Bestard Guillermo Alvarez, Sampaio Renato Coral, Vargas José A R, Alfaro Sadek C Absi
Postgraduate Program in Mechatronic Systems (PPMEC), Faculty of Technology, University of Brasilia, Campus Darcy Ribeiro, Brasilia 70910-900, Brazil.
Department of Automatic Control, Institute of Cybernetics, Mathematics and Physics, Havana 10400, Cuba.
Sensors (Basel). 2018 Mar 23;18(4):962. doi: 10.3390/s18040962.
The arc welding process is widely used in industry but its automatic control is limited by the difficulty in measuring the weld bead geometry and closing the control loop on the arc, which has adverse environmental conditions. To address this problem, this work proposes a system to capture the welding variables and send stimuli to the Gas Metal Arc Welding (GMAW) conventional process with a constant voltage power source, which allows weld bead geometry estimation with an open-loop control. Dynamic models of depth and width estimators of the weld bead are implemented based on the fusion of thermographic data, welding current and welding voltage in a multilayer perceptron neural network. The estimators were trained and validated off-line with data from a novel algorithm developed to extract the features of the infrared image, a laser profilometer was implemented to measure the bead dimensions and an image processing algorithm that measures depth by making a longitudinal cut in the weld bead. These estimators are optimized for embedded devices and real-time processing and were implemented on a Field-Programmable Gate Array (FPGA) device. Experiments to collect data, train and validate the estimators are presented and discussed. The results show that the proposed method is useful in industrial and research environments.
弧焊工艺在工业中广泛应用，但其自动控制受到焊缝几何形状测量困难以及电弧控制回路闭合问题的限制，电弧所处环境条件恶劣。为解决这一问题，本文提出一种系统，用于采集焊接变量并向配备恒压电源的气体保护金属极电弧焊（GMAW）传统工艺发送激励信号，该系统可通过开环控制估计焊缝几何形状。基于热成像数据、焊接电流和焊接电压在多层感知器神经网络中的融合，实现了焊缝深度和宽度估计器的动态模型。利用一种新开发的用于提取红外图像特征的算法所获取的数据，对估计器进行离线训练和验证，采用激光轮廓仪测量焊缝尺寸，并通过对焊缝进行纵向切割的图像处理算法测量深度。这些估计器针对嵌入式设备和实时处理进行了优化，并在现场可编程门阵列（FPGA）设备上实现。本文展示并讨论了用于收集数据、训练和验证估计器的实验。结果表明，所提出的方法在工业和研究环境中均有用处。
