J-Integral Experimental Reduction Reveals Fracture Toughness Improvements in Thin-Ply Carbon Fiber Laminates with Aligned Carbon Nanotube Interlaminar Reinforcement.

The Mode I, Mode II, and mixed-mode interlaminar failure behavior of a thin-ply (54 gsm) carbon fiber-epoxy laminated composite reinforced by 20 µm tall z-direction-aligned carbon nanotubes (CNTs), comprising ∼50 billion CNT fibers per cm2, is analyzed following J-integral-based data reduction methods. The inclusion of aligned CNTs in the ply interfaces provides enhanced crack resistance, resulting in sustained crack deflection from the reinforced interlaminar region to the intralaminar region of the adjacent plies, i.e., the CNTs drive the crack from the interlaminar region into the plies. The CNTs do not appreciably increase the interlaminar thickness or laminate weight and preserve the intralaminar microfiber morphology. Improvements of 34 and 62% on the Mode I and Mode II initiation fracture toughness, respectively, are observed. This type of interlaminar nanoreinforcement effectively drives crack propagation from the interface to within the ply where the crack propagates parallel to the interlaminar region, providing new insight into previously reported strength and fatigue performance increases. These findings extend to industries where lightweight and durable materials are critical for improving the structural efficiency.

Deep Learning Unlocks X-ray Microtomography Segmentation of Multiclass Microdamage in Heterogeneous Materials.

Four-dimensional quantitative characterization of heterogeneous materials using in situ synchrotron radiation computed tomography can reveal 3D sub-micrometer features, particularly damage, evolving under load, leading to improved materials. However, dataset size and complexity increasingly require time-intensive and subjective semi-automatic segmentations. Here, the first deep learning (DL) convolutional neural network (CNN) segmentation of multiclass microscale damage in heterogeneous bulk materials is presented, teaching on advanced aerospace-grade composite damage using ≈65 000 (trained) human-segmented tomograms. The trained CNN machine segments complex and sparse (<<1% of volume) composite damage classes to ≈99.99% agreement, unlocking both objectivity and efficiency, with nearly 100% of the human time eliminated, which traditional rule-based algorithms do not approach. The trained machine is found to perform as well or better than the human due to "machine-discovered" human segmentation error, with machine improvements manifesting primarily as new damage discovery and segmentation augmentation/extension in artifact-rich tomograms. Interrogating a high-level network hyperparametric space on two material configurations, DL is found to be a disruptive approach to quantitative structure-property characterization, enabling high-throughput knowledge creation (accelerated by two orders of magnitude) via generalizable, ultrahigh-resolution feature segmentation.