maushart et al [67] use Newton's Method to optimize the solution of the inverse kinematics problem to enhance teleoperation performance via mixed reality for multi-robot systems.…”

Section: Optimization Techniquesmentioning

confidence: 99%

“…Finite Element Analysis (FEA) is often necessary for multiphysics simulations of physical assets involving structural analysis, fluid flow, or thermal loads. Finite Element Analysis tools, such as ANSYS [68,69], COMSOL [66], and ABAQUS [70], have been frequently used to create DTs for biomedical applications and physical assets for which health/condition monitoring is desired [71,72] FEM-based high-fidelity physics simulations are generally computationally costly. Reduced-Order Models based on FEM Simulations can be a useful tool to deploy DTs at scale for complex systems [72], for which commercially available software such as ANSYS Twin Builder (https://www.ansys.com/products/digital-twin/ ansys-twin-builder accessed on 14 December 2022) are becoming increasingly popular [73].…”

Section: Numerical Analysis Toolsmentioning

confidence: 99%

“…Using AR and DT in CNC machining operations for visualization and communication (voice/gesture commands) [66] COMSOL Multiphysics DT of an Optical Fiber Drawing Process is used for real-time monitoring and evaluation (using DL algorithms) of process parameters and quality control [129] Siemens Tecnomatix Process planning and configuration for manufacturing of an impeller using industrial robots, machine tools, and an AGV in a with a DT based Cyber-Physical Production System (CPPS) [130] VINCENT Unity…”

Section: Ref Year Toolsmentioning

confidence: 99%

See 1 more Smart Citation

Human-Centric Digital Twins in Industry: A Comprehensive Review of Enabling Technologies and Implementation Strategies

Asad

Khan

Khalid

et al. 2023

Sensors

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The last decade saw the emergence of highly autonomous, flexible, re-configurable Cyber-Physical Systems. Research in this domain has been enhanced by the use of high-fidelity simulations, including Digital Twins, which are virtual representations connected to real assets. Digital Twins have been used for process supervision, prediction, or interaction with physical assets. Interaction with Digital Twins is enhanced by Virtual Reality and Augmented Reality, and Industry 5.0-focused research is evolving with the involvement of the human aspect in Digital Twins. This paper aims to review recent research on Human-Centric Digital Twins (HCDTs) and their enabling technologies. A systematic literature review is performed using the VOSviewer keyword mapping technique. Current technologies such as motion sensors, biological sensors, computational intelligence, simulation, and visualization tools are studied for the development of HCDTs in promising application areas. Domain-specific frameworks and guidelines are formed for different HCDT applications that highlight the workflow and desired outcomes, such as the training of AI models, the optimization of ergonomics, the security policy, task allocation, etc. A guideline and comparative analysis for the effective development of HCDTs are created based on the criteria of Machine Learning requirements, sensors, interfaces, and Human Digital Twin inputs.

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A digital twin–driven method for online quality control in process industry

Cited by 24 publications

References 53 publications

A Systematic Online Update Method for Reduced-Order-Model-Based Digital Twin

A Systematic Online Update Method for Reduced-Order-Model-Based Digital Twin

Digital twin for product versus project lifecycles’ development in manufacturing and construction industries

Human-Centric Digital Twins in Industry: A Comprehensive Review of Enabling Technologies and Implementation Strategies

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