Flexible Mechanoluminescent SrAl2O4:Eu Film with Tracking Performance of CFRP Fracture Phenomena

Non-destructive testing of carbon-fiber-reinforced plastic (CFRP) laminates with bidirectional fiber bundles (twill-weave) using a mechanoluminescence (ML) technique was proposed. The dynamic strain distributions and fracture phenomena of the CFRP laminates in the tensile testing were evaluated by the fabricated ML sensor consisting of SrAl₂O₄:Eu (SAOE) powder and epoxy resin. The ML images for the ML sensor attached to the CFRP laminates with bidirectional fiber bundles gave a net-like ML intensity distribution similar to the original twill weave pattern. Specifically, it was found that the ML intensity on the longitudinal fiber bundle, which is the same as the tensile direction, is higher than that on the transverse fiber bundle. This indicates that the ML sensor can visualize the load share between fiber bundles in different directions of the CFRP laminate with high spatial resolution. Meanwhile, the ML sensor could also visualize the ultrafast discontinuous fracture process of the CFRP laminates and its stress distribution. The amount of SAOE powder in the ML sensor affects the tracking performance of the crack propagation. A higher SAOE amount leads to a fracture of the ML sensor itself, and a lower SAOE amount leads to poor ML characteristics.

Structural similarity analysis of midfacial fractures-a feasibility study

The structural similarity index metric is used to measure the similarity between two images. The aim here was to study the feasibility of this metric to measure the structural similarity and fracture characteristics of midfacial fractures in computed tomography (CT) datasets following radiation dose reduction, iterative reconstruction (IR) and deep learning reconstruction. Zygomaticomaxillary fractures were inflicted on four human cadaver specimen and scanned with standard and low dose CT protocols. Datasets were reconstructed using varying strengths of IR and the subsequently applying the PixelShine™ deep learning algorithm as post processing. Individual small and non-dislocated fractures were selected for the data analysis. After attenuating the osseous anatomy of interest, registration was performed to superimpose the datasets and subsequently to measure by structural image quality. Changes to the fracture characteristics were measured by comparing each fracture to the mirrored contralateral anatomy. Twelve fracture locations were included in the data analysis. The most structural image quality changes occurred with radiation dose reduction (0.980036±0.011904), whilst the effects of IR strength (0.995399±0.001059) and the deep learning algorithm (0.999996±0.000002) were small. Radiation dose reduction and IR strength tended to affect the fracture characteristics. Both the structural image quality and fracture characteristics were not affected by the use of the deep learning algorithm. In conclusion, evidence is provided for the feasibility of using the structural similarity index metric for the analysis of structural image quality and fracture characteristics.