Work

Machine Learning application in predicting the properties of Ti-6Al-4V samples produced with Power Bed Fusion technology

Year: 2025

Type: article

Abstract: In the context of Additive Manufacturing (AM) with high-cost materials such as Ti-6Al-4V (Ti64), minimizing physical experimentation through predictive models offers a strategic advantage in reducing lead time and manufacturing costs. In view of this, the present study attempts to build a systematic framework to establish such a predictive compacity. In particular, eight supervised Machine Learning (ML) models are compared based on their performance in predicting critical physical properties of AM components, i.e., 3D surface roughness, relative density, and hardness. The curated dataset comprises both traditional dimensional printing parameters and dimensionless parameters. Specifically, in addition to the conventional Volumetric Energy Density (VED) and four primary printing parameters (laser power, hatching distance, scanning speed, and layer height), the study discusses two dimensionless numbers Π 1 and Π 2 for property prediction. This work underscores the role of selecting predictors and models in advancing data-driven process optimization, offering a scalable approach for reducing experimental overhead in metal AM.

Source: Results in Engineering

DOI: 10.1016/j.rineng.2025.107404