Zhang Xu, Yao Jianyao, Wu Yulin, Liu Xuyang, Wang Changyin, Liu Hao
College of Aerospace Engineering, Chongqing University, Chongqing 400044, China.
Materials (Basel). 2023 Oct 22;16(20):6804. doi: 10.3390/ma16206804.
In view of the differences in the applicability and prediction ability of different creep rupture life prediction models, we propose a creep rupture life prediction method in this paper. Various time-temperature parametric models, machine learning models, and a new method combining time-temperature parametric models with machine learning models are used to predict the creep rupture life of a small-sample material. The prediction accuracy of each model is quantitatively compared using model evaluation indicators (RMSE, MAPE, R), and the output values of the most accurate model are used as the output values of the prediction method. The prediction method not only improves the applicability and accuracy of creep rupture life predictions but also quantifies the influence of each input variable on creep rupture life through the machine learning model. A new method is proposed in order to effectively take advantage of both advanced machine learning models and classical time-temperature parametric models. Parametric equations of creep rupture life, stress, and temperature are obtained using different time-temperature parametric models; then, creep rupture life data, obtained via equations under other temperature and stress conditions, are used to expand the training set data of different machine learning models. By expanding the data of different intervals, the problem of the low accuracy of the machine learning model for the small-sample material is solved.
鉴于不同蠕变断裂寿命预测模型在适用性和预测能力方面存在差异，本文提出了一种蠕变断裂寿命预测方法。采用各种时间 - 温度参数模型、机器学习模型以及一种将时间 - 温度参数模型与机器学习模型相结合的新方法，对小样本材料的蠕变断裂寿命进行预测。使用模型评估指标（RMSE、MAPE、R）对各模型的预测精度进行定量比较，并将最精确模型的输出值作为预测方法的输出值。该预测方法不仅提高了蠕变断裂寿命预测的适用性和准确性，还通过机器学习模型量化了各输入变量对蠕变断裂寿命的影响。提出了一种新方法，以便有效利用先进的机器学习模型和经典的时间 - 温度参数模型。使用不同的时间 - 温度参数模型获得蠕变断裂寿命、应力和温度的参数方程；然后，利用在其他温度和应力条件下通过方程获得的蠕变断裂寿命数据来扩展不同机器学习模型的训练集数据。通过扩展不同区间的数据，解决了机器学习模型对小样本材料预测精度低的问题。
