Model-based data integration along the product &amp; service life cycle supported by digital twinning

Woitsch, Robert; Sumereder, Anna; Falcioni, Damiano

doi:10.1016/j.compind.2022.103648

Cited by 24 publications

(10 citation statements)

References 30 publications

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“…Yang et al [ 20 ] developed a model-based DT of an aero-engine disk for online detection of disk unbalance and crack failure using an ANSYS simulation platform. Woitsch et al [ 21 ] proposed a meta-model of a model-based DT environment to bridge the between the manufacturing and the use of products and services based on an ADOxx meta-modeling platform.…”

Section: Dt Architecture Categorizationsmentioning

confidence: 99%

Overview of Digital Twin Platforms for EV Applications

Ibrahim

Rjabtšikov

Zequera

2023

Sensors

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Digital twin (DT) technology has been used in a wide range of applications, including electric vehicles. The DT platform provides a virtual representation or advanced simulation of a physical object in real-time. The implementation of DT on various aspects of EVs has recently transpired in different research studies. Generally, DT can emulate the actual vehicle on the road to predict/optimize its performance and improve vehicle safety. Additionally, DT can be used for the optimization of manufacturing processes, real-time condition monitoring (at all levels and in all powertrain components), energy management optimization, repurposing of the components, and even recycling processes. This paper presents an overview of different DT platforms that can be used in EV applications. A deductive comparison between model-based and data-driven DT was performed. EV main systems have been discussed regarding the usable DT platform. DT platforms used in the EV industry were addressed. Finally, the review showed the superiority of data-driven DTs over model-based DTs due to their ability to handle systems with high complexity.

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Section: Dt Architecture Categorizationsmentioning

confidence: 99%

Overview of Digital Twin Platforms for EV Applications

Ibrahim

Rjabtšikov

Zequera

2023

Sensors

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“…The application of DT started in the field of aeronautics and has expanded to other areas, such as airframes [61,62], industry [63,64], product service systems [65,66], systems engineering [67], health care [68,69], structural health monitoring [70], product design [71], product life cycle management [72], construction [73], urban planning and transportation [74,75], disaster management [76], infrastructure management [77], agriculture [72,78], and more. While most use cases of DT are in the field of manufacturing [79], the development of DT in the construction industry was not achieved until 2018 [73,80].…”

Section: Digital Twin Definitionmentioning

confidence: 99%

Digital Twin for Fault Detection and Diagnosis of Building Operations: A Systematic Review

Hodavand,

Ramaji,

Sadeghi

2023

Buildings

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Intelligence in Industry 4.0 has led to the development of smart buildings with various control systems for data collection, efficient optimization, and fault detection and diagnosis (FDD). However, buildings, especially with regard to heating, ventilation, and air conditioning (HVAC) systems, are responsible for significant global energy consumption. Digital Twin (DT) technology offers a sustainable solution for facility management. This study comprehensively reviews DT performance evaluation in building life cycle and predictive maintenance. 200 relevant papers were selected using a systematic methodology from Scopus, Web of Science, and Google Scholar, and various FDD methods were reviewed to identify their advantages and limitations. In conclusion, data-driven methods are gaining popularity due to their ability to handle large amounts of data and improve accuracy, flexibility, and adaptability. Unsupervised and semi-supervised learning as data-driven methods are important for FDD in building operations, such as with HVAC systems, as they can handle unlabeled data and identify complex patterns and anomalies. Future studies should focus on developing interpretable models to understand how the models made their predictions. Hybrid methods that combine different approaches show promise as reliable methods for further research. Additionally, deep learning methods can analyze large and complex datasets, indicating a promising area for further investigation.

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“…O'Sullivan et al presented case studies that demonstrate the advantages of digital twins in improving manufacturing efficiency, quality, and sustainability [6]. Woitsch et al explored the benefits, challenges, and emerging trends in digital twin modelling and simulation, highlighting its potential for improving design, manufacturing, and maintenance processes [7]. Perno et al (2023) proposed a digital twin-driven framework for the design and manufacturing optimization of complex products.…”

Section: Literature Surveymentioning

confidence: 99%

Digital Twin Modeling and Simulation of Computer Aided Design and Manufacturing Structure: Case Study

2023

dmpm

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This paper explores the application of digital twin technology in the CAD/CAM domain, focusing on its potential to enhance the design and manufacturing structure. The paper begins by introducing the concept of digital twins and their role in bridging the gap between physical and virtual worlds. It emphasizes the benefits of real-time data synchronization and it enables continuous monitoring, analysis, and decision-making throughout the product lifecycle in detail. Next, the focus shifts to the integration of digital twin modelling into the CAD/CAM processes. The paper outlines the steps involved in creating a digital twin of the design and manufacturing structure, from data acquisition and integration to model calibration and validation. Special attention is given to ensuring the accuracy and fidelity of the digital twin to enable reliable simulation results. The paper then explores the various simulation capabilities offered by the digital twin model. It delves into the use of finite element analysis (FEA), computational fluid dynamics (CFD), and other simulation techniques to analyse product performance, optimize manufacturing processes, and assess structural integrity. Case studies demonstrate the application of digital twin simulations in improving design efficiency, reducing time-to-market, and enhancing overall product quality.

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Model-based data integration along the product & service life cycle supported by digital twinning

Cited by 24 publications

References 30 publications

Overview of Digital Twin Platforms for EV Applications

Overview of Digital Twin Platforms for EV Applications

Digital Twin for Fault Detection and Diagnosis of Building Operations: A Systematic Review

Digital Twin Modeling and Simulation of Computer Aided Design and Manufacturing Structure: Case Study

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