焊丝电弧增材制造中喷嘴到工件距离的测量与控制

Raven T. Reisch, T. Hauser, Jürgen Franke, F. Heinrich, Konstantinos Theodorou, T. Kamps, Alois Knoll
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引用次数: 3

摘要

在多轴电弧增材制造中,保持正确的喷嘴到工件的距离对于避免碰撞和工艺缺陷至关重要。测量这一距离是具有挑战性的,因为焊接电弧使传统距离测量的使用复杂化,而不需要在过程中进行位置偏移。因此,本研究调查并评估了几种传感器(导线馈送传感器、电流和电压传感器、麦克风、焊接相机、光谱仪、结构声学传感器)用于方向独立的过程测量的使用情况。基于领域知识提取特征,并通过相关性分析选择特征。在计算波长为960 nm的相对强度时,光谱仪(Pearson’s r = - 0.90)对稳定工艺参数的测量结果最稳健,其次是焊接相机(Pearson’s r = 0.84),使用卷积神经网络对图像进行分析。在此基础上,建立了闭环控制系统。由于系统辨识表明,与送丝速度相比,焊接速度对轨道高度的影响较大(Pearson’s r−0.90 < >−0.16),因此通过对焊接速度的简单p控制来实现闭环控制。该方法可实现多轴沉积路径的多层多芯零件的制造。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Nozzle-to-Work Distance Measurement and Control in Wire Arc Additive Manufacturing
In multi-axes Wire Arc Additive Manufacturing, keeping the correct nozzle-to-work distance is crucial to avoid collisions and process defects. Measuring this distance is challenging as the welding arc complicates the usage of conventional distance measurements without positional offset in-process. For that reason, this study investigated and evaluated the usage of several sensors (wire feed sensor, current and voltage sensor, microphone, welding camera, spectrometer, structural acoustic sensor) for a direction independent in-process measurement. Features were extracted based on domain knowledge and selected by means of a correlation analysis. The spectrometer (Pearson’s r = −0.90) showed the most robust measurements for stable process parameters when computing the relative intensity at a wavelength of 960 nm, followed by the welding camera (Pearson’s r = 0.84) when analyzing the images with a convolutional neural network. Based on the findings, a closed-loop-control was created. As a system identification revealed a high impact of the welding speed on the track height in comparison to the wire feed rate (Pearson’s r − 0.90 < > − 0.16), the closed-loop-control was realized by means of a simple P-control for the welding speed. The proposed approach enabled the manufacturing of multi-layer multi-bead parts with multi-axes deposition paths.
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