A New Stored Energy Model Based on Plastic Work of Back Stress during Cyclic Loading in Polycrystalline Metal.

Two mesomechanics models were analyzed in an attempt to reveal the relationship between stored energy and back stress. It has been indicated that the portion of elastic stored energy due to residual microstresses () is closely related to intergranular back stress (), and the stored energy of dislocations inside grains () can be estimated with the plastic work of intragranular back stress (). Then, the evolution of back stress during cyclic loading was studied, and the plastic work of back stress () was calculated with the low cycle fatigue experimental data of Ti-6Al-4V.

Deep Learning Approach for Damage Classification Based on Acoustic Emission Data in Composite Materials.

Damage detection and the classification of carbon fiber-reinforced composites using non-destructive testing (NDT) techniques are of great importance. This paper applies an acoustic emission (AE) technique to obtain AE data from three tensile damage tests determining fiber breakage, matrix cracking, and delamination. This article proposes a deep learning approach that combines a state-of-the-art deep learning technique for time series classification: the InceptionTime model with acoustic emission data for damage classification in composite materials.

Study on Delamination Damage of CFRP Laminates Based on Acoustic Emission and Micro Visualization.

This study investigated the mechanism of delamination damage in the double cantilever beam (DCB) standard test by the use of the strain energy release rate. The curve of the strain energy release rate was verified by the Rise Angle () method. For this purpose, 24-layer carbon fiber/epoxy multidirectional laminates with interface orientations of 0°, 30°, 45°, and 60° were fabricated according to the standard ASTM D5528.