Multi-scale surface characterization in additive manufacturing using CT

Abstract

In additive manufacturing, the part geometry, including its internal structure, can be optimized to answer functional requirements by optimizing process parameters. This can be performed by linking process parameters to the resulting manufactured geometry. This paper deals with an original method for surface geometry characterization of printed parts (using Fused Filament Fabrication FFF) based on 3D Computer Tomography (CT) measurements. From 3D measured data, surface extraction is performed, giving a set of skin voxels corresponding to the internal and external part surface. A multi-scale analysis method is proposed to analyse the relative internal area of the total surface obtained at different scales (from sub-voxel to super-voxel scales) with different process parameters. This analysis turns out to be relevant for filling strategy discrimination.

Challenges and Status on Design and Computation for Emerging Additive Manufacturing Technologies

“…By taking X-ray tomography or scanning electron microscope images, Vlasea et al [129] took the natural bone porous macro architecture as an input, and draw the micro-channels to mimic its biological and mechanical properties. Quinsat et al [130] extracted surface information from the images and represented the information by skin voxels corresponding to the internal and external surfaces in subvoxel to super-voxel scales, which are used to determine the filling strategy. Martinez et al [131] applied the Voronoi diagram to generate an implicit representation to create foam geometry.…”

Analysis of tensile behavior of Nylon-CF fabricated using material extrusion

“…By optimizing process parameters, it is possible to answer functional requirements for the part geometry, including its internal structure. 6 The parameters are selected based on the individual experiences and the acquired information. Although plenty of information is available in the public domain to select printing parameters for various materials and AM processes, there is a lack of comprehensive information on the combination of particular techniques and materials.…”

A deep learning-based dimensional measurement directly from limited-angle CT sinograms for Additive Manufacturing

“…Previous studies have proved that CT can effectively detect AM defects such as porosity, crack and discontinuity [4,5,6]. In addition, AM surface detecting using CT technology is increasingly studied, especially for roughness of internal surface [7,8]. CT is also used in density measurement to replace traditional methods such as Archimedes test [9,10].…”