In-process evaluation of layer defects and surface topography on material extruded parts through a novel point cloud functional analysis

Abstract

Material Extrusion (MEX) is one of the most used Additive Manufacturing (AM) technologies, thanks to its appealing in various industrial fields. Despite its numerous advantages, process quality remains an issue when compared to other, more established technologies. In-process monitoring has become fundamental to meet the tight requirements of precision industries. 
In this work, a novel MEX monitoring methodology based on point-cloud functional analysis was tested and discussed. Using a blue-laser line profilometer integrated into a consumer 3D printer, the point cloud of each layer was obtained immediately after its completion and subsequently analysed. Functional analysis tools were employed to characterize the surface topography and assess the distribution of material and voids to detect defects. Common defective conditions in the form of excess or lack of deposited material, referred to as overfill and underfill, respectively, were induced by varying printing parameters and the specimen’s cross-section. After slicing the layer surface according to specified height thresholds, functional parameters, such as the Projected Percentage Area (PPA), void and material volume percentage, and void and material mean thickness were computed and analysed. Results indicated that the PPA values effectively depicted the overall surface conditions and were suitable for enabling corrective actions through the contour extraction of the defective regions. Meanwhile, the percentage volume and mean thickness of voids and material, referred to as functional parameters, provided more detailed insights into the morphology of the layer surface and the detected defects.