Closed-Loop Control of Non-Newtonian Fluid Flow Using Machine Learning

Controlling the flow of non-Newtonian fluids is crucial for advancements in various applications including 3D printing. However, realizing this control necessitates overcoming substantial challenges due to the intricacies in real-time flow monitoring and manipulation. In this research, we introduce a novel methodology that amalgamates electrical flow monitoring with machine learning to meticulously control non-Newtonian fluid flow. We employ a miniaturized non-contact electrical flow monitoring technique based on flow triboelectricity. To realize high-speed, accurate, and real-time control of non-Newtonian flow, we implement a Radial Basis Function Neural Network (RBFNN). The RBFNN is meticulously trained to understand the absolute error and to adapt to the aspired reference rate of the non-Newtonian flow, operating in real-time at a frequency of 1 s^-1. Our machine-learning–enabled closed-loop control system demonstrates exceptional overlap fidelity with the predetermined reference flow rate and performs real-time adjustments to maintain the flow rate. This approach provides immense potential to augment the precision and dependability of various processes that involve non-Newtonian fluids, paving the way for enhanced reliability and accuracy in applications.