利用机器人线弧增材制造技术进行深度学习辅助金属部件制造

Pingyang Zheng, Shaohua Han, Dingqi Xue, Ling Fu, Bifeng Jiang
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引用次数: 0

摘要

目的与其他增材制造技术相比,机器人线弧增材制造(WAAM)技术具有沉积效率高、制造成本低等优点,已被广泛应用于制造中大型金属部件。增材制造(AM)方法是一个相对复杂的过程,涉及工件建模、模型文件转换、切片、路径规划等。然后通过累积焊珠形成结构。然而,由于 WAAM 的成形精度较差,通常会导致竣工结构与预先设计结构之间存在严重的尺寸偏差。本文旨在提出一种视觉传感技术和深度学习辅助的金属结构制造 WAAM 方法,以简化复杂的 WAAM 过程,提高成形精度。设计/方法/途径该方法无需在制造过程中提前对工件建模切片并生成所有焊枪路径,而是通过在 Yolov5 算法中加入特征点回归分支,从竣工结构图像中检测特征点。每个沉积层特征点的坐标可以自动计算。结果改进后的 YOLOv5 检测器的平均精度为 99.5%。利用该方法制作了两种悬挂结构。原创性/价值在不使用复杂的切片策略、转台或其他额外支持的情况下制造圆形悬挂结构,验证了采用深度学习技术的机器人 WAAM 系统的可能性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep learning assisted fabrication of metallic components using the robotic wire arc additive manufacturing
Purpose Because of the advantages of high deposition efficiency and low manufacturing cost compared with other additive technologies, robotic wire arc additive manufacturing (WAAM) technology has been widely applied for fabricating medium- to large-scale metallic components. The additive manufacturing (AM) method is a relatively complex process, which involves the workpiece modeling, conversion of the model file, slicing, path planning and so on. Then the structure is formed by the accumulated weld bead. However, the poor forming accuracy of WAAM usually leads to severe dimensional deviation between the as-built and the predesigned structures. This paper aims to propose a visual sensing technology and deep learning–assisted WAAM method for fabricating metallic structure, to simplify the complex WAAM process and improve the forming accuracy. Design/methodology/approach Instead of slicing of the workpiece modeling and generating all the welding torch paths in advance of the fabricating process, this method is carried out by adding the feature point regression branch into the Yolov5 algorithm, to detect the feature point from the images of the as-built structure. The coordinates of the feature points of each deposition layer can be calculated automatically. Then the welding torch trajectory for the next deposition layer is generated based on the position of feature point. Findings The mean average precision score of modified YOLOv5 detector is 99.5%. Two types of overhanging structures have been fabricated by the proposed method. The center contour error between the actual and theoretical is 0.56 and 0.27 mm in width direction, and 0.43 and 0.23 mm in height direction, respectively. Originality/value The fabrication of circular overhanging structures without using the complicate slicing strategy, turning table or other extra support verified the possibility of the robotic WAAM system with deep learning technology.
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