Improved data-driven performance of Charpy impact toughness via literature-assisted production data in pipeline steel  

作　　者：SHANG ChunLei WANG ChuanJun WU HongHui LIU WenYue CHEN YiMian PAN GuangFei WANG ShuiZe WU GuiLin GAO JunHeng ZHAO HaiTao ZHANG ChaoLei MAO XinPing 

机构地区：[1]Beijing Advanced Innovation Center for Materials Genome Engineering,Innovation Research Institute for Carbon Neutrality,University of Science and Technology Beijing,Beijing 100083,China [2]State Key Laboratory of Metal Materials for Marine Equipment and Application,Anshan 114009,China [3]Ansteel Beijing Research Institute Co.Ltd.,Beijing 102200,China

出　　处：《Science China(Technological Sciences)》2023年第7期2069-2079,共11页中国科学（技术科学英文版）

基　　金：supported by the National Natural Science Foundation of China(Grant Nos.52122408,51901013,52071023);financial support from the Fundamental Research Funds for the Central Universities(University of Science and Technology Beijing)(Grant Nos.FRF-TP-2021-04C1,and 06500135);supported by USTB MatCom of Beijing Advanced Innovation Center for Materials Genome Engineering。

摘　　要：Pipeline transportation is one of the most economical ways to transport crude oil and natural gas over long distances.High toughness is one of the important qualities of pipeline steel to ensure safe transportation,wherein a key factor characterizing toughness is Charpy impact toughness(CIT).In this work,according to the production line data provided by a steel mill and the experimental data collected in literature,two machine learning model construction strategies were proposed.One was based solely on the production line dataset,and the other was based on the production line dataset together with the literature dataset.In these two strategies,the random forest model displayed the best prediction results,the accuracy of strategy I was 0.58,and the accuracy of strategy II was 0.90,wherein literature data effectively improved the CIT prediction accuracy.Finally,an optimized CIT model based on machine learning algorithms was established.The proposed strategy of literature data-assisted production line data provides a new perspective for optimizing and predicting the performance of traditional structural materials.

关 键 词：data-driven design pipeline steel Charpy impact toughness machine learning 

分 类 号：TE973[石油与天然气工程—石油机械设备] TP181[自动化与计算机技术—控制理论与控制工程]