R12521/2bce1510a451master
README.md
Additive-Manufacturing-Self-Supervised-Learning-Coaxial-DED_Process-Zone-Imaging
Real-Time Monitoring and Quality Assurance for Laser-Based Directed Energy Deposition: Integrating Coaxial Imaging and Self-Supervised Deep Learning Framework
Journal link
Overview
Artificial Intelligence (AI) has emerged as a promising solution for real-time monitoring of the quality of additively manufactured (AM) metallic parts. This study focuses on the Laser-based Directed Energy Deposition (LDED) process and utilizes embedded vision systems to capture critical melt pool characteristics for continuous monitoring. Two self-learning frameworks based on Convolutional Neural Networks and Transformer architecture are applied to process zone images from different DED process regimes, enabling in-situ monitoring without ground truth information. The evaluation is based on a dataset of process zone images obtained during the deposition of titanium powder (Cp-Ti, grade 1), forming a cube geometry using four laser regimes. By training and evaluating the Deep Learning (DL) algorithms using a co-axially mounted CCD camera within the process zone, the down-sampled representations of process zone images are effectively used with conventional classifiers for L-DED process monitoring. The high classification accuracies achieved validate the feasibility and efficacy of self-learning strategies in real-time quality assessment of AM. This study highlights the potential of AI-based monitoring systems and selflearning algorithms in quantifying the quality of AM metallic parts during fabrication. The integration of embedded vision systems and self-learning algorithms presents a novel contribution, particularly in the context of the L-DED process. The findings open avenues for further research and development in AM process monitoring, emphasizing the importance of self-supervised in-situ monitoring techniques in ensuring part quality during fabrication. !Experimental
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Code
bash git clone https://github.com/vigneashpandiyan/Additive-Manufacturing-Self-Supervised-Learning-Coaxial-DED_Process-Zone-Imaging cd Additive-Manufacturing-Self-Supervised-Learning-Coaxial-DED_Process-Zone-Imaging python Transformer_Byol.py python Main_Byol.py python Main_CNN.py python Main_Transformer.py
Citation
@article{, title={}, author={}, journal={}, volume={}, pages={}, year={}, publisher={} }