2026-09-18T09:49:50Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/92962024-06-21T00:05:31Zcom_10259_4201com_10259_5086com_10259_2604com_10259_6168col_10259_4505col_10259_6169

Ex-ante life cycle assessment of directed energy deposition based additive manufacturing: A comparative gearbox production case study Santiago Herrera, Mario Igos, Elorri Alegre Calderón, Jesús Manuel Martel Martín, Sonia Barros García, Rocío Life cycle assessment Scale-up Ex-ante LCA Additive manufacturing Emerging technologies Eco-desing As new technologies emerge is necessary to assess if they can actually contribute to sustainable improvement of industrial processes. Life Cycle Assessment (LCA) is a valuable tool to determine environmental impacts and compare systems. However, this comparison raises challenges when they have different maturity. This paper performs ex-ante LCA of an additive manufacturing (AM) technology, based on a step-wise approach built with parametrized modelling, allowing fair comparison with its conventional counterpart, for the study case of a gearbox component. Results show that AM technology generates higher impacts than conventional manufacturing (CM) casting process, using baseline values. These impacts can be reduced by 94% with best operating performances from literature, with emissions from 4520 to 264 kg CO2 eq./kg piece, and non-significant difference with CM (demonstrated by Monte Carlo sampling). A 58% weight reduction is necessary for the AM process to improves its environmental sustainability. This research provides eco-design recommendations supporting decision making for further development of new technology. 2024-06-20T08:15:40Z 2024-06-20T08:15:40Z 2024-04 info:eu-repo/semantics/article 2214-9937 http://hdl.handle.net/10259/9296 10.1016/j.susmat.2023.e00819 eng Sustainable Materials and Technologies. 2024, V. 39, e00819 https://doi.org/10.1016/j.susmat.2023.e00819 http://creativecommons.org/licenses/by-nc/4.0/ info:eu-repo/semantics/openAccess Atribución-NoComercial 4.0 Internacional Elsevier