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DC Field | Value | Language |
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dc.contributor.author | Naser, AZ | - |
dc.contributor.author | Defersha, F | - |
dc.contributor.author | Pei, E | - |
dc.contributor.author | Zhao, YF | - |
dc.contributor.author | Yang, S | - |
dc.date.accessioned | 2023-08-30T14:09:08Z | - |
dc.date.available | 2023-08-30T14:09:08Z | - |
dc.date.issued | 2023-08-18 | - |
dc.identifier | ORCID iD: Eujin Pei https://orcid.org/0000-0002-3628-8689 | - |
dc.identifier.citation | Naser, A.Z. et al. (2023) 'Toward automated life cycle assessment for additive manufacturing: A systematic review of influential parameters and framework design', Sustainable Production and Consumption, 41, pp. 253 - 274. doi: 10.1016/j.spc.2023.08.009. | en_US |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/27090 | - |
dc.description.abstract | Additive manufacturing (AM), a disruptive technology of building parts layer-by-layer directly from 3D models, has been considered a tempting cleaner production process compared to other conventional production routes. This is because AM has demonstrated impressive green characteristics which contributed to significantly reduced material and energy consumptions, a shorter supply chain, and diversion of the waste stream by reparation, etc. The majority of the current quantitative studies on the environmental assessment of AM are based on utilizing the knowledge-intensive Life Cycle Assessment (LCA) methodology. Further, current studies on assessing the environmental performance of AM are based on a limited selection of design- or process-related parameters. These knowledge barriers may cause delays and challenges in the selection of the optimal design and process parameters for additively manufactured parts. Such challenges are particularly prevalent during the product design and planning stages due to the iterative design-evaluation process. Therefore, there is a need for an automated LCA tool to support AM toward elevated sustainability. As such, this paper provides three main contributions to the research community. Firstly, this is the first study to identify a comprehensive set of influential AM design and process parameters that pose an impact on the environmental performance of AM. Secondly, this review also summarizes the impacts of each of these parameters on the environmental sustainability of AM. Lastly, to the best of the authors' knowledge, no work in the literature has been reported on automating LCA for AM. Thus, this paper promotes research toward a more environmentally benign and innovative AM technology by proposing a new framework to automate the environmental assessment of the process. The proposed framework is anticipated to take advantage of the fruitful integration between Machine Learning (ML) and the product process co-design concept to mitigate the challenges and limitations associated with the current LCA-based assessment tools. | en_US |
dc.description.sponsorship | Natural Science and Engineering Research Council of Canada (NSERC) Discovery Grant (RGPIN-2022-03448). | en_US |
dc.format.extent | 253 - 274 | - |
dc.format.medium | Electronic | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | Elsevier on behalf of Institution of Chemical Engineers | en_US |
dc.rights | Copyright © 2023 Institution of Chemical Engineers. Published by Elsevier Ltd. All rights reserved. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1016/j.spc.2023.08.009, made available on this repository under a Creative Commons CC BY-NC-ND attribution licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). | - |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | - |
dc.subject | additive manufacturing | en_US |
dc.subject | life cycle assessment | en_US |
dc.subject | design for additive manufacturing | en_US |
dc.subject | design and process parameters | en_US |
dc.subject | product-process co-design | en_US |
dc.subject | machine learning | en_US |
dc.subject | data-driven predictive model | en_US |
dc.title | Toward automated life cycle assessment for additive manufacturing: A systematic review of influential parameters and framework design | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1016/j.spc.2023.08.009 | - |
dc.relation.isPartOf | Sustainable Production and Consumption | - |
pubs.publication-status | Published | - |
pubs.volume | 41 | - |
dc.identifier.eissn | 2352-5509 | - |
dc.rights.holder | Institution of Chemical Engineers | - |
Appears in Collections: | Brunel Design School Embargoed Research Papers |
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FullText.pdf | Embargoed until 18 August 2024 | 1.11 MB | Adobe PDF | View/Open |
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