Adaptive Control by Convolutional Neural Network in Plasma Arc Welding System

Abstract

Plasma arc welding (PAW) was employed in joining thick materials with groove. Due to the high-density plasma arc, keyhole welding was used in the butt welding. The gap might be taken in places due to the heat distortion during the welding. To achieve a high-quality welding, the adaptive control is required according to the gap. The authors tried to apply CMOS camera to obtain information from the top surface and achieve synchronization between the camera shutter and welding current. Thus, a clear image of the weld pool with the keyhole and the gap was taken. The brightness distribution in the front of the weld pool becomes uneven due to the plasma arc. To classify the gap and select the welding conditions, such as the welding current and the plasma gas flow rate, the authors used one of the deep learning algorithms, convolutional neural network (CNN). In the training and testing data, the performance of the CNN was found to be satisfactory. However, if the plasma torch does not trace the welding line, the pool image will be different from the training image. Hence, the CNN will not be able to estimate the correct gap. To avoid this, the authors detected the welding line from the pool image and selected the image area near the welding line as the input of the CNN, i.e., the input area is selected based on the image obtained during the welding. The validity of the proposed method was verified by the welding experiments.

Fault Diagnosis Method of Distribution Equipment Based on Hybrid Model of Robot and Deep Learning

“…In addition, RPN network adopts anchor mechanism, which not only solves the problem of translation invariance, but also enables R-FCN algorithm to identify and locate targets with different overall dimensions. In the actual process of infrared image recognition of power distribution equipment, due to different equipment with different shape and structure, different sizes and variable aspect ratio, in order to ensure that there are targets in the receptive field corresponding to each sliding window on the feature map, multiscale anchor is required to ensure that the candidate frame is as complete as possible to select the target [25]. In the implementation of RPN network anchor, multiscale anchor can be obtained by setting the area of reference window (base_size), different area multiples and anchor aspect ratio, so that RPN can give more accurate foreground recommendation area.…”

Possibilities of Artificial Intelligence-Enabled Feedback Control System in Robotized Gas Metal Arc Welding

“…Artificial intelligence (AI) has been typically applied to evaluate the weld quality, possible inconclusions, and weld bead dimensions [2,3], and has achieved penetration [4][5][6] during welding. Different AI-based algorithms can be applied to process sensor data automatically, to enable the possibility of real-time analysis and process control during welding [6][7][8][9][10][11][12][13].…”

“…Manually defined parameter adjustment can be defined as a fully manually created parameter control library based on experiments or theory. Algorithm-based systems can be utilized as an example by using linear [12], curve-fitting [16] and model-free [17] adaptive controls. These control methods are effective ways to control the welding process.…”

Visual Sensing And Controlling of The Keyhole In Robotic Plasma Arc Welding

“…Image registration was conducted and showed keyhole entrance and topside weld pool behaved closely related to the keyhole exit evolution process [15]. Yamane applied convolutional neural network (CNN) to identify a possible gap in the image of weld pool [16]. Zhang proposed a controlled-pulse strategy to improve the stability and dynamics of keyhole [17].…”