This paper proposes a methodology for automated assessment of fatigue damage, which has been tested and validated with polycrystalline-alloy (Aℓ7075-T6) specimens on an experimental apparatus. Based on an ensemble of time series of ultrasonic test (UT) data, the proposed procedure is found to be capable of detecting fatigue-damage (at an early stage) in mechanical structures, which is followed by online evaluation of the associated risk. The underlying concept is built upon two neural network (NN)-based models, where the first NN model identifies the feature of the UT data belonging to one of the two classes: undamaged structure and damaged structure, and the second NN model further classifies an identified damaged structure into three classes: low-risk, medium-risk, and high-risk. The input information to the second NN model is the crack tip opening displacement (CTOD), which is computed by the first NN model via linear regression from an ensemble of optical data, acquired from the experiments. Both NN models have been trained by using scaled conjugate gradient algorithms. The results show that the first NN model classifies the energy of UT signals with (up to) 98.5% accuracy, and that the accuracy of the second NN model is 94.6%.

Forecasting and detection of fatigue cracks play a key role in damage mitigation of mechanical structures (e.g., those made of polycrystalline alloys) to enhance their service life, and ultrasonic testing (UT) has emerged as a powerful tool for detection of fatigue cracks at early stages of damage evolution. Along this line, the work reported in this paper aims to improve the performance of fatigue crack forecasting and detection based on a synergistic combination of discrete wavelet transform (DWT) and Hilbert transform (HT) of UT data, collected from a computer-instrumented and computer-controlled fatigue-testing apparatus. Performance of the proposed method is evaluated by comparison with the images generated from a digital microscope, which are treated as the ground truth in this paper. The results of comparison reveal that forthcoming fatigue cracks can be detected ahead of their appearance on the surface of test specimens. The proposed method apparently outperforms both HT and conventional DWT, when they are applied individually, because the synergistic combination of DWT and HT provides a better characterization of UT signal attenuation for detection of fatigue crack damage.