基于集成数据挖掘的钢的疲劳强度预测:钢的疲劳强度预测

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management Pub Date : 2016-10-24 DOI:10.1145/2983323.2983343

Ankit Agrawal, A. Choudhary

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引用次数: 15

摘要

疲劳强度是钢最重要的力学性能之一。疲劳测试的高成本和时间，以及疲劳失效的潜在灾难性后果，促使了这种特性的预测模型的发展。为此，我们开发了先进的数据驱动集成预测模型，具有> 98%的交叉验证精度，并将这些模型部署在用户友好的在线网络工具中，该工具可以非常快速地预测由其成分和加工信息表示的给定钢的疲劳强度。这种具有快速和准确模型的工具有望成为材料科学研究人员和从业人员帮助他们寻找新的和提高质量的钢的非常有用的资源。该网络工具可在http://info.eecs.northwestern.edu/SteelFatigueStrengthPredictor上获得

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A Fatigue Strength Predictor for Steels Using Ensemble Data Mining: Steel Fatigue Strength Predictor

Fatigue strength is one of the most important mechanical properties of steel. High cost and time for fatigue testing, and potentially disastrous consequences of fatigue failures motivates the development of predictive models for this property. We have developed advanced data-driven ensemble predictive models for this purpose with an extremely high cross-validated accuracy of >98\%, and have deployed these models in a user-friendly online web-tool, which can make very fast predictions of fatigue strength for a given steel represented by its composition and processing information. Such a tool with fast and accurate models is expected to be a very useful resource for the materials science researchers and practitioners to assist in their search for new and improved quality steels. The web-tool is available at http://info.eecs.northwestern.edu/SteelFatigueStrengthPredictor

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Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

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