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

Notice

This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 04 | 01 | Page :
    By

    Kazi Kutubuddin Sayyad Liyakat,

  1. Professor and Head, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur (MS), India

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: AI Driven IoT, Optical lens Manufacturing, Quality Assurance, KSK Approach, IoT, Cloud

How to cite this article:
Kazi Kutubuddin Sayyad Liyakat. An AI-Driven IoT Framework for Autonomous Quality Assurance in Optical Lens Manufacturing. International Journal of Optical Innovations & Research. 2026; 04(01):-.
How to cite this URL:
Kazi Kutubuddin Sayyad Liyakat. An AI-Driven IoT Framework for Autonomous Quality Assurance in Optical Lens Manufacturing. International Journal of Optical Innovations & Research. 2026; 04(01):-. Available from: https://journals.stmjournals.com/ijoir/article=2026/view=245115


References

  1. Tamboli ANA, Pathan MU, Kuchbal LM, Halle SS, Shaikh SI. KSK approach in decision making: AI-driven IoT based system. International Journal of Advanced Research in Science, Communication and Technology. 2026;6(1):803-816. doi: 10.48175/IJARSCT-31179
  2. Almazmomi NK. Artificial intelligence-driven blockchain and Internet of Things framework for secure data management in precision agriculture. Journal of High Speed Networks. 2025 Aug;31(3):183-201.
  3. Behzadipour F, Ghasemi Nezhad Raeini M, Abdanan Mehdizadeh S, Taki M, Khalil Moghadam B, Zare Bavani MR, Lloret J. A smart IoT-based irrigation system design using AI and prediction model. Neural computing and applications. 2023 Dec;35(35):24843-57.
  4. Blessy JA. Smart irrigation system techniques using artificial intelligence and IoT. In2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV) 2021 Feb 4 (pp. 1355-1359). IEEE.
  5. Lawal Y. AI and IoT-Based Smart Irrigation: A Review of Challenges and Future Trends. International Congresses of Turkish Science and Technology Publishing. 2025 May 29:770-6.
  6. Rankhamb DD, Raut SR, Velapure AS. Smart agriculturing based on KSK approach: a novel AI-driven-IoT (AIIoT) based decision making approach. International Journal of Advanced Research in Science, Communication and Technology. 2024;4(1):347. doi: 10.48175/IJARSCT-19764
  7. AlZubi AA, Galyna K. Artificial intelligence and internet of things for sustainable farming and smart agriculture. IEEE access. 2023 Jul 24;11:78686-92.
  8. Singh AK. Smart farming: Applications of IoT in agriculture. InHandbook of Smart Materials, Technologies, and Devices: Applications of Industry 4.0 2022 Nov 10 (pp. 1655-1687). Cham: Springer International Publishing.
  9. Yadav M, Preety, Saxena E, Das A. Smart Agriculture System Using Artificial Intelligence and Internet of Things. Reshaping Intelligent Business and Industry: Convergence of AI and IoT at the Cutting Edge. 2024 Oct 15:403-18.
  10. S. B. Khadake, P. S. More, R. J. Shinde, K. P. Kondubhairi and S. S. Kamble, (2025). AI-Driven IoT based Decision Making for Hepatitis Diseases Patient’s Healthcare Monitoring: KSK Approach for Hepatitis Patient Monitoring, 2025 7th International Conference on Intelligent Sustainable Systems (ICISS), India, 2025, pp. 256-263, doi: 10.1109/ICISS63372.2025.11076213.
  11. Maingi A, Patel R. AI-IOT for sustainable farming decisions. InPerspectives on Artificial Intelligence and Internet of Things for Sustainable Environment 2026 Jan 1 (pp. 203-223). Elsevier.
  12. Mat I, Kassim MR, Harun AN, Yusoff IM. Smart agriculture using internet of things. In2018 IEEE conference on open systems (ICOS) 2018 Nov 21 (pp. 54-59). IEEE.
Ahead of Print Subscription Review Article
Volume 04
01
Received 18/05/2026
Accepted 21/05/2026
Published 26/05/2026
Publication Time 8 Days
1

Login


My IP

PlumX Metrics