Improvement of data-driven techniques, specifically machine learning (ML), in material science turned it into a powerful tool for predicting materials behavior. Accordingly, this study provides a ML prediction of empirical creep lifetimes of 9Cr-1Mo ex-service heater tubes that have been used in industry for up to 47 years. Data from over 90,000 h of stress rupture tests shows that the service parameters influence creep lifetime similar to mechanical properties. Employing six different ML algorithms, viz., K Nearest Neighbors (KNN), Support Vector Regressor (SVR), Random Forest (RF), Gradient Boosting (GB), Gaussian Process (GP), and Multi-Layer Perceptron (MLP) demonstrated that the GP and MLP methods performed significantly better in predicting the creep lifetimes rather than other algorithms. Finally, a validation set involving 12 samples was conducted, and the GP algorithm showed better agreement with experimental values than other ML and Larson-Miller Parameter approaches, illustrating the capability of this model to predict creep lifetimes.

中文翻译：

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蠕变寿命预测的突破：利用机器学习和服务数据

材料科学中数据驱动技术（特别是机器学习 (ML)）的改进使其成为预测材料行为的强大工具。因此，本研究对已在工业中使用长达 47 年的 9Cr-1Mo 退役加热器管的经验蠕变寿命进行了 ML 预测。超过 90,000 小时的应力断裂测试数据表明，使用参数影响蠕变寿命，类似于机械性能。采用六种不同的 ML 算法，即 K 最近邻 (KNN)、支持向量回归器 (SVR)、随机森林 (RF)、梯度提升 (GB)、高斯过程 (GP) 和多层感知器 (MLP) GP 和 MLP 方法在预测蠕变寿命方面明显优于其他算法。最后，进行了包含 12 个样本的验证集，与其他 ML 和 Larson-Miller 参数方法相比，GP 算法与实验值的一致性更好，说明了该模型预测蠕变寿命的能力。