機械学習を用いたピストンリングの外観検査システム

カテゴリ: 論文誌(論文単位)

グループ名: 【D】産業応用部門

発行日: 2023/02/01

タイトル(英語): Visual Inspection System for Piston-Ring Parts via Machine Learning

著者名: 平良　琢真（（株）AI-Signコンサルティング），Zheng　Wanxian（琉球大学），宮城　武志（（株）AI-Signコンサルティング），長田　康敬（琉球大学）

著者名(英語): Takuma Taira (AI-Sign Consulting), Wanxian Zheng (University of The Ryukyus), Takeshi Miyagi (AI-Sign Consulting), Yasunori Nagata (University of The Ryukyus)

キーワード: 外観検査，機械学習，異常検知，画像処理　　visual inspection，machine learning，anomaly detection，image processing

要約(英語): Development of a system that can automatically detect appearance defects of piston-ring components of the engine cylinder caused during the coating process. In the proposed system, piston-rings are sent from the feeder to conveyor belt, and an image captured by camera. Subsequently, the image is cut along with the shape of ring into small images. A convolution neural network (CNN) model to classify which piston-ring is a normal and anomaly. Finally, a robot arm is utilized to remove the anomaly piston-ring from the conveyor belt.In our previous experiment, when a GPU-based computer was used to process images, the system could achieve approximately 90-100% accuracy based on the type of defects. To reduce the costs of system, we study single-board computer (SBC) with Google Edge TPU USB Accelerator to classify images, which exhibits good potential to replace GPU-based processing. Furthermore, this paper also proposes some approaches to improve processing speed when using proposes low-cost SBC platform.

本誌: 電気学会論文誌D（産業応用部門誌） Vol.143 No.2 （2023） 特集：ドローンとロボット組み込み/サスティナブルシステム

本誌掲載ページ: 101-105 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejias/143/2/143_101/_article/-char/ja/