D. Suganthi

Papers

Total Citations

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About

D. Suganthi is a researcher at the forefront of integrating artificial intelligence with industrial and IoT systems, with a primary focus on predictive maintenance and intelligent automation. Her most impactful work, "AI-enhanced predictive maintenance in hybrid roll-to-roll manufacturing integrating multi-sensor data and self-supervised learning" (2024), has already garnered 16 citations, demonstrating its immediate relevance to the manufacturing sector. This study introduces a novel framework that leverages self-supervised learning to analyze multi-sensor data, enabling early fault detection and reducing downtime in complex production environments—a significant contribution to Industry 4.0. Suganthi also explores the intersection of deep learning and IoT in robotics, as seen in her work on traffic monitoring systems. Her research bridges the gap between theoretical AI models and practical, real-world applications, offering scalable solutions for smart manufacturing and urban infrastructure. With a growing citation footprint, Suganthi is establishing herself as a key voice in applied AI, particularly in hybrid manufacturing and sensor-driven automation.