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

Journal of Polymer & Composites Original Research Special issue

Deep Learning for Real-Time Monitoring and Defect Detection in Additive Manufactured Polymer Composites

  1. Dillip Kumar Mohanta Department of Mechanical Engineering, Centurion University of Technology and Management
  2. M Khaleel Ullah Khan Department of Electronics and Communication Engineering, Vignan Institute of Technology and Science, Hyderabad
  3. Subhajeet Das Department of Computer Science and Engineering – Artificial Intelligence, Brainware University
  4. Nitish Kumar Choudhary State Board of Technical Education, Patna
  5. Rahat Naz School of Computer Science, University of Petroleum and Energy Studies (UPES), Dehradun
  6. Vishwesh Nagamalla Department of Artificial Intelligence and Data Science, Sanjivani University

Abstract

Additives Fiber-reinforced polymer composite ADDs have high utility in making lightweight structural components, but due to process-related defects (interlayer delamination and reinforcement stacking) the integrity of consolidation during extrusion-based deposition is frequently compromised. This paper has presented a physics-informed deep learning framework that is applicable to real-time measurements of reinforced thermoplastic composite fabrication. Multimodal sensing was provided with thermal gradient, optical morphology, and acoustics emission signals being used to assess the interlayer bonding behavior at successive cycles of deposition. This approach explicitly captures the influence of melt rheology and interlayer diffusion on consolidation stability during composite layer formation. The proposed monitoring system achieved a defect detection accuracy of 91.8% with a reduced standard deviation of 1.7% across monitored layers. Under stable deposition conditions, the consolidation integrity index remained above 0.85, whereas disturbed extrusion parameters resulted in a decline to 0.38, corresponding to a 44% increase in defect probability. Comparative analysis demonstrated an accuracy improvement of 7.3% over conventional vision-based monitoring techniques. Furthermore, the proposed framework establishes a scalable pathway toward intelligent, process-adaptive quality control in polymer composite additive manufacturing systems. These results suggest that the use of polymer process-structure associations in a deep learning based monitoring framework can be used to increase sensitive consolidation anomalies in the fiber-reinforced thermoplastic systems to facilitate better real-time quality evaluation during additive manufacturing of polymer composite structures.

Keywords

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