Understanding geometrical size effect on fatigue life of A588 steel using a machine learning approach
Source abstract
Abstract
In this paper, both experimental and machine learning results show that the fatigue life of A588 steel specimens with different gauge lengths and widths varies more greatly compared with that of the specimens with different thicknesses as the gauge dimensions are reduced from 15 mm to 1.5 mm. The optimal machine learning algorithm is derived to predict the fatigue life of specimens with a thickness of 1 mm, and the predicted results are verified by the fatigue experiments.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Fatigue and fracture mechanics · Non-Destructive Testing Techniques · Metallurgy and Material Forming
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (52)
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