Application of machine learning models for estimating the material parameters for multiaxial fatigue strength calculation

Nagode, Marko (author), Papuga, Jan (author), Oman, Simon (author)

This paper deals with a practical task of estimating missing material fatigue strengths required for the evaluation of multiaxial fatigue strength criteria, knowing other static or fatigue material parameters. Instead of searching for various analytical equations describing the dependencies between different material parameters, several machine learning models implemented in the caret R package are used here. The dataset used to train and test these models is based on the FatLim dataset with different material parameters, which has been redesigned for this new purpose. It is demonstrated that substantially more data points, such as were available in this study, are needed to achieve the goal set here. Although the results obtained at the current scale may be improved by the addition of new data points, the best performance of the random forest model rf and the worst performance of the pcr model are evident.

• Language: English
• Keywords: estimation of material parameters, fatigue strength, multiaxial fatigue analysis, machine learning, random forest
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FS - Faculty of Mechanical Engineering
• Publication status: Published
• Publication version: Version of Record
• Year: 2023
• Number of pages: 19 str.
• Numbering: Vol. 46, iss. 11
• PID: 20.500.12556/RUL-152813
• UDC: 620.178.3
• ISSN on article: 8756-758X
• DOI: 10.1111/ffe.14128
• COBISS.SI-ID: 162437891
• Publication date in RUL: 07.12.2023

Record is a part of a journal

• Title: Fatigue & fracture of engineering materials & structures
• Shortened title: Fatigue fract. eng. mater. struct.
• Publisher: Wiley
• ISSN: 8756-758X
• COBISS.SI-ID: 584210

Secondary language

• Language: Slovenian
• Keywords: ocenjevanje materialnih parametrov, zdržljivost materiala, strojno učenje, analiza večosnega utrujanja

Projects

• Funder: Other - Other funder or multiple funders
• Funding programme: European Social Fund
• Project number: CZ.02.2.69/0.0/0.0/18_053/0016980

• Funder: ARRS - Slovenian Research Agency
• Project number: P2-0182
• Name: Razvojna vrednotenja