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With the wide application of fibre-reinforced composites in aerospace, the fatigue problem of composites is becoming more prominent. In order to achieve efficient and accurate fatigue damage analysis, a data-mechanism driven method for the progressive analysis of fatigue damage in composites is proposed, in which a single-hiddenlayer neural network as its fatigue constitutive law for simulations of fatigue delamination under cyclic loading. The Paris-law-informed regulation is used to achieve data-mechanism fusion for neural network model training. The ability to analyze fatigue delamination is validated in the full range of mode-Ⅰ and mode-Ⅱ as well as mixed modes of different mode ratios using double cantilever beam (DCB) and 4-point end flexure (4ENF). The applicability of the cohesive model in the case of complex fatigue delamination front is verified by using the reinforced double cantilever beam (R-DCB) model. The results show that the data-mechanism driven fatigue damage progressive analysis method for composites could rapidly and effectively simulate the composite delamination propagation with high fidelity, which can provide a new idea and method for composite structure design and safety assurance.

As composite materials are playing more important role in advanced aircraft structures,the change of mechanical properties of composites during service is of significant importance for the overall safety of the aircraft.In order to achieve the goal of fatigue evaluation and life prediction of composite components of aircraft based on guided wave in-situ detection,firstly,the fatigue evolution law of composite materials is studied from the perspectives of macroscopic phenomenology and microscopic physics.Then,the potential of guided wave phase velocity and mode conversion phenomenon for fatigue characterization is discussed through analyzing the guided wave field.At the same time,a deep learning framework is constructed to extract fatigue evolution features from the guided wave field in a data-driven manner.Finally,a fatigue evolution model based on the Bayesian model averaging method is proposed to predict the residual fatigue life of the composite specimen.The results show that,by extracting and analyzing the guided wave propagating features,the fatigue state of composite materials can be accurately characterized.Combining the Bayesian model averaging method and the confidence interval criterion,the goal of residual life prediction before specimen fatigue failure is achieved.

To investigate the local damage characteristics and properties of stiffness degradation in aluminum structures under vibration fatigue, an evaluation method of the fatigue property for local areas has been proposed by identifying the stiffness from the phase velocity obtained by the laser ultrasonic system. First, vibration fatigue tests were conducted on 2024‐O aluminum alloy components under four different stress levels. To address the issue of local damage characteristics under vibration fatigue, a scan window to achieve the local phase velocities within a small local region was employed, and then, the local stiffness can be calculated. By tracking the residual stiffness of the weakest region, the local damage factor was calculated. A unified model with normalized periods at different stress levels was carried out to predict the evolutionary trend. This approach offers a more efficient alternative to estimate local fatigue damage and residual life through the monitoring of local stiffness during maintenance. Highlights: A method for quantifying local damage induced by vibration fatigue is proposed.Local stiffness obtained from the phase velocity acquired by the laser ultrasonic system is used for fatigue assessment.The focus on the most vulnerable area is achieved using a local damage factor.The fatigue life of the specimen is predicted by a normalized model under different stress levels. [ABSTRACT FROM AUTHOR]

Employing the laser ultrasonic system, information on fatigue evolution can be captured and explicitly stored in the frequency‐wavenumber wavefield of the guided wave. This paper presents a deep‐learning architecture for processing the frequency‐wavenumber wavefield, which comprises two distinct steps: fatigue characterization and evolution prediction. Firstly, the fatigue characterization step employs a convolutional autoencoder (CAE) for compressing the frequency‐wavenumber wavefield and a fully connected network (FCN) for obtaining the fusion fatigue characteristic. Due to the high cost of experimental samples, extensive simulation‐generated wavefields are used to pre‐train the network. Subsequently, the fatigue evolution prediction model based on the latent ordinary differential equation (Latent‐ODE) is trained for the step‐by‐step prediction of fatigue evolution with a small amount of composite fatigue test data. The results validate the effectiveness of the deep‐learning architecture in characterizing fatigue and predicting its evolution, as well as the feasibility of the frequency‐wavenumber wavefield compression of guided wave. Highlights: The deep‐learning architecture for fatigue evaluation and prediction is proposed.The frequency‐wavenumber wavefield of guided wave is fused for fatigue evaluation.The fatigue evolution prediction is achieved using the Latent‐ODE model.The fatigue life of the composite specimen is predicted. [ABSTRACT FROM AUTHOR]

The thermal protection structures of supersonic vehicles are vulnerable to damage in the extreme environment of high temperature. With advantages of the large propagation range and high sensitivity to damage, guided waves show great potential for structural health monitoring. However, guided waves are susceptible to ambient temperature change, resulting in low reliability of damage detection results. Therefore, this paper proposed a multi-scale entropy feature extraction method for structural damage detection. Firstly, to eliminate the influence of multi-mode guided waves, a sliding window was used to extract the wave packet of different signals to analyze disturbance caused by damage and temperature. Then, an ant colony optimization algorithm was introduced as a feature fusion method to improve the classification performance. To evaluate the performance of features selected by the ant colony optimization algorithm, K -means clustering algorithm, and silhouette coefficients were utilized to calculate the evaluation function and represent the characteristics of damage. Finally, a set of guided wave signals from 20°C to 40°C were obtained to investigate the influence of temperature variation on the damage feature extraction. The results show that the multi-scale entropy method based on the sliding window can effectively extract damage features in the condition of temperature change. [ABSTRACT FROM AUTHOR]

Abstract Auxetic structures with negative Poisson's ratio (NPR) have been drawing an increasing attention due to the distinctive mechanical performance, i.e. expanding laterally when stretched axially or contracting when compressed. And the mechanical characteristic can be tailorable, which has vast prospects in large deformation structures. In this study, a novel auxetic structure, named reentrant anti-tetrachiral (RATC) structure, is designed by introducing a reentrant angle into the existing anti-chiral structure. The present structure can exhibit a significant improvement of NPR. In order to facilitate numerical analysis, ABAQUS and Python script are utilized to generate numerical models with different configurations automatically. Then effects of geometrical parameters on the elastic modulus, shear modulus, Poisson's ratio and relative area (the ratio between base material area and unit cell area) are numerically investigated. The simulation results show that the structural parameters have great influence on the mechanical performance. Finally, additive manufacturing technique is employed to fabricate samples of the proposed design, which are subjected to uniaxial quasi-static tensile tests and three-point bending tests. The mechanical characteristics are verified through both simulation and experiments. Overall, the improved mechanical performance of RATC is demonstrated, which provides guidelines for auxetic structure designs. [ABSTRACT FROM AUTHOR]