Development of an AI Based Failure Predictor Model to Reduce Filament Waste for a Sustainable 3D Printing Process

This paper delves into the integration of motion tracking technology for real-time monitoring in 3D printing, with a focus on the popular fused filament fabrication (FFF) technique. Despite FFF's cost-efficiency, prevalent printing errors pose significant challenges to its commercial and environmental viability. This study proposes a solution by incorporating motion tracking nodes into the 3D printing process, tracked by cameras, enabling dynamic identification and rectification of printing failures. Addressing key research questions, the paper explores the applicability of motion tracking for failure detection, its impact on printed object quality, and the potential reduction in 3D printing waste. The proposed real-time monitoring system aims to fill a critical gap in existing 3D printing procedures, providing dynamic failure identification. The study integrates machine learning, computer vision, and motion tracking technologies, employing an inductive theoretical development strategy with active learning iterations for continuous improvement. Highlighting the revolutionary potential of 3D printing and acknowledging challenges in continuous monitoring and waste management, the suggested system pioneers real-time monitoring, striving to enhance efficiency, sustainability, and adaptability to diverse production demands. As the study progresses into implementation, it aspires to contribute significantly to the evolution of 3D printing technologies.

@article{Mohammadian2024,
  title     = {Development of an AI Based Failure Predictor Model to Reduce Filament Waste for a Sustainable 3D Printing Process},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {10},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Noushin Mohammadian and Melissa Sofía Molina Silva and Giorgi Basiladze and Omid Fatahi Valilai},
  doi       = {10.14569/IJACSA.2024.0151001},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151001}
}