Classification of Mechanical Properties of Aluminum Foam by Machine Learning

被引：4

作者：

Hangai, Yoshihiko ^{[1
]}

Okada, Kenji ^{[1
]}

Tanaka, Yuuki ^{[1
]}

Matsuura, Tsutomu ^{[1
]}

Amagai, Kenji ^{[1
]}

Suzuki, Ryosuke ^{[1
]}

Nakazawa, Nobuaki ^{[1
]}

机构：

[1] Gunma Univ, Fac Sci & Technol, Kiryu, Gumma 3768515, Japan

来源：

MATERIALS TRANSACTIONS | 2022年 / 63卷 / 02期

关键词：

cellular materials; machine learning; foam; X-ray CT; COMPRESSIVE BEHAVIOR; PORE STRUCTURE; FABRICATION; PREDICTION; POWDER;

D O I：

10.2320/matertrans.MT-M2021130

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In this study, the mechanical properties of aluminum foam were classified by machine learning from their X-ray computed tomography (CT) images. It was found that aluminum foam samples with high and low compressive strengths can be classified with an accuracy rate of more than 95%. In addition, it was indicated that the accuracy rate can be further improved by increasing the amount of training data. From these results, it is expected that the quality assurance method of aluminum foam can be established by nondestructively acquiring the images of the manufactured aluminum foam product.

引用

页码：257 / 260

页数：4

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