Abstract:

Given the lack of generic assessments for the position and shape behavior of particles under acoustic levitation, a system was conceived to study these behaviors using both classical and advanced AI-based methods. Two acoustic levitators were designed and successfully levitated particles, while a camera-based visual system was developed to capture their movement. A classical image pixel filter-based particle tracking system and an aspect ratio-based shape analysis system were created. This system was validated using an Artificial Neural Network (ANN)-based shape classification algorithm and a YOLO v4-enabled position tracking system. The results showed nearly 100% accuracy in particle motion tracking with the classical method, and 90% accuracy in particle detection through YOLO-v4. The shape analysis system, utilizing the ANN, achieved highly accurate tracking of particle orientation and aspect ratio, ensuring precise behavior monitoring. This integration with the levitation control system provided real-time feedback, allowing for enhanced control and manipulation of the levitated particles, with the ability to analyze particle dynamics and ensure stable levitation.

About the Speaker:

Vinay Adithya Subrahmanya is a Mechatronics Engineer with extensive expertise in automation, AI-driven solutions, and industrial applications. He is currently engaged in cutting-edge research involving acoustic levitation and particle behavior analysis, combining classical image processing and AI techniques to develop novel systems for particle tracking and shape analysis. Vinay has previously worked as a Territory Account Manager at MathWorks, where he played a pivotal role in driving technology adoption and strategic sales for automation and control solutions. With a solid background in engineering, business development, and project management, Vinay’s work focuses on creating innovative solutions to complex problems in manufacturing, automation, and smart systems. He is also passionate about exploring AI applications in real-world engineering challenges, contributing to advancements in smart factory systems and material handling.