Machine-learning-based investigation into the effect of defect/inclusion on fatigue behavior in steels
Source abstract
Abstract
Abstract Fatigue tests were conducted under constant amplitude loading with a stress ratio of R = −1 to evaluate the effect of defect/inclusion on fatigue life of welding martensitic stainless steel (13Cr-5Ni) and KSFA90 steel (manufactured for crankshaft). An analysis based on the combined area eff of defect/inclusion and linear elastic fracture mechanics (LEFM), was performed. The results indicate that defect/inclusion dominated the fracture mode for both materials utilized. Moreover, the combined area eff and LEFM methods lose their accuracy for predicting fatigue life. Final, random forest machine-learning approach provided acceptable precision for unified predicting the fatigue of materials utilized in the current study.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Fatigue and fracture mechanics · Hydrogen embrittlement and corrosion behaviors in metals · Non-Destructive Testing Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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