Physics-informed and hybrid machine learning in additive manufacturing: application to fused filament fabrication

Summary

This article studies physics-informed and hybrid machine-learning strategies for predicting bond quality and porosity in fused filament fabrication. It compares ways to incorporate physical constraints, simulation outputs, and simulation-based pretraining.

Research contribution

The study evaluates three strategies for integrating physical knowledge with experimental data in deep-learning predictions of manufacturing quality.