Application of soft constrained machine learning algorithms for creep rupture prediction of an austenitic heat resistant steel Sanicro 25

He, Jiankui; Sandström, Rolf; Zhang, Jing; Qin, Hai-Ying

doi:10.1016/j.jmrt.2022.11.154

Cited by 8 publications

(2 citation statements)

References 42 publications

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“…The Larson-Miller model is often used in engineering to predict the creep rupture life of materials [14][15][16][17][18][19][20][21][22][23]. Recently, some researchers used machine learning models to predict the creep rupture life of materials [24][25][26][27][28][29]. Both the time-temperature parametric model and the machine learning model possess unique advantages when predicting material creep rupture life.…”

Section: Introductionmentioning

confidence: 99%

“…Tan et al [26] proposed an integrated model coupled with Larson-Miller parameters and predicted a creep rupture life of 9% Cr martensitic heat-resistant steel through individual machine learning models (linear regression, support vector machine, and artificial neural network models) and integrated learning models, evaluating the prediction accuracy of each model. He et al [27] predicted the creep fracture behavior of austenitic heat-resistant steel Sanicro 25 using a soft-constrained machine learning model. Xiang et al [28] predicted the creep rupture life of Fe-Cr-Ni heat-resistant alloy using a deep learning model.…”

Section: Introductionmentioning

confidence: 99%

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A Method for Predicting the Creep Rupture Life of Small-Sample Materials Based on Parametric Models and Machine Learning Models

Zhang,

Yao,

et al. 2023

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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, R2), 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.

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