Experimental validation of an ANN model for random loading fatigue analysis
• The paper presents the validation of a generalised ANN model for a broad of component conditions and material properties. • Validation of theoretical ANN models with experimental data is rare in the literature. • The results of the validation show ANN to be a better approach for fatigue analysis t...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ramachandra, S. [verfasserIn] |
|---|
| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Umfang: |
10 |
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| Übergeordnetes Werk: |
Enthalten in: Study On Air Pollution and Respiratory Health Of Children In Delhi, India - Mathew, Jincy ELSEVIER, 2014, materials, structures, components, Oxford |
|---|---|
| Übergeordnetes Werk: |
volume:126 ; year:2019 ; pages:112-121 ; extent:10 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.ijfatigue.2019.04.028 |
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ELV04703856X |
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| 520 | |a • The paper presents the validation of a generalised ANN model for a broad of component conditions and material properties. • Validation of theoretical ANN models with experimental data is rare in the literature. • The results of the validation show ANN to be a better approach for fatigue analysis than existing frequency domain methods. • The results of the study also show that rainflow time domain fatigue analysis is not universally agreeable with experimental results. | ||
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10.1016/j.ijfatigue.2019.04.028 doi GBV00000000000646.pica (DE-627)ELV04703856X (ELSEVIER)S0142-1123(19)30152-5 DE-627 ger DE-627 rakwb eng 610 VZ 610 VZ 44.85 bkl Ramachandra, S. verfasserin aut Experimental validation of an ANN model for random loading fatigue analysis 2019 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • The paper presents the validation of a generalised ANN model for a broad of component conditions and material properties. • Validation of theoretical ANN models with experimental data is rare in the literature. • The results of the validation show ANN to be a better approach for fatigue analysis than existing frequency domain methods. • The results of the study also show that rainflow time domain fatigue analysis is not universally agreeable with experimental results. Frequency domain Elsevier SAE Elsevier Artificial neural networks Elsevier Time domain Elsevier Random fatigue Elsevier Artificial intelligence Elsevier Durodola, J.F. oth Fellows, N.A. oth Gerguri, S. oth Thite, A. oth Enthalten in Elsevier Mathew, Jincy ELSEVIER Study On Air Pollution and Respiratory Health Of Children In Delhi, India 2014 materials, structures, components Oxford (DE-627)ELV011995165 volume:126 year:2019 pages:112-121 extent:10 https://doi.org/10.1016/j.ijfatigue.2019.04.028 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.85 Kardiologie Angiologie VZ AR 126 2019 112-121 10 |
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10.1016/j.ijfatigue.2019.04.028 doi GBV00000000000646.pica (DE-627)ELV04703856X (ELSEVIER)S0142-1123(19)30152-5 DE-627 ger DE-627 rakwb eng 610 VZ 610 VZ 44.85 bkl Ramachandra, S. verfasserin aut Experimental validation of an ANN model for random loading fatigue analysis 2019 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • The paper presents the validation of a generalised ANN model for a broad of component conditions and material properties. • Validation of theoretical ANN models with experimental data is rare in the literature. • The results of the validation show ANN to be a better approach for fatigue analysis than existing frequency domain methods. • The results of the study also show that rainflow time domain fatigue analysis is not universally agreeable with experimental results. Frequency domain Elsevier SAE Elsevier Artificial neural networks Elsevier Time domain Elsevier Random fatigue Elsevier Artificial intelligence Elsevier Durodola, J.F. oth Fellows, N.A. oth Gerguri, S. oth Thite, A. oth Enthalten in Elsevier Mathew, Jincy ELSEVIER Study On Air Pollution and Respiratory Health Of Children In Delhi, India 2014 materials, structures, components Oxford (DE-627)ELV011995165 volume:126 year:2019 pages:112-121 extent:10 https://doi.org/10.1016/j.ijfatigue.2019.04.028 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.85 Kardiologie Angiologie VZ AR 126 2019 112-121 10 |
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• The paper presents the validation of a generalised ANN model for a broad of component conditions and material properties. • Validation of theoretical ANN models with experimental data is rare in the literature. • The results of the validation show ANN to be a better approach for fatigue analysis than existing frequency domain methods. • The results of the study also show that rainflow time domain fatigue analysis is not universally agreeable with experimental results. |
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• The paper presents the validation of a generalised ANN model for a broad of component conditions and material properties. • Validation of theoretical ANN models with experimental data is rare in the literature. • The results of the validation show ANN to be a better approach for fatigue analysis than existing frequency domain methods. • The results of the study also show that rainflow time domain fatigue analysis is not universally agreeable with experimental results. |
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• The paper presents the validation of a generalised ANN model for a broad of component conditions and material properties. • Validation of theoretical ANN models with experimental data is rare in the literature. • The results of the validation show ANN to be a better approach for fatigue analysis than existing frequency domain methods. • The results of the study also show that rainflow time domain fatigue analysis is not universally agreeable with experimental results. |
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