Deep Learning algorithm for the assessment of the first damage initiation monitoring the energy release of materials
Monitoring the energy release during fatigue tests of common engineering materials has been shown to give relevant information on fatigue properties, reducing the testing time and material consumption. During a static tensile test, it is possible to assess two distinct phases: In the first phase (P...
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| Format: | Article |
| Language: | English |
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Gruppo Italiano Frattura
2022-09-01
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| Series: | Frattura ed Integrità Strutturale |
| Subjects: | |
| Online Access: | https://3.64.71.86/index.php/fis/article/view/3605 |
| _version_ | 1797996345554894848 |
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| author | Dario Milone Dario Santonocito |
| author_facet | Dario Milone Dario Santonocito |
| author_sort | Dario Milone |
| collection | DOAJ |
| description |
Monitoring the energy release during fatigue tests of common engineering materials has been shown to give relevant information on fatigue properties, reducing the testing time and material consumption.
During a static tensile test, it is possible to assess two distinct phases: In the first phase (Phase I), where all the crystals are elastically stressed, the temperature trend follows the linear thermoelastic law; while, in the second phase (Phase II), some crystals begin to deform, and the temperature assumes a non-linear trend. The macroscopic transition stress between Phase I and Phase II could be related to the “limit stress” that, if cyclically applied, would lead to material failure. Nowadays, it is impossible to distinguish the transition between Phase I and Phase II in an objective way. Indeed, it is up to the operator's experiences.
This work aims to create a universal methodology that predicts the limit stress by assessing the change in temperature trend by adopting Neural Networks. A Deep Learning algorithm has been created and trained on experimental data coming from static tensile tests performed on several classes of materials (steels, plastics, composite materials). Once trained, the network can predict the transition temperature at which the first plastic deformation occurs within the material.
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| first_indexed | 2024-04-11T10:16:06Z |
| format | Article |
| id | doaj.art-5457f91b58ec4f6798491ced8d5982a1 |
| institution | Directory Open Access Journal |
| issn | 1971-8993 |
| language | English |
| last_indexed | 2024-04-11T10:16:06Z |
| publishDate | 2022-09-01 |
| publisher | Gruppo Italiano Frattura |
| record_format | Article |
| series | Frattura ed Integrità Strutturale |
| spelling | doaj.art-5457f91b58ec4f6798491ced8d5982a12022-12-22T04:29:58ZengGruppo Italiano FratturaFrattura ed Integrità Strutturale1971-89932022-09-011662Deep Learning algorithm for the assessment of the first damage initiation monitoring the energy release of materialsDario Milone0Dario Santonocito1University of Messina, ItalyUniversity of Messina, Italy Monitoring the energy release during fatigue tests of common engineering materials has been shown to give relevant information on fatigue properties, reducing the testing time and material consumption. During a static tensile test, it is possible to assess two distinct phases: In the first phase (Phase I), where all the crystals are elastically stressed, the temperature trend follows the linear thermoelastic law; while, in the second phase (Phase II), some crystals begin to deform, and the temperature assumes a non-linear trend. The macroscopic transition stress between Phase I and Phase II could be related to the “limit stress” that, if cyclically applied, would lead to material failure. Nowadays, it is impossible to distinguish the transition between Phase I and Phase II in an objective way. Indeed, it is up to the operator's experiences. This work aims to create a universal methodology that predicts the limit stress by assessing the change in temperature trend by adopting Neural Networks. A Deep Learning algorithm has been created and trained on experimental data coming from static tensile tests performed on several classes of materials (steels, plastics, composite materials). Once trained, the network can predict the transition temperature at which the first plastic deformation occurs within the material. https://3.64.71.86/index.php/fis/article/view/3605Fatigue limitSTMDeep LearningInfrared Thermography |
| spellingShingle | Dario Milone Dario Santonocito Deep Learning algorithm for the assessment of the first damage initiation monitoring the energy release of materials Frattura ed Integrità Strutturale Fatigue limit STM Deep Learning Infrared Thermography |
| title | Deep Learning algorithm for the assessment of the first damage initiation monitoring the energy release of materials |
| title_full | Deep Learning algorithm for the assessment of the first damage initiation monitoring the energy release of materials |
| title_fullStr | Deep Learning algorithm for the assessment of the first damage initiation monitoring the energy release of materials |
| title_full_unstemmed | Deep Learning algorithm for the assessment of the first damage initiation monitoring the energy release of materials |
| title_short | Deep Learning algorithm for the assessment of the first damage initiation monitoring the energy release of materials |
| title_sort | deep learning algorithm for the assessment of the first damage initiation monitoring the energy release of materials |
| topic | Fatigue limit STM Deep Learning Infrared Thermography |
| url | https://3.64.71.86/index.php/fis/article/view/3605 |
| work_keys_str_mv | AT dariomilone deeplearningalgorithmfortheassessmentofthefirstdamageinitiationmonitoringtheenergyreleaseofmaterials AT dariosantonocito deeplearningalgorithmfortheassessmentofthefirstdamageinitiationmonitoringtheenergyreleaseofmaterials |