Background: Obtaining reliable/consistent electrospun fibers is crucial for the electrospinning process since this will improve the product performance (e.g., scaffolds, high-performance filters, etc.). The process variations are influenced by process parameters (e.g., voltage, solution feeding rate, needle diameter, etc.), material parameters (e.g., density, viscosity, etc.), and ambient conditions (e.g., humidity, temperature, etc.). Having mechanisms to understand and detect these variations are important for the process stability, reliability, and repeatability. Data-driven approaches have demonstrated important progress at improving manufacturing processes but they lack the physics principles that govern the manufacturing phenomenon (e.g., electro-hydrodynamics in electrospinning). On the other hand, physics-based models can be utilized to encode the physics, but they are computationally inefficient for real time applications. Hence, integrating these two becomes paramount to improve the model performance for anomalies detection in electrospinning.

Research Objectives: The objective of this project is to develop a framework to integrate electro-hydrodynamics models and in-situ data to have accurate predictions (e.g., Taylor cone, jet, whipping, etc. behaviors) in electrospinning (see Fig. 1) and relate them to the quality of the products (e.g., fiber alignment, diameter distribution, etc.).  

Figure 1: A Scheme of the Electrospinning Process: (a) Electrospinning Setup and (b) Stable Regimen

Research Plan: (1) Strategic sensor-equip of electrospinning setup for experimental data collection, (2) construct physics-based (CFD) models for electrospinning simulated data collection, (3) design patterned dielectric substrates to build flexible substrates for flexible electronics applications, and (4) product characterizations (e.g., SEM images, tensile test, etc.).  

REU Student Outcomes: This project fits a student with coding skills (e.g., computer science). REU student will gain hands-on experience in advanced manufacturing technology (e.g., electrospinning and additive manufacturing). Additionally, the students will have exposure to state-of-the-art machine learning, deep learning, etc. Student will be supervised by Dr. Luis Segura (Industrial Engineering) and participate in writing conference and journal papers.

References: 

[1] Segura, L. J., Muñoz, C. N., Zhou, C., & Sun, H. (2020, September). Sketch-based tensor decomposition for non-parametric monitoring of electrospinning processes. In International Manufacturing Science and Engineering Conference (Vol. 84263, p. V002T09A002). American Society of Mechanical Engineers.

[2] Toscano, J. D., Li, Z., Segura, L. J., & Sun, H. (2020, September). A machine learning approach to model the electrospinning process of biocompatible materials. In International Manufacturing Science and Engineering Conference (Vol. 84263, p. V002T06A031). American Society of Mechanical Engineers.