Detection of fatigue degradation in austenitic stainless steel with eddy current probe and machine learning
| Autor: | Klaus Heckmann, Ruth Acosta, Tobias Bill, Kai Donnerbauer, Christian Boller, Jürgen Sievers, Marina Macias Barrientos, Frank Walther, Peter Starke |
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| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Journal of Materials Research and Technology, Vol 27, Iss , Pp 7336-7346 (2023) |
| Druh dokumentu: | article |
| ISSN: | 2238-7854 47097418 |
| DOI: | 10.1016/j.jmrt.2023.11.176 |
| Popis: | Low cycle fatigue tests are performed on specimens of niobium stabilized austenitic steel AISI 347 (1.4550) at ambient temperature. During the test, the fatigue specimens are equipped with eddy current probes, and it can be seen here that the impedance phase shift changes significantly at very early stages of fatigue (i.e. before cracking). Electron backscattering diffraction investigations were carried out to better connect microstructure evolution with impedance phase shifts. Machine learning techniques are employed to relate the impedance shift to the fatigue degradation. This approach allows also the derivation of fatigue life curves with few specimens. |
| Databáze: | Directory of Open Access Journals |
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