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

2026-08-17

AI Summary

本文探讨了通过将物理知识融入实验数据驱动的深度学习模型中，以预测熔融长丝制造零件的粘结质量和孔隙度的问题。

Abstract

This article investigates several physics-informed and hybrid machine learning strategies that incorporate physics knowledge in experimental data-driven deep-learning models for predicting the bond quality and porosity of fused filament fabrication (FFF) parts. Three types of strategies are explored to incorporate physics constraints and multi-physics FFF simulation results into a deep neural network (DNN), thus ensuring consistency with physical laws: (1) incorporate physics constraints within the loss function of the DNN, (2) use physics model outputs as additional inputs to the DNN model, and (3) pre-train a DNN model with physics model input-output and then update it with experimental data. These strategies help to enforce a physically consistent relationship between bond quality and tensile strength, thus making porosity predictions physically meaningful. Eight different combinations of the above strategies are investigated. The results show how the combination of multiple strategies produces accurate machine learning models even with limited experimental data.

Problem

• Physics-Informed
• Machine Learning
• Additive Manufacturing
• Fused Filament Fabrication
• Porosity Prediction

Innovation

• Physics-Informed Machine Learning
• Hybrid Machine Learning
• Fused Filament Fabrication
• Deep Neural Network
• Physical Consistency