Current microfluidic-based microencapsulation systems rely on human experts to monitor and oversee the entire process spanning hours in order to detect and rectify when defects are found. This results in high labor costs, degradation and loss of quality in the desired collected material, and damage to the physical device. We propose an automated monitoring and classification system based on deep learning techniques to train a model for image classification into four discrete states. Then we develop an actuation control system to regulate the flow of material based on the predicted states. Experimental results of the image classification model show class average recognition rate of 95.5%. In addition, simulated test runs of our valve control system verify its robustness and accuracy.
Conference Committee Involvement (3)
Applications of Machine Learning 2023
23 August 2023 | San Diego, California, United States
Applications of Machine Learning 2022
23 August 2022 | San Diego, California, United States
Applications of Machine Learning 2021
4 August 2021 | San Diego, California, United States
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