Thermomechanical fatigue life prediction of cast iron materials for cylinder head based on self-supervision adversarial training algorithm
PU Bowen
SUN Xingyue
ZHOU Tianguo
WEI Junchao
WANG Genquan
CHEN Xu
Abstract：Taking cast iron material of cylinder head as the research object,a series of thermo-mechanical fatigue experiments under different temperature ranges were conducted through bulk sampling.The results show that the fatigue test of cast iron materials exhibits three stages:cyclic softening,cyclic stability and rapid failure.Additionally,the fatigue life of materials under inverse phase loading is significantly shorter than that under positive phase loading.Six typical supervised learning models,including artificial neural networks(ANN)and random forest(RF),were used to predict the fatigue life of the experimental data.However,the results indicate that these models failed to learn the fatigue life distribution trend of the materials.For this problem,the prediction of the thermal mechanical fatigue life of cast iron materials for cylinder heads was achieved by using the self-supervised algorithm based on the generative adversarial network(GAN),and it showed a good prediction effect under the condition of small samples.This research has strong guiding significance and reference value for cylinder head design and fatigue analysis.
Keywords：Self-supervised algorithmDeep learningThermo-mechanical fatiguePulse fatigueLife prediction
Publication Date：2025-09-15
Online Publishing Date：2026-09-12(First online date of this platform, not the publication date of the document)
Pages：9( 241-249 )