Enhanced detection of surface deformations in LPBF using deep convolutional neural networks and transfer learning from a porosity model

Abstract

Our previous research papers have shown the potential of deep-learning models for real-time detection and control of porosity defects in 3D printing, specifically in the laser powder bed fusion (LPBF) process. Extending these models to identify other defects like surface deformation poses a challenge due to the scarcity of available data. This study introduces the use of Transfer Learning (TL) to train models on limited data for high accuracy in detecting surface deformations, marking the first attempt to apply a model trained on one defect type to another. Our approach demonstrates the power of transfer learning in adapting a model known for porosity detection in LPBF to identify surface deformations with high accuracy (94%), matching the performance of the best existing models but with significantly less complexity. This results in faster training and evaluation, ideal for real-time systems with limited computing capabilities. We further employed Gradient-weighted Class Activation Mapping (Grad-CAM) to visualize the model’s decision-making, highlighting the areas influencing defect detection. This step is vital for developing a trustworthy model, showcasing the effectiveness of our approach in broadening the model’s applicability while ensuring reliability and efficiency.