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

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

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.

• Jezik: Angleški jezik
• Ključne besede: estimation of material parameters, fatigue strength, multiaxial fatigue analysis, machine learning, random forest
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FS - Fakulteta za strojništvo
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2023
• Št. strani: 19 str.
• Številčenje: Vol. 46, iss. 11
• PID: 20.500.12556/RUL-152813
• UDK: 620.178.3
• ISSN pri članku: 8756-758X
• DOI: 10.1111/ffe.14128
• COBISS.SI-ID: 162437891
• Datum objave v RUL: 07.12.2023

Gradivo je del revije

• Naslov: Fatigue & fracture of engineering materials & structures
• Skrajšan naslov: Fatigue fract. eng. mater. struct.
• Založnik: Wiley
• ISSN: 8756-758X
• COBISS.SI-ID: 584210

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: ocenjevanje materialnih parametrov, zdržljivost materiala, strojno učenje, analiza večosnega utrujanja

Projekti

• Financer: Drugi - Drug financer ali več financerjev
• Program financ.: European Social Fund
• Številka projekta: CZ.02.2.69/0.0/0.0/18_053/0016980

• Financer: ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
• Številka projekta: P2-0182
• Naslov: Razvojna vrednotenja