conference papers
Peer-reviewed conference proceedings spanning SAMPE, the Solid Freeform Fabrication Symposium, IISE, and MARTEC.
Evaluation of deep learning architectures for defect detection in fused filament fabrication
Dola, I. S., Ahmed, R., Zulqernine, M. J., Shanto, T. A., & Taylor, R. M. SAMPE Conference and Exhibition, Seattle, WA, April 27–30, 2026.
YOLO, Mask R-CNN, and DeepLab compared for detecting and segmenting FFF defects: cracking, warping, stringing, off-platform errors, and layer shifting. YOLO gave the best object-level localization, Mask R-CNN better spatial interpretability, and DeepLab worked mainly for compact defects.
Machine learning-based prediction of toughness in fused filament fabrication: leveraging in-process annealing with enhanced printheads
Ahmed, R., Shanto, T. A., Rahman, M. M., Taylor, R. M., & Jain, A. Solid Freeform Fabrication 2025: Proceedings of the 36th Annual International Solid Freeform Fabrication Symposium, 1182–1200.
Forty-four thermal features from infrared thermography, reduced by PCA to 99.8% retained variance, used to predict toughness in annealed ABS. Artificial neural networks reached 86.7% balanced accuracy and 90.7% AUC, pointing toward non-destructive real-time quality assurance.
Leveraging large language models for process parameter optimization in 3D-printed ABS polymer specimens
Shanto, T. A., Pavel, H. R., Ahmed, R., Abdullah, M., & Taylor, R. M. IISE Annual Conference and Expo 2025, 1351–1356.
Four small language models (Phi-2, Qwen2.5-Math-1.5B, DeepSeek-R1-Distill-Qwen-1.5B, and StableLM-3B-4e1t) tested for few-shot prediction of tensile strength from nozzle type, extrusion width, and print speed. Phi-2 performed best, suggesting a route for parameter optimization where data is too scarce for conventional design of experiments.
Predicting mechanical strength in FDM printed ABS parts with in-process annealing: a machine learning approach
Shanto, T. A., Shahriar, M. A., Ahmed, T., Zulqernine, M. J., & Taylor, R. M. IISE Annual Conference and Expo 2025, 1224–1229.
ASTM D638 Type IV specimens printed with a patent-pending heater block against a conventional brass nozzle, varying nozzle type, print speed, and part spacing. Nozzle type dominated ultimate tensile strength, and Random Forest gave the most accurate prediction among the regressors tested.
A statistical approach for evaluating printing temperature and material flowrate effects on lightweight polylactic acid in fused filament fabrication
Ahmed, S., Rahman, M. M., Shanto, T. A., Ahmed, R., & Taylor, R. M. IISE Annual Conference Proceedings 2026 (Abstract ID 17263).
A full-factorial study of printing temperature and flowrate on lightweight PLA, with modulus of toughness as the response. Both main effects and their interaction proved statistically significant, addressing the coupled effect of temperature-dependent foaming and flowrate.
In press.
A statistical study on the influence of build-volume temperature and bead overlapping of PEI in fused filament fabrication
Rahman, M. M., Ahmed, S., Shanto, T. A., Ahmed, R., & Taylor, R. M. IISE Annual Conference Proceedings 2026 (Abstract ID 16940).
ULTEM™ 9085 printed across build-volume temperatures of 80, 100, and 120 °C and infill overlaps of 0, 10, and 16.67%. Both factors strongly influence part quality and strength, giving practical settings for high-performance polymer printing.
In press.
Genetic algorithms in order-picking route optimization: a review of advances and implications for logistics
Abdullah, M., Ozay, D., Ahmed, S. M. T., Shanto, T. A., & Sridhar, E. P. IISE Annual Conference and Expo 2025, 1375–1380.
A PRISMA systematic review across ProQuest, Web of Science, and ScienceDirect, narrowing 30 full-text papers to the 10 most cited. Genetic algorithms proved flexible across routing conditions and outperformed traditional methods in warehouse order picking.
Application of fused filament fabrication in the marine sector: from rapid prototyping to final product
Ahmed, R., Niloy, R. S., Mozumder, M. R., & Shanto, T. A. Proceedings of MARTEC 2024, Johor Bahru, Malaysia, 24–26 September 2024.
A review of where FFF has moved from prototyping toward finished marine components, the limitations still constraining it, and the research directions that would close them.
An investigation on the applications of additive manufacturing in the marine industry
Zulqernine, M. J., Alam, M. A., Uddin, M. R., Dola, I. S., & Shanto, T. A. Proceedings of MARTEC 2024, Johor Bahru, Malaysia, 24–26 September 2024.
Metal and polymer AM surveyed across ship structural components, machinery, propeller blades, on-board repair, and boat manufacturing. Sustainability, production capacity, and part reliability emerge as the principal barriers to adoption.