STM Journals Peer-reviewed research from Consortium e-Learning Network

International Journal of Optical Innovations & Research Review Article

An AI-Driven IoT Framework for Autonomous Quality Assurance in Optical Lens Manufacturing

  1. Kazi Kutubuddin Sayyad Liyakat Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur (MS)

Abstract

The evolution of high-precision optics—ranging from smartphone micro-lenses to high-end astronomical glass—demands unprecedented accuracy in manufacturing. Traditional inspection methods, reliant on manual sampling or static automated optical inspection (AOI), often fail to bridge the gap between high-speed production and the detection of microscopic surface aberrations. This paper introduces an integrated architecture combining the Internet of Things (IoT) and Deep Learning-based decision-making systems to revolutionize lens quality control. By deploying an array of interconnected IoT sensors along the fabrication line, we capture high-fidelity spatial data and environmental telemetry in real-time. This stream is processed by a Convolutional Neural Network (CNN) embedded at the edge, capable of classifying optical defects—such as subsurface fractures, coating inconsistencies, and curvature deviations—with 99.4% accuracy. Furthermore, we implement a reinforcement learning (RL) feedback loop that autonomously adjusts CNC polishing parameters based on real-time sensor output, minimizing material waste and energy consumption. Our results demonstrate that this AI-IoT ecosystem not only reduces defect-related latency by 40% but also enables predictive maintenance, shifting the paradigm from reactive error correction to proactive, self-optimizing optical engineering.

Keywords

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