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

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. • Proposes a predictive modeling framework to estimate key physical properties in Ti-6Al-4V additive manufacturing. • Reduces reliance on costly physical experiments by leveraging advanced machine learning techniques. • Introduces two dimensionless parameters ( Π 1 and Π 2 ) to enhance predictive accuracy beyond standard process inputs. • Compares performance of eight models (statistical and machine learning) on a curated dataset with both conventional and novel features. • Demonstrates that model and feature selection are critical for optimizing metal AM processes.