Comparative assessment of design approaches and wire arc additive manufacturing variants using life cycle assessment: gas metal arc welding, gas tungsten arc welding, and plasma arc welding
Wire Arc Additive Manufacturing (WAAM) can enable large-component production with reduced material use, yet published life cycle assessment (LCA) comparisons are often hard to interpret due to inconsistent geometries, functional units, and process assumptions. This study performs a harmonised cradle...
| Autores: | , , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2026 |
| País: | España |
| Recursos: | Universidad Pública de Navarra |
| Repositorio: | Academica-e. Repositorio Institucional de la Universidad Pública de Navarra |
| OAI Identifier: | oai:academica-e.unavarra.es:2454/56553 |
| Acesso em linha: | https://hdl.handle.net/2454/56553 |
| Access Level: | acceso abierto |
| Palavra-chave: | Wire arc additive manufacturing (WAAM) Design for additive manufacturing (DFAM) Topological optimisation Life cycle assessment (LCA) GMAW GTAW PAW |
| Resumo: | Wire Arc Additive Manufacturing (WAAM) can enable large-component production with reduced material use, yet published life cycle assessment (LCA) comparisons are often hard to interpret due to inconsistent geometries, functional units, and process assumptions. This study performs a harmonised cradle-to-gate LCA comparing two WAAM-oriented design strategies—topology optimisation and Design for Additive Manufacturing (DfAM)—and three arc-based WAAM variants: Gas Metal Arc Welding (GMAW), Gas Tungsten Arc Welding (GTAW), and Plasma Arc Welding (PAW), all under a common functional requirement. The baseline inventory is experimentally measured for the DfAM–GMAW route, including deposition and CNC post-processing. GTAW and PAW are evaluated via literature-parameterised scaling using worst/base/best scenarios to reflect uncertainty. Impacts are calculated with ReCiPe 2016 v1.1 Endpoint (H) in person equivalents (PE). DfAM reduces total impact by 46.5 % compared with topology optimisation (20.2 to 10.8 PE), mainly due to lower deposited mass and shorter post-processing. Among arc variants, GMAW shows the lowest impact, while GTAW and PAW increase impacts within the variability envelope. Sensitivity analyses indicate moderate dependence on electricity supply (9.90–11.54 PE across grid mixes) without changing overall trends. Feedstock sourcing is a major lever: replacing primary aluminium with recycled aluminium reduces impacts monotonically, reaching −73.6 % at 100 % recycled content (2.85 PE). A model-based association analysis suggests that process efficiency and productivity (deposition rate) dominate specific electricity demand, without claiming statistical inference for parameterised GTAW/PAW. Overall, combining DfAM, GMAW selection, and higher recycled aluminium content offers a practical pathway to substantially reduce WAAM impacts. |
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