Artificial Neural Network-based fatigue behavior prediction of metals and composite materials

This article presents a study devoted to predicting the fatigue behavior of two different materials: aluminum alloy AL-2024-T6 and glass fiber composite samples. The approach used in the study involves the use of artificial neural networks (ANNs) to develop accurate models for predicting the fatigue life of these materials at various skewness ratios (R). For the first case study, the S-N curve of tensile-tested AL-2024-T6 was predicted for different values of R using a few sets of data for learning. The model was then tested on the same values of R as the learning set, as well as on a different value of R (-0.4), to demonstrate the ability of the model to predict fatigue data under varying conditions. The results showed that the model was capable of accurately predicting the fatigue life of AL-2024-T6 for different values of R. For the second case study, the stiffness degradation of bending-tested glass fiber woven composite samples was predicted for different values of R using ANN. Different layups of composite samples were considered in this study. The model was trained on a few sets of data and tested on the same and different values of R, demonstrating the ability of the model to accurately predict stiffness degradation of composite samples under varying coefficients of asymmetry. The results of both case studies showed that ANN-based models can be effective in predicting the fatigue behavior of different materials tested using different methods under varying coefficients of asymmetry. These findings have practical implications for industries involved in the design and manufacturing of materials, particularly in the aerospace and automotive sectors, where fatigue behavior is critical to the structural integrity of components

Artificial Neural Network-based fatigue behavior prediction of metals and composite materials

This article presents a study devoted to predicting the fatigue behavior of two different materials: aluminum alloy AL-2024-T6 and glass fiber composite samples. The approach used in the study involves the use of artificial neural networks (ANNs) to develop accurate models for predicting the fatigue life of these materials at various skewness ratios (R). For the first case study, the S-N curve of tensile-tested AL-2024-T6 was predicted for different values of R using a few sets of data for learning. The model was then tested on the same values of R as the learning set, as well as on a different value of R (-0.4), to demonstrate the ability of the model to predict fatigue data under varying conditions. The results showed that the model was capable of accurately predicting the fatigue life of AL-2024-T6 for different values of R. For the second case study, the stiffness degradation of bending-tested glass fiber woven composite samples was predicted for different values of R using ANN. Different layups of composite samples were considered in this study. The model was trained on a few sets of data and tested on the same and different values of R, demonstrating the ability of the model to accurately predict stiffness degradation of composite samples under varying coefficients of asymmetry. The results of both case studies showed that ANN-based models can be effective in predicting the fatigue behavior of different materials tested using different methods under varying coefficients of asymmetry. These findings have practical implications for industries involved in the design and manufacturing of materials, particularly in the aerospace and automotive sectors, where fatigue behavior is critical to the structural integrity of components

Temperature Dependence of the Resonant Magnetoelectric Effect in Layered Heterostructures

The dependence of the resonant direct magnetoelectric effect on temperature is studied experimentally in planar composite structures. Samples of rectangular shapes with dimensions of 5 mm × 20 mm employed ferromagnetic layers of either an amorphous (metallic glass) alloy or nickel with a thickness of 20–200 μm and piezoelectric layers of single crystalline langatate material or lead zirconate titanate piezoelectric ceramics with a thickness of 500 μm. The temperature of the samples was varied in a range between 120 and 390 K by blowing a gaseous nitrogen stream around them. It is shown that the effective characteristics of the magnetoelectric effect—such as the mechanical resonance frequency fr, the quality factor Q and the magnitude of the magnetoelectric coefficient αE at the resonance frequency—are contingent on temperature. The interrelations between the temperature changes of the characteristics of the magnetoelectric effect and the temperature variations of the following material parameters—Young’s modulus Y, the acoustic quality factor of individual layers, the dielectric constant ε, the piezoelectric modulus d of the piezoelectric layer as well as the piezomagnetic coefficients λ(n) of the ferromagnetic layer—are established. The effect of temperature on the characteristics of the nonlinear magnetoelectric effect is observed for the first time. The results can be useful for designing magnetoelectric heterostructures with specified temperature characteristics, in particular, for the development of thermally stabilized magnetoelectric devices