Additive Manufacturing (AM) appears to be the best candidate to manufacture random architected materials, as it offers significant freedom in the design of hollowed parts with complex geometry. However, when these structures are needed with thins walls and struts, AM processes may encounter difficulties in properly manufacturing these structures due to their capability limits. This study proposes to characterize the manufacturing of random architected structures to see firstly their fabricability and the capability of the additive manufacturing processes used, such as vat photopolymerization (Stereolithography process (SLA)), material extrusion (Fused Filament Fabrication process (FFF)) and powder bed fusion (Selective Laser Sintering process (SLS)) through tomographic, dimensional, and mass analysis. Several defects specific to each process were identified. A higher predominance of porosities, lack of printing and excess of material manifests as trapped or partially fused powder for SLS and angel hair for FFF. These defects strongly affect the dimensional and geometric accuracy of the struts and, thus, the final mass of the structure obtained with these two processes. The SLA process makes it possible to print thinner details of random architected structures with better material quality and good dimensional and geometric accuracy, under the conditions and protocol used in this study.
The aim of this study is to characterize the link between the cutting conditions used while trimming a composite material glass fiber reinforced polymer (GFRP) and the surface quality by characterizing the area roughness of the machined surfaces. In the experimental evaluation, a central composite design with 20 combinations was used to study cutting parameters (cutting speed (V c), radial engagement (a e) and tooth feed (f z)). The area roughness parameters (Sa, Sq, Sz) were measured by a KEYENCE VHX-6000 3D profilometer. Response Surface Methodology (MSR) were used to determine mathematical models using experimental data.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.