A computational method for detecting aspect ratio and problematic features in additive manufacturing

Abstract In metal additive manufacturing, geometries with high aspect ratio (AR) features are often associated defects caused by thermal stresses and other related build failures. Ideally, excessively AR would be detected removed in the design phase to avoid unwanted failure during manufacture. However, is scale orientation independent identifying across all scales orientations exceptionally challenging. Furthermore, not as easy recognise thin walls fine needles. There therefore a pressing need for further development field of problematic detection manufacturing processes. this work, dimensionless ( D 1/ 2) based on two distance metrics that extracted from triangulated mesh proposed. Based method, different (e.g. wall, helices polyhedra) were generated evaluated produce similar AR. The prediction results compared known theoretical values typical geometries.By combining metric segmentation, method was extended analyse geometry complex features. proposed provides powerful, general promising way automatically detect tackle relevant defect issues prior