Surrogate modeling of acoustic field-assisted particle patterning process with physics-informed encoder–decoder approach

Manipulating the distribution of functional particles in a polymer matrix can enable the fabrication of multifunctional smart composite devices. Using an acoustic field for particle patterning is a promising technique to alleviate the need for electrically conductive particles or magnetically responsive particles. To better understand the acoustic particle patterning process, a 3D high-fidelity multiphysics model is generally utilized. However, thousands of forward simulations are often required to determine a suitable set of input parameters for a desired particle pattern. It is advantageous to replace the computationally expensive forward simulation model with a cheaper-to-evaluate surrogate model to optimize the acoustic particle patterning process. This work develops a physics-informed machine learning approach to build a surrogate model capable of predicting the acoustic pressure pattern, which is highly related to the particle pattern. The surrogate model has an encoder–decoder structure, and the model training uses simulation data generated from a 3D multiphysics model. The multiphysics model is validated against experimental data before the generation of the simulation data. Physical knowledge is incorporated into the encoder–decoder model through a physics-informed input derived from the output of a 2D multiphysics model. This 2D model is constructed based on a cut plane of the 3D model to preserve most of the acoustic pressure information from the complex 3D model while being more efficient to evaluate and suitable for online prediction. The proposed physics-informed encoder–decoder model can increase the quality of the acoustic pattern prediction by over 40% compared to the base encoder–decoder model. Incorporating the physics-informed input into the base encoder–decoder can significantly reduce the sample size and model complexity required for achieving a given acoustic pattern prediction accuracy. This work provides a guideline for developing physics-informed machine learning models for manufacturing processes.