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

Journal of Polymer & Composites Original Research Special issue

ML-Driven Defect Detection in Additive Manufacturing of Polymer Composites Using Thermal Imaging

  1. Harish Reddy Gantla Department of Computer Science & Engineering, Vignan Institute of Technology and Science
  2. Vadakattu Prabhakar Department of Computer Science & Engineering, Sreenidhi Institute of Science and Technology
  3. Harish Chandra Mohanta Department of Electronics and Communication Engineering, Centurion University of Technology and Management
  4. A. Anandhan Department of Chemistry, Erode Sengunthar Engineering College, Thudupathi, Perundurai
  5. Parul Goyal Department of Computer Science & Engineering, M. M. Engineering College, Maharishi Markandeshwar Deemed to be University, Mullana, Ambala
  6. Radha Seelaboyina Department of Computer Science and Engineering, Geethanjali College of Engineering and Technology, Hyderabad

Abstract

Polymer-based flexible biosensors have emerged as a pivotal technology in continuous health monitoring, yet their deployment in real-world settings is often hindered by undetected micro-defects and signal distortion caused during fabrication or usage. Existing diagnostic frameworks typically rely on post-hoc processing or bulky instrumentation, failing to offer scalable, real-time detection during additive manufacturing workflows. This study introduces an end-to-end, thermographic imaging-integrated framework for in-situ defect identification during the additive manufacturing of polymer composites, guided by a lightweight convolutional neural network (CNN) architecture. The system fuses thermal signatures with structural cues to detect anomalies embedded within multilayer flexible substrates. A streamlined fabrication pipeline—including conductive polymer deposition, thermal data capture, and edge-based CNN classification—enables robust, near-instantaneous feedback during biosensor assembly. Experimental evaluations demonstrate that the proposed system achieves a classification accuracy of 96.4% with a latency reduction of 28.3% compared to traditional offline inspection methods. Signal fidelity under deformation stress conditions remains consistently above 92%, even in high-strain regions. This approach not only enhances the reliability and production yield of wearable biosensors but also sets a precedent for embedding explainable AI-driven quality control directly into smart manufacturing cycles—paving the way for self-validating, adaptive biomedical devices suited for the evolving landscape of personalized, IoT-enabled healthcare.

Keywords

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