Prediction of porosity, hardness and surface roughness in additive manufactured AlSi10Mg samples.

ABSTRACT: Despite the advantages of additive manufacturing, its widespread adoption is still hindered by the poor quality of the fabricated parts. Advanced machine learning techniques to predict part quality can improve repeatability and open additive manufacturing to various industries. This study aims to accurately predict the relative density, surface roughness and hardness of AlSi10Mg samples produced by selective laser melting regarding process parameters such as scan speed, layer thickness, laser power, and hatch distance. For this purpose, data including porosity, surface hardness, and roughness were extracted from the literature, and additional measurements were performed on additive manufactured samples in the current work. This work compares five supervised machine learning algorithms, inc

SUBMITTER: Alamri F 

PROVIDER: S-EPMC11892887 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature