Ansari Muhammad Ayub, Crampton Andrew, Mubarak Samer Mohammed Jaber
School of Computing and Engineering, University of Huddersfield, Huddersfield, HD1 3DH, UK.
University of Baghdad, Baghdad, 10071, Iraq.
Sci Rep. 2024 Nov 6;14(1):26920. doi: 10.1038/s41598-024-76445-3.
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.
我们之前的研究论文已经展示了深度学习模型在3D打印中实时检测和控制孔隙缺陷的潜力，特别是在激光粉末床熔融（LPBF）工艺中。由于可用数据稀缺，将这些模型扩展到识别其他缺陷（如表面变形）面临挑战。本研究引入迁移学习（TL），以便在有限数据上训练模型，从而在检测表面变形时实现高精度，这标志着首次尝试将针对一种缺陷类型训练的模型应用于另一种缺陷类型。我们的方法展示了迁移学习的强大作用，即让一个在LPBF孔隙检测方面知名的模型能够高精度（94%）地识别表面变形，其性能与现有最佳模型相当，但复杂度显著降低。这使得训练和评估速度更快，对于计算能力有限的实时系统来说非常理想。我们还采用了梯度加权类激活映射（Grad-CAM）来可视化模型的决策过程，突出影响缺陷检测的区域。这一步骤对于开发一个值得信赖的模型至关重要，展示了我们的方法在扩大模型适用性的同时确保可靠性和效率方面的有效性。
