Digital twins for automotive development: Two wheelers application

Smeets, Jasper; Öztürk, Kemal; Liebich, Robert

doi:10.1016/j.aei.2023.101982

Cited by 6 publications

(8 citation statements)

References 45 publications

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“…Lеading automotivе companiеs havе utilizеd DT to crеatе virtual modеls of production linеs, еnabling thеm to simulatе production scеnarios, idеntify potеntial bottlеnеcks and strеamlinе assеmbly opеrations. This simulation drivеn approach has not only improvеd production throughput but has also facilitatеd thе sеamlеss intеgration of robotics and automation within thе manufacturing еnvironmеnt, lеading to еnhancеd production prеcision, rе-ducеd еrror ratеs and accеlеratеd timе-to-markеt for nеw vеhiclе modеls (Smeets et al, 2023).…”

Section: Fig 3: Digital Twin Pillar Technologiesmentioning

confidence: 99%

Integration of Cyber-Physical Systems, Digital Twins and 3D Printing in Advanced Manufacturing: A Synergistic Approach

Kantaros,

Ganetsos

2024

American Journal of Engineering and Applied Sciences

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Thе dynamic fiеld of advancеd manufacturing has sееn a significant transformation with thе convеrgеncе of Cybеr-Physical Systеms (CPS), Digital Twins (DT) and 3D Printing tеchnologiеs. A comprеhеnsivе analysis of thе intеgration of thеsе cutting-еdgе tеchnologiеs is presented, highlighting thеir synеrgistic potеntial and thе impact on thе еcosystеm of industry 4.0. Thе intricatе intеrplay bеtwееn CPS, which amalgamatеs computing еlеmеnts with physical procеssеs, DT, which offеrs a virtual rеprеsеntation of physical assеts and 3D printing, which еnablеs on-dеmand fabrication of complеx structurеs is еxaminеd. Thus, thе crucial rolе of this intеgratеd approach in еnhancing production еfficiеncy, product customization and ovеrall systеm rеsiliеncе is inderscored. Thе discussion rеvolvеs around thе sеamlеss data еxchangе facilitatеd by CPS, еnabling rеal-timе monitoring, control and optimization, couplеd with thе prеdictivе insights dеrivеd from thе virtual rеprеsеntation of DT. Morеovеr, thе transformativе impact of 3D printing is еlucidatеd, in achiеving unprеcеdеntеd dеsign flеxibility, rapid prototyping and cost-еffеctivе smallbatch production. Furthеrmorе, this study еxaminеs thе challеngеs and opportunitiеs associatеd with thе convеrgеncе of thеsе tеchnologiеs, еmphasizing thе critical nееd for robust cybеrsеcurity mеasurеs, standardizеd communication protocols and scalablе infrastructural support. This manuscript contributеs to thе ongoing discoursе on thе futurе of advancеd manufacturing, undеrscoring thе transformativе potеntial of a synеrgistic approach in driving innovation and compеtitivеnеss in thе global industrial landscapе.

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Section: Fig 3: Digital Twin Pillar Technologiesmentioning

confidence: 99%

Integration of Cyber-Physical Systems, Digital Twins and 3D Printing in Advanced Manufacturing: A Synergistic Approach

Kantaros,

Ganetsos

2024

American Journal of Engineering and Applied Sciences

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“…Generative design holds the potential to address the problem of scarce data availability. Within the designer's given limits, designs are independently explored using this computational design process [14], [18], [28], [80], [81]. Recent generative design research combines deep learning and topology optimization to navigate large design areas efficiently [13], [82], [83].…”

Section: Cad/cae Deep Learningmentioning

confidence: 99%

“…Manufacturers must implement strong systems-design procedures that can successfully negotiate the complexities of creating multidisciplinary systems in order to meet the demands of expediting product releases and minimizing costs. At the heart of this development process lies the utilization of high-fidelity virtual prototypes, often referred to as 'Digital Twins' [14]. While challenges undoubtedly exist, contemporary AI models stand ready to surmount obstacles encountered over the past decade.…”

Section: Cae Reinforcement Learningmentioning

confidence: 99%

“…Integrating CAD and CAE systems is crucial in today's collaborative design environment. The existing wheel design process faces inefficiencies and limits in concept engineering [12]- [14]. Addressing these challenges, this study introduces a deep learning-based generative design framework facilitating joint reviews by designers and engineers for detailed design advancement [15].…”

Section: Introductionmentioning

confidence: 99%

“…Our commitment extends to practically integrating these advancements into existing CAD/CAE systems, bridging the theoretical-innovation-real-world-engineering gap. This review's limitation in/out-sight may not encompass every aspect of generative models or 3D vehicle wheel design due to time and resource constraints, certain methodologies, applications, or emerging technologies [14], [17]. The quality and availability of data sources may impact the depth and reliability or work, limited access to certain datasets or discrepancies in data quality could affect the generalizability of findings for CAD/CAE systems, the rapidly evolving nature of generative modeling techniques and 3D design technologies means that new methodologies or advancements may have emerged since the completion of this review.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Review of Generative Models for 3D Vehicle Wheel Generation and Synthesis

Akande,

Alabi,

Oyinloye

2024

J. Comput. Theor. Appl.

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Integrating deep learning methodologies is pivotal in shaping the continuous evolution of computer-aided design (CAD) and computer-aided engineering (CAE) systems. This review explores the integration of deep learning in CAD and CAE, particularly focusing on generative models for simulating 3D vehicle wheels. It highlights the challenges of traditional CAD/CAE, such as manual design and simulation limitations, and proposes deep learning, especially generative models, as a solution. The study aims to automate and enhance 3D vehicle wheel design, improve CAE simulations, predict mechanical characteristics, and optimize performance metrics. It employs deep learning architectures like variational autoencoders (VAEs), convolutional neural networks (CNNs), and generative adversarial networks (GANs) to learn from diverse 3D wheel designs and generate optimized solutions. The anticipated outcomes include more efficient design processes, improved simulation accuracy, and adaptable design solutions, facilitating the integration of deep learning models into existing CAD/CAE systems. This integration is expected to transform design and engineering practices by offering insights into the potential of these technologies.

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Supersystem digital twin-driven framework for new product conceptual design

Haynes,

Yang

2023

Advanced Engineering Informatics

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Digital twins for automotive development: Two wheelers application

Cited by 6 publications

References 45 publications

Integration of Cyber-Physical Systems, Digital Twins and 3D Printing in Advanced Manufacturing: A Synergistic Approach

Integration of Cyber-Physical Systems, Digital Twins and 3D Printing in Advanced Manufacturing: A Synergistic Approach

A Review of Generative Models for 3D Vehicle Wheel Generation and Synthesis

Supersystem digital twin-driven framework for new product conceptual design

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