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Pioneering fatigue life prediction model based on the Gaussian process in conjunction with machine learning

2021/41/B/ST8/00257

Keywords:

Fatigue life multiaxial loading fatigue life prediction Gaussian process machine learning

Descriptors:

  • ST8_7: Machine design (modelling, shaping, machining)

Panel:

ST8 - Production and processes engineering: modelling, product design, process design and control, construction methods and engineering, power units and systems

Host institution :

Politechnika Opolska, Wydział Mechaniczny

woj.

Other projects carried out by the institution 

Principal investigator (from the host institution):

prof. Aleksander Gracjan Karolczuk 

Number of co-investigators in the project: 4

Call: OPUS 21 - announced on 2021-03-15

Amount awarded: 381 860 PLN

Project start date (Y-m-d): 2022-01-12

Project end date (Y-m-d): 2025-01-11

Project duration:: 36 months (the same as in the proposal)

Project status: Project completed

Project description

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Note - project descriptions were prepared by the authors of the applications themselves and placed in the system in an unchanged form.

Information in the final report

  • Publication in academic press/journals (4)
  1. Heterogeneous effect of aging temperature on the fatigue life of additively manufactured thin-walled 18Ni300 maraging steel tubular specimen
    Authors:
    Aleksander Karolczuk, Andrzej Kurek, Michał Bohm, Szymon Derda, Mariusz Prażmowski, Krzysztof Kluger, Krzysztof Żak, Łukasz Pejkowski, Jan Seyda
    Academic press:
    Materials & Design (rok: 2024, tom: 237, strony: 112561), Wydawca: Elsevier
    Status:
    Published
    DOI:
    10.1016/j.matdes.2023.112561 - link to the publication
  2. Gaussian Process for Machine Learning-Based Fatigue Life Prediction Model under Multiaxial Stress–Strain Conditions
    Authors:
    Aleksander Karolczuk, Dariusz Skibicki, Łukasz Pejkowski
    Academic press:
    Materials (rok: 2022, tom: 15, strony: 24), Wydawca: MDPI
    Status:
    Published
    DOI:
    10.3390/ma15217797 - link to the publication
  3. Gaussian Process for Machine Learning-Based Fatigue Life Prediction Model under Multiaxial Stress–Strain Conditions
    Authors:
    Aleksander Karolczuk, Dariusz Skibicki, Łukasz Pejkowski
    Academic press:
    Materials (rok: 2022, tom: 15, strony: 24), Wydawca: MDPI
    Status:
    Published
    DOI:
    10.3390/ma15217797 - link to the publication
  4. Physics-constrained Gaussian process for life prediction under in-phase multiaxial cyclic loading with superposed static components
    Authors:
    Aleksander Karolczuk, Yongming Liu, Krzysztof Kluger, Szymon Derda, Dariusz Skibicki, Łukasz Pejkowski
    Academic press:
    International Journal of Fatigue (rok: 2023, tom: 175, strony: 107776), Wydawca: Elsevier
    Status:
    Published
    DOI:
    10.1016/j.ijfatigue.2023.107776 - link to the publication

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