TY - JOUR
T1 - Neural network-based automated assessment of fatigue damage in mechanical structures
AU - Alqahtani, Hassan
AU - Ray, Asok
N1 - Funding Information:
Acknowledgments: The first author gratefully acknowledges the financial support of the Saudi Arabian Cultural Mission (SACM). The work reported in this paper has been supported in part by the U.S. Air Force Office of Scientific Research (AFOSR) under Grant No. FA9550-15-1-0400. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the sponsoring agencies.
Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2020
Y1 - 2020
N2 - 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%.
AB - 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%.
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U2 - 10.3390/machines8040085
DO - 10.3390/machines8040085
M3 - Article
AN - SCOPUS:85097867165
SN - 2075-1702
VL - 8
SP - 1
EP - 19
JO - Machines
JF - Machines
IS - 4
M1 - 85
ER -