A Physics Enforced Neural Network to Predict Polymer Melt Viscosity

Abstract

Achieving superior polymeric components through additive manufacturing (AM) relies on precise control of rheology. One key rheological property particularly relevant to AM is melt viscosity ($\eta$). Melt viscosity is influenced by polymer chemistry, molecular weight ($M_w$), polydispersity, induced shear rate ($\dot\gamma$), and processing temperature ($T$). The relationship of $\eta$ with $M_w$, $\dot\gamma$, and $T$ may be captured by parameterized equations. Several physical experiments are required to fit the parameters, so predicting $\eta$ of a new polymer material in unexplored physical domains is a laborious process. Here, we develop a Physics-Enforced Neural Network (PENN) model that predicts the empirical parameters and encodes the aforementioned equations to calculate $\eta$ as a function of polymer chemistry, $M_w$, polydispersity, $\dot\gamma$, and $T$. We benchmark our PENN against physics-unaware Artificial Neural Network (ANN) and Gaussian Process Regression (GPR) models. Finally, we demonstrate that the PENN offers superior values of $\eta$ when extrapolating to unseen values of $M_w$, $\dot\gamma$, and $T$ for sparsely seen polymers.