[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119264-en":3,"doc-seo-119264-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119264,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","A parametric environmental impact model for manufacturing components based on machine learning techniques","Environmental sustainability-oriented design is gaining importance in industry due to climate-change impacts. Most effective sustainable decisions are made at the earliest design stage, when teams must estimate environmental effects quickly and with limited data. This paper proposes a method to build a parametric model for environmental impact assessment of manufacturing components during conceptual design. The approach enables consistent environmental considerations while relying on high-level data, supporting early-stage evaluation through machine learning.","[Available online at www.sciencedirect.com](Available online at www.sciencedirect.com)  \nScienceDirect  \nProcedia CIRP 128 (2024) 351–356  \n34th CIRP Design Conference  \nA parametric environmental impact model for manufacturing components  \nbased on machine learning techniques  \nLuca Manuguerraa *, Federica Cappellettia, Marta Rossib, Marco Mandolinia, Michele Germania  \na Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, Italy  \nbUniecampus, Via Isimbardi, 10, 22060 Novedrate (CO), Italy  \n* Corresponding author. Tel.: 071 2204402. E-mail address: [l.manuguerra@staff.univpm.it](l.manuguerra@staff.univpm.it)  \nAbstract  \nEnvironmental sustainability-oriented design is becoming increasingly important in the industrial field partly because of the effects of climate change. Sustainable development-oriented choices are most effective at the early design stage. The design team must be able to assess approximately and quickly the environmental impact early in the design phase. From these motivations comes the need for a method that quickly and with few parameters can estimate the product environmental impact during the conceptual design phase. Machine learning techniques appear to be well suited to meet this challenge. Machine learning is an established research topic in Industry 4.0 and its adoption is increasing. The integration of machine learning within conceptual design quickly facilitates the approximate assessment of environmental impact through highlevel data. In this paper, a method is proposed to obtain a parametric model for the environmental impact assessment of manufacturing components at the early design stage. It allows consistent considerations concerning environmental matters, albeit little information available during design phase.  \n© 2024 The Authors. Published by Elsevier B.V.  \nThis is an open access article under the CC BY-NC-ND license ([https://creativecommons.org/licenses/by-nc-nd/4.0](https://creativecommons.org/licenses/by-nc-nd/4.0)) Peer-review under responsibility of the scientific committee of the 34th CIRP Design Conference  \nKeywords: Machine Learning, Data Science, Sustainability, Design, Life Cycle Assessment  \n1. Introduction  \nThe environmental effects of a product's life cycle must be considered when designing a product due to the damage caused by climate change [1] . The design team must be able to evaluate the environmental performance of numerous project proposals from the early design stages [2] . Furthermore, it must already be able to evaluate the different scenarios of the production chain of the product's life cycle. Life Cycle Assessment (LCA) tools allow very detailed analyses of the product environmental impact, without however giving the designer alternatives [3] .The solution to the opportunity to make less impactful products from an environmental point of view is left to the designer’s and engineer’s skills and ability. Based on the available data and the objective of the analysis, the system  \nboundaries can be chosen. The boundaries of the system can include the phases of raw material extraction, production, distribution, use and final disposal of the product. The reference standards are ISO 14040 and ISO 14044. The practical application of eco-design and circular economy approaches represents an opportunity to be seized in the industrial sector for the reduction of environmental impacts and the creation of economic value [4] .  \nProducing products with less and less impact and placed within a circular process is now necessary for multiple purposes: from an environmental perspective (i.e., aware consumers) , economic (i.e., competitiveness) and geo-political point of view, to reduce dependence in the supply of increasingly scarce raw materials.  \n2212-8271 © 2024 The Authors. Published by Elsevier B.V.  \nThis is an open access article under the CC BY-NC-ND license ([https://creativecommons.org/licenses/by-nc-nd/4.0](https://creativecommons.org/licenses/by","cbCaiq3K1fp4bHid","https://ap.wps.com/l/cbCaiq3K1fp4bHid","pdf",473335,1,6,"English","en",105,"# Introduction\n## Parametric LCA for Conceptual design\n## Machine learning in Life Cycle Assessment","[{\"question\":\"Why is early-stage environmental assessment important in manufacturing design?\",\"answer\":\"Because sustainable development choices are most effective in the early design phase, when designers need fast, approximate evaluation despite limited information.\"},{\"question\":\"What challenge does conceptual design pose for environmental impact modeling?\",\"answer\":\"Conceptual design lacks detailed data, requires quick decisions and trade-offs, and makes detailed parametric LCA models time-consuming and information-intensive.\"},{\"question\":\"How does machine learning help in lifecycle assessment for conceptual design?\",\"answer\":\"Machine learning can learn from training data to create a surrogate model that enables approximate environmental impact assessment using high-level data without reprogramming.\"}]","A parametric environmental impact model for manufacturing components based on machine learning techniques | 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is early-stage environmental assessment important in manufacturing design?","Question",{"text":75,"@type":76},"Because sustainable development choices are most effective in the early design phase, when designers need fast, approximate evaluation despite limited information.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What challenge does conceptual design pose for environmental impact modeling?",{"text":80,"@type":76},"Conceptual design lacks detailed data, requires quick decisions and trade-offs, and makes detailed parametric LCA models time-consuming and information-intensive.",{"name":82,"@type":73,"acceptedAnswer":83},"How does machine learning help in lifecycle assessment for conceptual design?",{"text":84,"@type":76},"Machine learning can learn from training data to create a surrogate model that enables approximate environmental impact assessment using high-level data without 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