融合物理规律与机器学习，提升3D打印质量预测精度
Physics-Informed and Hybrid Machine Learning in Additive Manufacturing: Application to Fused Filament Fabrication
- 在损失函数中加入物理约束，确保模型符合力学规律
- 结合仿真结果与实验数据，使孔隙率预测更可靠
- 小样本下仍可实现高精度预测，适合工业应用
本文研究了多种融合物理知识的机器学习策略，将物理规律嵌入基于实验数据的深度神经网络模型中，用于预测熔融沉积成型（FFF）零件的结合质量与孔隙率。探索了三种方法：(1) 在DNN损失函数中引入物理约束；(2) 将物理模型输出作为DNN输入；(3) 先用物理模型预训练DNN，再用实验数据微调。这些方法确保了结合质量与拉伸强度之间的物理解释一致性，使孔隙率预测具有物理意义。共测试了八种策略组合，结果显示，多种策略结合可在实验数据有限的情况下构建高精度模型。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。