Neural Network-Based Automated Assessment of Fatigue Damage in Mechanical Structures
This paper proposes a methodology for automated assessment of fatigue damage, which has been tested and validated with polycrystalline-alloy (A<i>ℓ</i>7075-T6) specimens on an experimental apparatus. Based on an ensemble of time series of ultrasonic test (UT) data, the proposed procedure...
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-12-01
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| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/8/4/85 |
| _version_ | 1797544497882595328 |
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| author | Hassan Alqahtani Asok Ray |
| author_facet | Hassan Alqahtani Asok Ray |
| author_sort | Hassan Alqahtani |
| collection | DOAJ |
| description | This paper proposes a methodology for automated assessment of fatigue damage, which has been tested and validated with polycrystalline-alloy (A<i>ℓ</i>7075-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%. |
| first_indexed | 2024-03-10T14:02:21Z |
| format | Article |
| id | doaj.art-c5b51ec5238e4702b1404740f90e8425 |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-10T14:02:21Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-c5b51ec5238e4702b1404740f90e84252023-11-21T01:03:59ZengMDPI AGMachines2075-17022020-12-01848510.3390/machines8040085Neural Network-Based Automated Assessment of Fatigue Damage in Mechanical StructuresHassan Alqahtani0Asok Ray1Department of Mechanical Engineering, Pennsylvania State University, University Park, PA 16802, USADepartment of Mechanical Engineering, Pennsylvania State University, University Park, PA 16802, USAThis paper proposes a methodology for automated assessment of fatigue damage, which has been tested and validated with polycrystalline-alloy (A<i>ℓ</i>7075-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%.https://www.mdpi.com/2075-1702/8/4/85fatigue damagecrack tip opening displacementdetection and classificationlinear regressionneural network |
| spellingShingle | Hassan Alqahtani Asok Ray Neural Network-Based Automated Assessment of Fatigue Damage in Mechanical Structures Machines fatigue damage crack tip opening displacement detection and classification linear regression neural network |
| title | Neural Network-Based Automated Assessment of Fatigue Damage in Mechanical Structures |
| title_full | Neural Network-Based Automated Assessment of Fatigue Damage in Mechanical Structures |
| title_fullStr | Neural Network-Based Automated Assessment of Fatigue Damage in Mechanical Structures |
| title_full_unstemmed | Neural Network-Based Automated Assessment of Fatigue Damage in Mechanical Structures |
| title_short | Neural Network-Based Automated Assessment of Fatigue Damage in Mechanical Structures |
| title_sort | neural network based automated assessment of fatigue damage in mechanical structures |
| topic | fatigue damage crack tip opening displacement detection and classification linear regression neural network |
| url | https://www.mdpi.com/2075-1702/8/4/85 |
| work_keys_str_mv | AT hassanalqahtani neuralnetworkbasedautomatedassessmentoffatiguedamageinmechanicalstructures AT asokray neuralnetworkbasedautomatedassessmentoffatiguedamageinmechanicalstructures |