A machine learning model for multi-class classification of quenched and partitioned steel microstructure type by the k-nearest neighbor algorithm

被引:11
作者
Gupta, Ashutosh Kumar [1 ]
Chakroborty, Sunny [2 ]
Ghosh, Swarup Kumar [2 ]
Ganguly, Subhas [1 ]
机构
[1] Natl Inst Technol, Dept Met & Mat Engn, Raipur 492010, India
[2] Indian Inst Engn Sci & Technol, Dept Met & Mat Engn, Howrah 711103, India
关键词
Quenched and partitioned steels; Machine learning; Steel microstructure; Multiclass classification; k-nearest neighbor classifier; MECHANICAL-PROPERTIES; MARTENSITE; STRENGTH;
D O I
10.1016/j.commatsci.2023.112321
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The paper proposed a machine learning model for multiclass classification of quenched and partitioned (Q & P) steel microstructure type. In this work, we implemented the k-nearest neighbor (k-NN) algorithm to train the classifier. A Q & P steel microstructure-type database has been compiled from the previous research data comprising the information of 348 steel samples. The feature space was described by the steel composition, lower critical temperature (Ac1), upper critical temperature (Ac3), martensitic-start temperature (Ms), etc., and the Q & P heat treatment parameters. At the same time, the target or dependent variable was recorded as the microstructure type, for example, martensite-retained austenite {M, RA}, martensite-bainite-retained austenite {M, B, RA} etc. The proposed classifier could achieve an overall performance of 97.7% and 77.7%, measured as f1-Score in the training and testing dataset, respectively. The martensite-retained austenite {M, RA} type was found to be the most confusing class. The model explored the effect of compositional parameters and heat treatment variables on the evolution of microstructure. The re-engineering through model study for targeted martensite-retained austenite microstructure type has depicted a steel composition and heat treatment window, which has been validated by experimental development of steel microstructure. The optical and SEM micrographs, along with hardness, strongly corroborated the model analysis from a re-engineering perspective.
引用
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页数:13
相关论文
共 33 条
[1]   Classification of steel microstructures using Modified Alternate Local Ternary Pattern [J].
Arivazhagan, S. ;
Tracia, J. Jasline ;
Selvakumar, N. .
MATERIALS RESEARCH EXPRESS, 2019, 6 (09)
[2]   Characterization and modeling of mechanical behavior of quenching and partitioning steels [J].
Arlazarov, A. ;
Bouaziz, O. ;
Masse, J. P. ;
Kegel, F. .
MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING, 2015, 620 :293-300
[3]   Advanced Steel Microstructural Classification by Deep Learning Methods [J].
Azimi, Seyed Majid ;
Britz, Dominik ;
Engstler, Michael ;
Fritz, Mario ;
Muecklich, Frank .
SCIENTIFIC REPORTS, 2018, 8
[4]  
Baird SG, 2023, COMPREHENSIVEINORGAN, P3, DOI DOI 10.1016/B978-0-12-823144-9.00079-0
[5]  
Beyer U.S. Kevin, 1999, IS NEAREST NEIGHBOR
[6]   Metallurgical Perspectives on Advanced Sheet Steels for Automotive Application [J].
Bhattacharya, Debanshu .
ADVANCED STEELS: THE RECENT SCENARIO IN STEEL SCIENCE AND TECHNOLOGY, 2011, :163-175
[7]   Image driven machine learning methods for microstructure recognition [J].
Chowdhury, Aritra ;
Kautz, Elizabeth ;
Yener, Bulent ;
Lewis, Daniel .
COMPUTATIONAL MATERIALS SCIENCE, 2016, 123 :176-187
[8]   NEAREST NEIGHBOR PATTERN CLASSIFICATION [J].
COVER, TM ;
HART, PE .
IEEE TRANSACTIONS ON INFORMATION THEORY, 1967, 13 (01) :21-+
[9]   Exploring the microstructure manifold: Image texture representations applied to ultrahigh carbon steel microstructures [J].
DeCost, Brian L. ;
Francis, Toby ;
Holm, Elizabeth A. .
ACTA MATERIALIA, 2017, 133 :30-40
[10]   A computer vision approach for automated analysis and classification of microstructural image data [J].
DeCost, Brian L. ;
Holm, Elizabeth A. .
COMPUTATIONAL MATERIALS SCIENCE, 2015, 110 :126-133