Automated Segmentation of In Situ X-ray Microtomography of Progressive Damage in Advanced Composites via Deep Learning

摘要

We present here the development and evaluation of a deep learning (artificial intelligence)-based computer vision machine to automate segmentation of multiclass progressive matrix damage across micro and mesoscales in aerospace-grade advanced composite laminates visualized in 4D via nondestructive in situ mechanical testing coupled with synchrotron radiation computed tomography (SRCT). Leveraging tens of thousands of manually-/human-annotated SRCT tomograms (i.e., 2D virtual cross-sectional slices) encompassing two different aerospace-grade advanced composite laminate systems (standard-thickness-ply and thin-ply) that were SRCT-scanned while under progressive tensile loading, we teach a fully convolutional neural network machine to segment complex polymer matrix damage mechanisms according to their host ply, replacing～10 hours of trained human labor per scan segmentation (～2000 tomograms per scan) with negligible time to configure the trained machine data-processing pipeline. Evaluating qualitatively and quantitatively the segmented tomograms independently in 2D, as well as collectively in 3D scans, we demonstrate good agreement between the state-of-the-art human-based region growing (semi-manual) method and machine-based segmentation results, summarized by test set macro-averages of the following common classification/segmentation performance metrics: 79% for F_1 score (harmonic mean of precision and recall) and 67% for intersection over union (IoU) score. Moreover, 2D inspection of segmented damage within tomograms reveals that F_1 and IoU scores actually underrate machine performance due to a nontrivial degree of human (used as ground truth) segmentation error, as the machine is found to regularly exceed the human (resulting in F_1 and IoU score penalties) by discovering new damage instances, augmenting existing diffuse segmentations, and extending segmentations to image artifact-prone specimen edges. Consequently, we discover that deep learning-based segmentation successfully and efficiently characterizes sparse (＜＜1% of scan volume), extremely complex 3D damage states within SRCT datasets, resolving an intractable computer vision challenge (as viewed through the lens of traditionally programmed automation) and establishing these high-throughput tools as promising candidates to accelerate understanding of basic structure-property relationships in traditional and next-generation advanced composite materials.