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This paper investigates physics-informed and hybrid machine learning strategies to predict bond quality and porosity in fused filament fabrication, showing accurate models even with limited experimental data.
Introduces PI-Splines, a structured spline-based architecture for physics-informed learning that parametrizes unknown fields with trainable B-spline coefficients, providing compact support, analytical derivatives, and strong boundary condition enforcement, demonstrated as a competitive alternative to neural network-based methods.