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Title: An autonomous system design for mold loading on press brake machines using a camera platform, deep learning, and image processing
Authors: Öziç, Muhammet Usame
Barstugan, Mucahid
Özdamar, Atakan
Keywords: Deep learning
Image processing
Press brake
Issue Date: 2023
Publisher: Korean Soc Mechanical Engineers
Abstract: Press brakes are among the most important machines used in sheet metal processing. In these machines, different numbers of molds are used for sheet bending and these molds are placed in the system by an operator. However, this process is slow, error-prone, and dependent on human labor. In this study, a real-time system that automatically detects molds and manipulates a robotic arm was designed using YOLOv4 and image processing. YOLOv4, a deep learning (DL)-based object detection algorithm, was applied to detect the positions, types, and holes of molds. Classical image processing methods were implemented to find the center (X, Y) coordinates of the mold hole. This study shows that the press brake machines currently used in industry can be transformed into smart machines through DL, image processing, camera systems, and robotic arm features.
Description: Article; Early Access
ISSN: 1738-494X
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

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