Process Parameter Effects on the Environmental Performance of Wire Arc Additive Manufacturing of Invar 36 Alloy: A Life Cycle Assessment Approach

This study quantitatively evaluates the impact of Wire Arc Additive Manufacturing (WAAM) process parameters on the environmental performance of components produced in Invar 36 alloy. An experimental campaign involving 49 parameter sets was carried out by varying wire feed speed, welding voltage, and welding speed. For each condition, electrical signals, shielding gas consumption, and wire usage were measured and converted into parameter-resolved Life Cycle Inventory (LCI) data. A cradle-to-gate Life Cycle Assessment (LCA) was implemented in SimaPro 9.6 using the European CML-IA baseline v3.10 midpoint method, adopting 1 kg of as-built deposited Invar 36 as the functional unit. Results show that feedstock production represents the dominant hotspot (8.68 kg CO 2 -eq/kg), while the WAAM stage contributes between 1.13 and 4.12 kg CO 2 -eq/kg, leading to a total impact ranging from 9.81 to 12.80 kg CO 2 -eq/kg. As a result, this study demonstrates that process parameter selection strongly influences environmental performance. Indeed, Specific Energy Consumption (SEC) ranges from 0.44 to 1.95 kWh/kg, while argon consumption varies between 0.26 and 1.51 kg/kg of deposited material. By analysing the results and excluding unstable or manufacturing-infeasible deposition regimes, the optimal trade-off between process stability and environmental impact is achieved at approximately WFS = 7 m/min, V = 20 V, and WS = 6.5 mm/s. Beyond quantifying the environmental hotspots of Invar 36 WAAM, this study provides a dedicated, parameter-resolved cradle-to-gate LCA based on experimentally measured foreground data collected across 49 process parameter combinations. By combining environmental assessment with feasibility screening of the investigated deposition regimes, the work identifies not only environmentally favourable conditions, but also parameter regions that are technologically viable for WAAM processing of Invar 36. The resulting dataset provides a benchmark foundation for future sustainability-oriented process optimisation and decision support in WAAM.