Roughness investigation of steel 316L parts fabricated by Metal Fused Filament Fabrication. (September 2022)

• Record Type: Journal Article
• Authors: Boschetto, Alberto; Bottini, Luana; Miani, Fabio; Veniali, Francesco
• Is Part Of: Journal of manufacturing processes. Volume 81(2022)
• Page Start: 261
• Page End: 280
• Publication Date: 2022-09
• Subjects: Metal Fused Filament Fabrication -- Roughness -- Metal-polymer composite filament; Production management -- Data processing -- Periodicals; Manufacturing processes -- Periodicals; Procestechnologie; Productietechniek; Production -- Gestion -- Informatique -- Périodiques; Fabrication -- Périodiques; Manufacturing processes; Production management -- Data processing; Periodicals; 670.5
• DOI: 10.1016/j.jmapro.2022.06.077
• Languages: English
• ISSNs: 1526-6125

Abstract

Abstract: Metal Fused Filament Fabrication attracted significant interest in both academia and industry to fabricate net-shape engineering components via low-cost 3D printing technology. However, many fields such as the roughness control and prediction are still unexplored. These aspects are very important to consider especially when the process is used for the fabrication of functional parts: the roughness, in fact, deeply influences the functionality of a component in terms of friction, contact deformation, heat, electric current conduction, etc. Aim of this work is to fulfill the lack of knowledge about the prediction of the obtainable roughness in this process. For the purpose an in-depth analysis of the effect of the processing parameters on surface roughness profiles obtained at green and sintered stages is reported. The experiment is planned using the Design of Experiments technique and the data are collected followed a rigid procedure developed for the scope. The results show that the profiles are markedly affected by the process parameters and depend upon the deposition angles. Green and sintered parts show different behaviors caused by different underling mechanisms. The complexity of the process is highlighted and described by regression models for different deposition angles. The models set allows for a multiple optimization in order to achieve processing parameters combinations able to satisfy desired requirements. Graphical abstract: Unlabelled Image