Manufacturing of microstructures using a microfluidic device is a largely empirical effort due to the multi-physical nature of the fabrication process. As such, models are desired that will predict microstructure performance characteristics (e.g., size, porosity, and stiffness) based on known inputs, such as sheath and core fluid flow rates. Potentially more useful is the prospect of inputting desired performance characteristics into a design model to extract appropriate manufacturing parameters. In this study, we demonstrate that deep neural networks (DNNs) trained with sparse datasets augmented by synthetic data can produce accurate predictive and design models. For our predictive model with known sheath and core flow rates and bath solution percentage, calculated solid microfiber dimensions are shown to be greater than 95% accurate, with porosity and Young's modulus exhibiting greater than 90% accuracy for a majority of conditions. Likewise, the design model is able to recover sheath and core flow rates with 95% accuracy when provided values for microfiber dimensions, porosity, and Young's modulus. As a result, DNN-based modeling of the microfiber fabrication process demonstrates high potential for reducing time to manufacture of microstructures with desired characteristics.

翻译：微流控装置制造微结构的过程因涉及多物理场耦合而主要依赖经验操作。因此，亟需建立能够根据已知输入参数（如鞘流与芯流流速）预测微结构性能特征（如尺寸、孔隙率、刚度）的模型。更具应用前景的是，通过输入目标性能特征至设计模型，反向获取最优制造参数。本研究证明，采用稀疏数据集结合合成数据训练的深度神经网络可构建精确的预测与设计模型。对于已知鞘流/芯流流速及浴液浓度的预测模型，所计算固体微纤维尺寸准确率超过95%，在多数工况下孔隙率与杨氏模量准确率突破90%。同时，当输入微纤维尺寸、孔隙率及杨氏模量时，设计模型能以95%的准确率逆向获取鞘流与芯流流速。因此，基于深度神经网络的微纤维制造建模为解决具有特定性能要求的微结构快速制造难题提供了高潜力方案。