A voxel-based automated life cycle assessment framework for additive manufacturing: a case study of laser powder bed fusion

Abstract

As global regulations on industrial greenhouse gas emissions become increasingly stringent, manufacturers are required to quantify the environmental consequences of their process chains with finer resolution and shorter turnaround times. Additive manufacturing (AM), with its high geometric design freedom and tunable process parameters, further amplifies this need because relatively small modifications in part geometry or process settings can substantially alter energy use, material consumption, and, ultimately, environmental impacts. Conventional life cycle assessment (LCA) provides a rigorous, ISO 14040/14044-compliant framework for such evaluations, but it is typically time-consuming, case-specific, and heavily dependent on expert modelling and software operation. Recent rapid LCA (RLCA) approaches partially alleviate these burdens by automating inventory construction and employing data-driven models. However, many of these methods remain tied to specific geometries or training datasets and provide limited transparency regarding how design and process changes propagate to environmental impact results. To address these limitations, this study develops a voxel-based automated life cycle assessment (VLCA) framework that adopts a small volumetric element (voxel) as the functional unit and analytically scales its environmental impact to arbitrary parts. The framework is conceptually applicable to a broad range of AM processes, but is instantiated and validated here for laser powder bed fusion (LPBF) solid parts under a gate-to-gate system boundary. First, voxel-level operating times are predicted from LPBF process parameters such as laser power, scan speed, hatch distance, and layer thickness, and the corresponding voxel-level environmental impacts are quantified using process energy use and gas consumption. Second, physics-informed regression models are constructed to link process parameters to voxel-level laser exposure and recoating times, enabling data-efficient prediction from a limited set of simulated voxel cases rather than extensive experimental campaigns. Finally, closed-form voxel-to-part scaling relations based on part volume and build height are used to compute part-level environmental impacts, defining a geometry-agnostic voxel-to-part architecture that does not require retraining when new solid geometries are introduced. In the LPBF case study, 225 voxel cases are generated and used to train and validate the voxel-level models, and six solid validation parts with different base areas and heights are employed to evaluate part-level prediction performance. The resulting mean absolute percentage error in part-level environmental impact ranges from 0.04% to 2.32% across variations in part geometry, and remains within 1.66–2.05% when the scan speed is systematically varied while other parameters are fixed. These results demonstrate that the proposed VLCA framework can provide accurate, interpretable, and data-efficient environmental impact predictions suitable for early-stage LPBF design and process parameter exploration. In addition, the voxel-based structure offers a generalizable foundation for extending the framework to additional impact indicators and other AM processes, supporting more responsive and scalable environmental decision-making in additive manufacturing.