Inevitable defects have an important impact on the fatigue lifetime of additive manufacturing materials. Therefore, it is critical to thoroughly characterize the characteristics of the defects, which requires effective semantic segmentation of the imaged defects. In this paper, a defect dataset for SLM Ti-6Al-4V alloy was obtained by synchrotron radiation computed tomography. Then a semantic segmentation method was developed based on the DeepLabV3 + network to automatically extract defects. Cropping and undersampling were introduced in the dataset pre-processing. A weighted scheme based on the ratio between the number of defect and matrix pixels was applied in the classification layer, and morphological operations were employed in image post-processing to improve the accuracy of identifying small-target defect. Finally, the above method was applied to segment the X-ray computed tomography data for two batches of SLM Ti-6Al-4V materials, and the defect segmentation results were used to predict the fatigue lifetime. The semantic segmentation method performs well with a pixel recognition accuracy of 98.2% for the test dataset, and the error in the predicted fatigue lifetime lies within the scatter band of ±2.2. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 2)'.
Keywords: deep convolutional neural networks; defect; fatigue lifetime; selective laser melting; semantic segmentation.