Applicability of Map generated by Self-Organizing Map Algorithm in Hammering Sound Inspection

被引：0

作者：

Oka, Daisuke ^{[1
]}

Hoshino, Yasutaka ^{[2
]}

Motegi, Kazuhiro ^{[1
]}

Shiraishi, Yoichi ^{[1
]}

机构：

[1] Gunma Univ, Grad Sch Sci & Technol, Dept Mech Sci & Technol, Gunma, Japan

[2] Gunma Univ, Sch Sci & Technol, Dept Mech Sci & Technol, Gunma, Japan

来源：

2020 59TH ANNUAL CONFERENCE OF THE SOCIETY OF INSTRUMENT AND CONTROL ENGINEERS OF JAPAN (SICE) | 2020年

关键词：

Unsupervised Machine Learning; Self-Organizing Map; Hammering Sound Inspection;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The hammering sound inspection for pieces plays the Digital Twin part in the Hybrid Twin approach The map generated by Self-Organizing Map (SOM) algorithm makes it possible to cluster the pieces into defected and non-defected groups by checking the corresponding hammering sounds. However, the applicability of map generated by SOM has not yet been discussed. This paper suggests a procedure how to improve a map generated by SOM algorithm. It shows that if the hammering sound of a piece is mapped on the boundary between the areas corresponding to defected and non-defected products, the map cannot be applied to inspect this piece and another map must be generated by repeating the training including the hammering sounds of such pieces by SOM algorithm. By executing the procedure some number of times. it is likely that the improved map will be used for hammering sound inspection.

引用

页码：1622 / 1627

页数：6

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