Surrogate modeling of acoustic field-assisted particle patterning process with physics-informed encoder–decoder approach
Surrogate modeling of acoustic field-assisted particle patterning process with physics-informed encoder–decoder approach
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DOI：
10.1007/s00158-022-03411-w
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发表时间：
2022-11
影响因子：
3.9
通讯作者：
Y. Lui;M. Shahriar;Yayue Pan;Chao Hu;Shan Hu
中科院分区：
文献类型：
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作者：
Y. Lui;M. Shahriar;Yayue Pan;Chao Hu;Shan Hu
控制功能粒子在聚合物基体中的分布可以实现多功能智能复合材料器件的制造。使用声场进行颗粒图案化是一种有前途的技术，以减轻对导电颗粒或磁响应颗粒的需求。为了更好地理解声学粒子图案化过程，通常利用3D高保真多物理场模型。然而，通常需要数千个正演模拟来确定用于期望的粒子图案的合适的输入参数集。有利的是，用更便宜的评估代理模型来代替计算上昂贵的前向仿真模型，以优化声学颗粒图案化过程。这项工作开发了一种基于物理学的机器学习方法，以建立一个能够预测声压模式的代理模型，该模型与颗粒模式高度相关。代理模型具有编码器-解码器结构，并且模型训练使用从3D多物理场模型生成的仿真数据。在生成模拟数据之前，针对实验数据对多物理场模型进行了验证。物理知识通过从2D多物理场模型的输出导出的物理信息输入被并入编码器-解码器模型。该2D模型基于3D模型的切割平面来构建，以保留来自复杂3D模型的大部分声压信息，同时更有效地评估并适合于在线预测。与基本编码器-解码器模型相比，所提出的基于物理信息的编码器-解码器模型可以将声学模式预测的质量提高40%以上。将物理信息输入到基本编码器-解码器中可以显著地减小实现给定声学模式预测精度所需的样本大小和模型复杂度。这项工作为开发用于制造过程的物理信息机器学习模型提供了指导。
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.