Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters
Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters
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DOI：
10.1186/2193-9772-3-8
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发表时间：
2014-12-01
影响因子：
3.3
通讯作者：
Kalidindi, Surya R.
中科院分区：
文献类型：
--
作者：
Agrawal, Ankit;Deshpande, Parijat D.;Kalidindi, Surya R.
本文介绍了如何使用数据分析工具来预测钢材的疲劳强度。几种基于物理和数据驱动的方法已用于得出合金的各种性能及其成分和制造工艺参数之间的相关性。数据驱动的方法对材料工程师非常感兴趣，特别是在获得循环疲劳等极值特性方面，当前最先进的基于物理的模型具有严重的局限性。不幸的是，这些努力的成功记录有限。在本文中，我们利用来自国家材料科学研究所（NIMS）公共领域数据库的数据，探索不同数据科学技术（包括特征选择和预测建模）在钢材疲劳性能中的应用，并提出了一个用于探索材料信息学的系统性端到端框架。结果表明，神经网络、决策树和多元多项式回归等多种先进的数据分析技术可以在预测精度方面比之前的工作取得显着提高，R-2 值超过 0.97。结果成功证明了此类数据挖掘工具的实用性，可以按照预测钢疲劳强度的潜力顺序对成分和工艺参数进行排序，并实际上开发出相同的预测模型。
This paper describes the use of data analytics tools for predicting the fatigue strength of steels. Several physics-based as well as data-driven approaches have been used to arrive at correlations between various properties of alloys and their compositions and manufacturing process parameters. Data-driven approaches are of significant interest to materials engineers especially in arriving at extreme value properties such as cyclic fatigue, where the current state-of-the-art physics based models have severe limitations. Unfortunately, there is limited amount of documented success in these efforts. In this paper, we explore the application of different data science techniques, including feature selection and predictive modeling, to the fatigue properties of steels, utilizing the data from the National Institute for Material Science (NIMS) public domain database, and present a systematic end-to-end framework for exploring materials informatics. Results demonstrate that several advanced data analytics techniques such as neural networks, decision trees, and multivariate polynomial regression can achieve significant improvement in the prediction accuracy over previous efforts, with R-2 values over 0.97. The results have successfully demonstrated the utility of such data mining tools for ranking the composition and process parameters in the order of their potential for predicting fatigue strength of steels, and actually develop predictive models for the same.