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Forming state recognition in deep drawing process with machine learning

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Title: Forming state recognition in deep drawing process with machine learning
Authors: Tsuruya, Tomohiro Browse this author
Danseko, Musashi Browse this author
Sasaki, Katsuhiko Browse this author →KAKEN DB
Honda, Shinya Browse this author →KAKEN DB
Takeda, Ryo Browse this author →KAKEN DB
Keywords: Sheet metal forming
Acoustic emission
Deep learning
Deep drawing
Data acquisition
Issue Date: 2019
Publisher: Japan Society of Mechanical Engineers
Journal Title: Journal of Advanced Mechanical Design, Systems, and Manufacturing
Volume: 13
Issue: 3
Start Page: 19-00205
Publisher DOI: 10.1299/jamdsm.2019jamdsm0066
Abstract: In press processing, quality inspection of a product is often carried out for each lot in the post process stage. When a failure occurs, it may result in a large number of defective products due to the fast processing speed. In order to prevent this, it is ideal to immediately stop the processing just after the defect occurs. Therefore, confirming the processing state in-process is required. This study proposes a new quality inspection method for deep drawing processes by using the count rate of acoustic emission (AE) signals. To analyze the AE count, deep learning, which is a multilayered neural network, is employed to recognize defects during a deep drawing process. The material used was a ductile material of cold rolled steel plate and is relatively difficult to find cracks during the deep drawing process. Characteristics were clarified by analysis of AE counts at plastic deformation and brake of material, and performing the forming state recognition experiment by a multilayer neural network (deep learning) showed a maximum recognition rate of 97.3%. High recognition rate was obtained despite the small number of data used.
Type: article
Appears in Collections:工学院・工学研究院 (Graduate School of Engineering / Faculty of Engineering) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

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