Data-driven phase recognition of steels for use in mechanical property prediction

被引：5
|
作者：
Zhang, Bin [1 ]
Shin, Yung C. [1 ]
机构：
[1] Purdue Univ, Sch Mech Engn, W Lafayette, IN 47907 USA
来源：
MANUFACTURING LETTERS | 2021年 / 30卷
关键词：
Martensite; Scanning electron microscope (SEM); Convolutional neural network (CNN); Pattern recognition; MICROSTRUCTURE; STRENGTH;
D O I：
10.1016/j.mfglet.2021.10.005
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
This paper develops a deep learning scheme of phase recognition for steel materials. A convolutional neural network classifier is established, such that the martensite phase, which has a substantial impact on the mechanical properties of steels, can be recognized from microstructure images and its volumetric fraction can also be estimated from multi-phase microconstituents. The testing results on an ultrahigh carbon steel dataset proved that the developed scheme has a rational phase recognition accuracy. The estimated martensite fraction can be used as an essential feature to predict the mechanical properties of materials in additive manufacturing. (C) 2021 Society of Manufacturing Engineers (SME). Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码：27 / 31
页数：5
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