Geometry aware laser polishing of LPBF AlSi10Mg defence components with physics inspired neural network based surface roughness prediction

Laser Powder Bed Fusion (LPBF) is widely used to produce lightweight aluminium alloys such as AlSi10Mg for aerospace and defence applications. Yet, the as-built parts often suffer from excessive surface roughness, which reduces fatigue resistance and impedes fluid flow problems that are especially critical in narrow slots and channels. Conventional finishing techniques find it difficult to access and polish such confined features, making alternative strategies necessary. In this work, we investigated laser-based surface remelting as a geometry-aware polishing method for LPBF AlSi10Mg slots with widths between 1 and 5 mm. The experiments, carried out on 30 samples, achieved up to ~ 87% reduction in average surface roughness (Ra), with profilometry confirming the removal of powder-induced asperities. Although this degree of improvement is in line with earlier reports, the novelty of the present study lies in introducing the first predictive framework that combines slot geometry particularly slot width ith physics-inspired descriptors in an artificial neural network (ANN). To overcome the limitations of the experimental dataset, around 2000 synthetic yet physically consistent data points were generated to guide the model. The resulting ANN achieved excellent predictive accuracy (R² ≈ 0.98–0.99) and closely matched experimental polishing trends. While demonstrated here for slot geometries, this integrated experimental–computational approach offers a pathway toward AI-assisted, geometry-sensitive surface optimisation for aerospace and defence components where conventional polishing is not feasible.