An autonomous system design for mold loading on press brake machines using a camera platform, deep learning, and image processing

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.