A recent research study by D3M Lab members has been published in the journal Results in Engineering (Elsevier).
The article, titled: “Comparative Analysis of Deep Learning Models for Defect Detection in Additive Manufacturing Using Thermal Imaging,” presents a comprehensive evaluation of multiple deep learning architectures for detecting defects in additive manufacturing processes using thermal imaging data. The study systematically compares model performance and highlights strengths and limitations across approaches, offering practical insights for improving quality control and reliability in advanced manufacturing systems.
This work reflects the D3M Lab’s continued focus on data-driven modeling, machine learning, and real-world decision-making applications, particularly at the intersection of AI and engineering systems.
DOI: https://doi.org/10.1016/j.rineng.2025.108359
The D3M Lab congratulates the authors on this publication and looks forward to building upon this research in future work on intelligent manufacturing and applied AI.
🔖 Citation:
Shah, Sapan, Dhruv Suraj, Sayed Mohsin Reza, Md Abdur Rahman Bin Abdus Salam, Ali Ashraf, and Shiekh Fahad Ferdous. “Comparative Analysis of Deep Learning Models for Defect Detection in Additive Manufacturing using Thermal Imaging.” Results in Engineering (2025): 108359. https://doi.org/10.1016/j.rineng.2025.108359