Minimizing environmental impact in hybrid additive manufacturing: a layer-resolved decision-support tool

Abstract Manufacturing remains a major source of greenhouse gas emissions, often locked into carbon-intensive routes. Although Additive Manufacturing (AM) improves material efficiency, its high specific energy consumption can offset environmental benefits compared to subtractive manufacturing (SM). Hybrid strategies combining AM and SM offer a promising approach, but model-based procedures to support their selection at the design stage are limited. This study presents an early-stage LCA-based decision-support framework, designed for straightforward industrial implementation, that starts from a CAD model to compare manufacturing sequences and identify those that minimize the carbon footprint (CF) within cradle-to-gate boundaries. The geometry is discretized into uniform layers, and the Solid-to-Cavity Ratio is extended to a layer-resolved form ( SCR i ) scaled by process-specific impact intensities for milling and Wire Arc Additive Manufacturing (WAAM); the switching layer is selected as the one minimizing the cumulative CF over all admissible positions. Applied to three controlled benchmark geometries and the mock-up of a real industrial landing gear, the framework locates the optimal switching layer and quantifies the resulting reduction in CF, up to 61.2% relative to milling and up to 17.5% relative to WAAM, with the optimum coinciding with a single-process route in some cases. The analysis highlights that overall SCR is not always sufficient for predicting performance, as the spatial distribution of material per layer can drive impact. Sensitivity analyses considering different levels of recycled content and different electricity mixes illustrate how the framework supports exploring uncertainty and trade-offs in route selection.