Life cycle analysis (LCA) applied to the calculation of environmental impacts of metal additive manufacturing processes (DED-WAAM)

Abstract

Wire Arc Additive Manufacturing (WAAM) belongs to the family of Direct Energy Deposition (DED) processes. It uses a metallic wire feedstock melted by an electric arc to build components layer by layer. The method is increasingly recognized for combining high material efficiency, geometric flexibility, and rapid deposition rates, making it attractive for large-scale parts in sectors such as aerospace, automotive, and heavy machinery.

Despite these advantages, the environmental performance of WAAM is not yet fully characterized due to the limited availability of reliable process-specific inventory data. To address this gap, this study develops a preliminary methodology for con-structing life cycle inventory (LCI) datasets for WAAM, coupled with uncertainty or reliability assessment of these data using a pedigree matrix approach. Based on this, an LCA model was implemented using empirical process data collected at the Addi-madour Additive Manufacturing Solutions platform.

The key contribution of this work lies in the development of a detailed process tree that maps the inventory for all relevant unit processes, ranging from pre-processing, deposition, and post processing. In addition to this, the application of a pedigree matrix to quantify uncertainty in each inventory dataset provides a more accurate and data-driven representation of the WAAM LCA inventory. The system boundary covered substrate preparation, wire feedstock deposition, shielding gas usage, and associated electricity inputs. The inventory was modelled using the SimaPro soft-ware and the Ecoinvent v3 database, with a functional unit defined as 1 kg of net printed part.

LCA results indicate that the substrate material and shielding gas are dominant con-tributors across most environmental impact categories (midpoint impact categories), including Global Warming Potential, Acidification, and Resource Scarcity. These results are consistent with literature while highlighting current data quality limitations in AM-LCA studies. The work also identifies opportunities for improving environmental performance through process optimization and emphasizes the need for in-situ monitoring to generate higher-resolution LCI data for future WAAM LCA studies. This thesis contributes to bridging the gap between academic LCA models and industrial-scale, data-driven assessments of metal additive manufacturing.