Physics-Informed and Hybrid Machine Learning in Additive Manufacturing: Application to Fused Filament Fabrication

Summary

This article investigates physics-informed and hybrid machine learning strategies that incorporate physics knowledge into experimental data-driven deep-learning models for predicting bond quality and porosity of fused filament fabrication (FFF) parts. Three strategies are explored: incorporating physics constraints into the loss function, using physics model outputs as additional inputs, and pre-training with physics model data then updating with experimental data. Eight combinations are tested. Results show that combining multiple strategies yields accurate models even with limited experimental data, ensuring physical consistency between bond quality and tensile strength.