Digital Twin-Driven Product Sustainable Design for Low Carbon FootprintSource: Journal of Computing and Information Science in Engineering:;2023:;volume( 023 ):;issue: 006::page 60805-1DOI: 10.1115/1.4062427Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Product sustainability is a pressing global issue that requires urgent improvement, and low-carbon design is a crucial approach toward achieving sustainable product development. Digital twin technology, which connects the physical and virtual worlds, has emerged as an effective tool for supporting product design and development. However, obtaining accurate product parameters remains a challenge, and traditional low-carbon product design primarily focuses on design parameters. To address these issues, this paper proposes a method for data collection throughout the product lifecycle, leveraging the Internet of Things. The paper envisions the automatic collection of product lifecycle data to enhance the accuracy of product design. Moreover, traditional low-carbon design often has a limited scope that primarily considers product structure and lifecycle stage for optimization. In contrast, combining digital twin technology with low-carbon design can effectively improve product sustainability. Therefore, this paper proposes a three-layer architecture model of product sustainability digital twin, comprising data layer, mapping layer, and application layer. This model sets the carbon footprint as the iterative optimization goal and facilitates the closed-loop sustainable design of the product. The paper envisions sustainable product design based on digital twins that can address cascading problems and achieve closed-loop sustainable design.
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contributor author | He, Bin | |
contributor author | Mao, Hangyu | |
date accessioned | 2023-11-29T18:58:53Z | |
date available | 2023-11-29T18:58:53Z | |
date copyright | 5/25/2023 12:00:00 AM | |
date issued | 5/25/2023 12:00:00 AM | |
date issued | 2023-05-25 | |
identifier issn | 1530-9827 | |
identifier other | jcise_23_6_060805.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4294505 | |
description abstract | Product sustainability is a pressing global issue that requires urgent improvement, and low-carbon design is a crucial approach toward achieving sustainable product development. Digital twin technology, which connects the physical and virtual worlds, has emerged as an effective tool for supporting product design and development. However, obtaining accurate product parameters remains a challenge, and traditional low-carbon product design primarily focuses on design parameters. To address these issues, this paper proposes a method for data collection throughout the product lifecycle, leveraging the Internet of Things. The paper envisions the automatic collection of product lifecycle data to enhance the accuracy of product design. Moreover, traditional low-carbon design often has a limited scope that primarily considers product structure and lifecycle stage for optimization. In contrast, combining digital twin technology with low-carbon design can effectively improve product sustainability. Therefore, this paper proposes a three-layer architecture model of product sustainability digital twin, comprising data layer, mapping layer, and application layer. This model sets the carbon footprint as the iterative optimization goal and facilitates the closed-loop sustainable design of the product. The paper envisions sustainable product design based on digital twins that can address cascading problems and achieve closed-loop sustainable design. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | Digital Twin-Driven Product Sustainable Design for Low Carbon Footprint | |
type | Journal Paper | |
journal volume | 23 | |
journal issue | 6 | |
journal title | Journal of Computing and Information Science in Engineering | |
identifier doi | 10.1115/1.4062427 | |
journal fristpage | 60805-1 | |
journal lastpage | 60805-8 | |
page | 8 | |
tree | Journal of Computing and Information Science in Engineering:;2023:;volume( 023 ):;issue: 006 | |
contenttype | Fulltext |