Ansari Muhammad Ayub, Crampton Andrew, Parkinson Simon
School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK.
Materials (Basel). 2022 Oct 14;15(20):7166. doi: 10.3390/ma15207166.
Surface deformation is a multi-factor, laser powder-bed fusion (LPBF) defect that cannot be avoided entirely using current monitoring systems. Distortion and warping, if left unchecked, can compromise the mechanical and physical properties resulting in a build with an undesired geometry. Increasing dwell time, pre-heating the substrate, and selecting appropriate values for the printing parameters are common ways to combat surface deformation. However, the absence of real-time detection and correction of surface deformation is a crucial LPBF problem. In this work, we propose a novel approach to identifying surface deformation problems from powder-bed images in real time by employing a convolutional neural network-based solution. Identifying surface deformation from powder-bed images is a significant step toward real-time monitoring of LPBF. Thirteen bars, with overhangs, were printed to simulate surface deformation defects naturally. The carefully chosen geometric design overcomes problems relating to unlabelled data by providing both normal and defective examples for the model to train. To improve the quality and robustness of the model, we employed several deep learning techniques such as data augmentation and various model evaluation criteria. Our model is 99% accurate in identifying the surface distortion from powder-bed images.
表面变形是一种多因素导致的激光粉末床熔融（LPBF）缺陷，使用当前的监测系统无法完全避免。如果不加以控制，变形和翘曲会损害机械和物理性能，导致构建出的部件具有不理想的几何形状。增加保压时间、对基板进行预热以及为打印参数选择合适的值，是应对表面变形的常见方法。然而，缺乏对表面变形的实时检测和校正，是LPBF的一个关键问题。在这项工作中，我们提出了一种新颖的方法，通过采用基于卷积神经网络的解决方案，从粉末床图像中实时识别表面变形问题。从粉末床图像中识别表面变形是迈向LPBF实时监测的重要一步。打印了13根带有悬垂结构的棒材，以自然地模拟表面变形缺陷。精心选择的几何设计通过为模型训练提供正常和有缺陷的示例，克服了与未标记数据相关的问题。为了提高模型的质量和鲁棒性，我们采用了多种深度学习技术，如数据增强和各种模型评估标准。我们的模型在从粉末床图像中识别表面变形方面的准确率为99%。
