Augmented Ultrasonic Data for Machine Learning

Autor: Virkkunen, Iikka, Koskinen, Tuomas, Jessen-Juhler, Oskari, Rinta-Aho, Jari

Flaw detection in non-destructive testing, especially in complex signals like ultrasonic data, has thus far relied heavily on the expertise and judgement of trained human inspectors. While automated systems have been used for a long time, these have

Externí odkaz: http://arxiv.org/abs/1903.11399

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Ultrasound as a non-destructive tool to estimate polymer embrittlement

Autor: Rinta-aho, Jari, Sipilä, Konsta, Zak, Pavel, Sarsounova, Zuzana

Publikováno v: Rinta-aho, J, Sipilä, K, Zak, P & Sarsounova, Z 2021, Ultrasound as a non-destructive tool to estimate polymer embrittlement . in European NDE Symposium for NPP : Event booklet . NOMAD project, pp. 17, European Symposium on NDE for NPP, Saarbruchen, Germany, 4/05/21 . < https://nomad-horizon2020.eu/events/european-symposium-on-non-destructive-evaluation-for-npp/_speakers/NOMAD_eventbooklet_0305_final.pdf >

In our study, 10 specimens of commercially available coaxial cable were exposed to gamma irradiation using two different dose rates and five different total doses. Then, the sound velocities were measured using traditional pulse-echo approach with an

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=355e65625b88::10959b554e283a1b3334682187d4418e
https://cris.vtt.fi/ws/files/65176323/TMC_european_NDT_symp.pdf

Sound velocity as a non-destructive indicator for gamma-irradiation induced embrittlement in polyethylene insulators

Autor: Rinta-aho, Jari, Sipilä, Konsta, Zak, Pavel, Sarsounova, Zuzana

Publikováno v: Rinta-aho, J, Sipilä, K, Zak, P & Sarsounova, Z 2021, ' Sound velocity as a non-destructive indicator for gamma-irradiation induced embrittlement in polyethylene insulators ', Physics Days 2021, Jyväskylä, Finland, 24/03/21-26/03/21 .

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=355e65625b88::464d74bdd437f1ba9ab63841bb8e694a
https://cris.vtt.fi/en/publications/ffb6a2e1-74e8-4813-a802-71a439119966

Machine Learning for NDT in the Finnish Nuclear Industry

Autor: Koskinen, Tuomas, Virkkunen, Iikka, Rinta-aho, Jari, Jessen-Juhler, Oskari

Publikováno v: Koskinen, T, Virkkunen, I, Rinta-aho, J & Jessen-Juhler, O 2019, Machine Learning for NDT in the Finnish Nuclear Industry . VTT Research Report, no. VTT-R-00584-19, VTT Technical Research Centre of Finland .

Machine learning (ML) has made marked advancements in the recent years. In many industries, machine learning algorithms have reached near-human or superhuman performance in tasks that were long considered impossible to automate. For NDT, the opportun

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=355e65625b88::1b39d13df177ea3e076f8d690215ae2c
https://cris.vtt.fi/en/publications/8dcb374f-dbff-40bc-b2d9-b6a642b99f93