Methodology for Online Estimation of Rheological Parameters in Polymer Melts Using Deep Learning and Microfluidics

TL;DR

This paper introduces a deep learning-based methodology that uses microfluidic device data to accurately and efficiently estimate rheological parameters of polymer melts online, enhancing device design and testing.

Contribution

It presents a novel integration of deep learning with microfluidic modeling for real-time viscosity estimation of polymer melts, reducing reliance on physical prototypes.

Findings

Deep learning model accurately predicts rheological parameters from microfluidic data.

Method improves estimation accuracy and flexibility over traditional techniques.

Reduces time and cost in microfluidic device development.

Abstract

Microfluidic devices are increasingly used in biological and chemical experiments due to their cost-effectiveness for rheological estimation in fluids. However, these devices often face challenges in terms of accuracy, size, and cost. This study presents a methodology, integrating deep learning, modeling and simulation to enhance the design of microfluidic systems, used to develop an innovative approach for viscosity measurement of polymer melts. We use synthetic data generated from the simulations to train a deep learning model, which then identifies rheological parameters of polymer melts from pressure drop and flow rate measurements in a microfluidic circuit, enabling online estimation of fluid properties. By improving the accuracy and flexibility of microfluidic rheological estimation, our methodology accelerates the design and testing of microfluidic devices, reducing reliance on physical prototypes, and offering significant contributions to the field.