Comparative evaluation of supervised machine learning algorithms in the pbkp_rediction of the relative density of 316L stainless steel fabricated by selective laser melting

Abstract:

To find a robust combination of selective laser melting (SLM) process parameters to achieve the highest relative density of 3D printed parts, pbkp_redicting the relative density of 316L stainless steel 3D printed parts was studied using a set of machine learning algorithms. The SLM process brings about the possibility to process metal powders and built complex geometries. However, this technology’s applicability is limited due to the inherent anisotropy of the layered manufacturing process, which generates porosity between adjacent layers, accelerating wear of the built parts when in service. To reduce interlayer porosity, the selection of SLM process parameters has to be properly optimized. The relative density of these manufactured objects is affected by porosity and is a function of process parameters, rendering it a challenging optimization task to solve. In this work, seven supervised machine learning …

Año de publicación:

2021

Keywords:

Fuente:

google