摘要
4130X钢是常见的储氢材料,在应用中面临氢致疲劳裂纹失效的风险,可能影响其使用寿命。目前,气体氢和液态氢氛围下的氢致腐蚀疲劳裂纹扩展测试的结果尚缺乏系统性的梳理和分析。采用机器学习的方法,分析研究4130X钢基础力学性能参数和腐蚀疲劳测试参数对4130X钢氢致疲劳裂纹扩展的影响。结果表明:氢的浓度和形式是影响4130X钢氢致疲劳裂纹的最重要因素,同时通过机器学习算法建立4130X钢氢致疲劳裂纹扩展预测模型,经验证该模型匹配性较高。
4130X steel is widely used for hydrogen storage vessels.Hydrogen induced fatigue crack expansion is a risk,which will affect 4130X steel's service life.At present,there’s still lack systematic analysis of hydrogen induced fatigue crack expansion test results for both gaseous and liquid hydrogen testing environment.In this paper,machine learning method is used to analyse the affect of basic mechanical property parameters and corrosion fatigue test parameters on 4130X steel's hydrogen induced fatigue crack expansion.The results show that hydrogen concentration and form are the most important factors affecting hydrogen induced fatigue crack of 4130X steel.Besides,the prediction model of hydrogen induced fatigue crack expansion of 4130X steel is established by machine learning method,this model is proved to be more accurate.
作者
毕然
Bi Ran(Tianjin DEWHP New Material Technology Co.,Ltd.)
出处
《宽厚板》
2024年第3期16-20,共5页
Wide and Heavy Plate
关键词
4130X钢
机器学习
疲劳裂纹扩展
氢能
4130X steel
Machine learning
Fatigue crack expansion
Hydrogen energy
