基于小样本数据驱动的材料力学性能预测：齿轮钢淬透性案例

BogdanNenchev; 陶庆; 董子晖; ChinnapatPanwisawas; 李海洋; 陶镳; and 董洪标

doi:10.1007/s12613-022-2437-0

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文章导航 > 矿物冶金与材料学报（英文） > 2022 > 29(4): 836-847

Bogdan Nenchev, Qing Tao, Zihui Dong, Chinnapat Panwisawas, Haiyang Li, Biao Tao, and Hongbiao Dong, Evaluating data-driven algorithms for predicting mechanical properties with small datasets: A case study on gear steel hardenability, Int. J. Miner. Metall. Mater., 29(2022), No. 4, pp. 836-847. https://doi.org/10.1007/s12613-022-2437-0

Cite this article as:

Bogdan Nenchev, Qing Tao, Zihui Dong, Chinnapat Panwisawas, Haiyang Li, Biao Tao, and Hongbiao Dong, Evaluating data-driven algorithms for predicting mechanical properties with small datasets: A case study on gear steel hardenability, Int. J. Miner. Metall. Mater., 29(2022), No. 4, pp. 836-847. https://doi.org/10.1007/s12613-022-2437-0

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研究论文Open Access

基于小样本数据驱动的材料力学性能预测：齿轮钢淬透性案例

1)
NISCO UK Research Centre, School of Engineering, University of Leicester, Leicester LE1 7RH, UK
2)
南京钢铁集团, 南京 210044, 中国

通讯作者:
and 董洪标 E-mail: h.dong@le.ac.uk

文章亮点

1）评估了现有的机器学习方法和经验公式预测材料力学性能的局限性，讨论了机器学习方法分析小样本数据集面临的挑战。

2）分别建立了浅层神经网络和极端梯度树增强(XGboots)的两种机器学习模型来预测化学成分、处理工艺、样品尺寸和硬度之间的冶金学关系。

3）评估了各种方法在小样本数据集齿轮钢淬透性能预测的应用。

中文摘要

材料力学性能的预测涉及多物理场耦合和非线性微观组织响应，所以很难使用传统的解析方法来计算和预测。通常依赖于大量昂贵的实验室测试来建立经验模型来预测性能。近年来，机器学习方法已被用来尝试建立更稳健的非线性材料力学性能预测模型，比如神经网络和决策树。本文通过齿轮钢淬透性的案例研究，评估了现有的机器学习方法和经验公式预测材料力学性能的局限性，讨论了机器学习方法分析小样本数据集面临的挑战，并提出了处理具有多个变量的小样本数据集的解决方案。本研究将高斯方法与新型算法相结合，克服了分析齿轮钢淬透性数据的难点，揭示了合金元素的相互作用和组织均匀性对硬度的影响。研究结果表明融合物理冶金原理的机器学习淬透性预测模型的预测结果优于经验方法的预测结果。本研究还分别建立了浅层神经网络和极端梯度树增强(XGboots)的两种机器学习模型来预测化学成分、处理工艺、样品尺寸和硬度之间的冶金学关系。通过对多种机器学习算法和经验公式的计算结果进行比较，评估了各种方法在小样本数据集齿轮钢淬透性能预测的应用。结果表明，XGboost 机器学习模型能够最有效处理小样本数据中的不同钢种和不同工艺的问题，具有最高的预测准确率。

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Research ArticleOpen Access

Evaluating data-driven algorithms for predicting mechanical properties with small datasets: A case study on gear steel hardenability

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Abstract

Abstract

Data-driven algorithms for predicting mechanical properties with small datasets are evaluated in a case study on gear steel hardenability. The limitations of current data-driven algorithms and empirical models are identified. Challenges in analysing small datasets are discussed, and solution is proposed to handle small datasets with multiple variables. Gaussian methods in combination with novel predictive algorithms are utilized to overcome the challenges in analysing gear steel hardenability data and to gain insight into alloying elements interaction and structure homogeneity. The gained fundamental knowledge integrated with machine learning is shown to be superior to the empirical equations in predicting hardenability. Metallurgical-property relationships between chemistry, sample size, and hardness are predicted via two optimized machine learning algorithms: neural networks (NNs) and extreme gradient boosting (XGboost). A comparison is drawn between all algorithms, evaluating their performance based on small data sets. The results reveal that XGboost has the highest potential for predicting hardenability using small datasets with class imbalance and large inhomogeneity issues.
- machine learning;
- small dataset;
- XGboost;
- hardenability;
- gear steel

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