Tracking and height control in plasma robotic welding using digital CCD camera

Yamane

et al. 2022

Preprint

Self Cite

With an advantage of "single-sided welding and double-sided forming", plasma arc welding (PAW) has a great application potential in modern industrial production. The welding quality can be guaranteed by sensing and controlling of the keyhole. However, it is difficult to make an on-line observation on the back of base metal, and realize a dynamic registration of the visual sensor and welding torch. In this study, it has investigated the relationship between the welding condition and image feature of keyhole. Image processing is designed to obtain the feature image and conduct a template matching of the keyhole. The target feature of weld zone will be extracted and processed in real time. Besides, it has designed a digital controller for the welding robot and power source in this study, and discussed control method to stabilize the keyhole and achieve good welding quality. Eventually, experiments are conducted to inspect the comprehensive performance of the welding control system with varying disturbance. This study is of important significance for the visual sensing and controlling of the keyhole in PAW. It will provide technical support for the weld quality control, and promote the development of welding technology based on machine vision in intelligent manufacturing field.

Section: Introductionmentioning

confidence: 99%

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

Yamane

et al. 2022

Preprint

Self Cite

“…Thus, if the metal plates have no gap, the PAW quality depends on the tracking of the welding line on the base metals. 6) However, taking a clear image of the keyhole on the top surface using visual sensors, such as a charge-coupled device (CCD) camera and a complementary metal oxide semiconductor (CMOS) camera, is challenging. Visual sensors are equipment used in getting information.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Control by Convolutional Neural Network in Plasma Arc Welding System

Yamane

Matsuo

2020

ISIJ Int.

Self Cite

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.

“…Therefore, the researchers makes an improvement on the acquisition, analysis, processing and feedback of traffic information, which is different from the previous single fuzzy control [2], and changes its artificial analysis to intelligent analysis. Researchers statistical observation "electronic policeman" The popularity of applications on the road, found that in many of the city intersection assembly gathering traffic cameras already, although an "electronic police" is designed for manual monitoring, however, we might as well will be connected with the traffic signal lamp control system, and the application of CCD camera sensor technology [3,4]. The transformation of the signal lamp depends entirely on the artificial intelligence of science.…”

Section: Introductionmentioning

confidence: 99%

Traffic Signal Light Optimization Control Based on Fuzzy Control and CCD Camera Technology

Xing¹,

Lian²,

Yan³

et al. 2018

dtcse

This paper combines the fuzzy control technology with Charge Coupled Device (CCD) camera sensor technology to solve the defect of the existing traffic signal control method. CCD camera sensor technology can be real-time to receive traffic information, each intersection traffic conditions, the length of the traffic flow, traffic speed, and then through the signal processor calculates the time needed for traffic through the intersection at the moment, transfer to the fuzzy controller. At this point, the traffic situation of the other road also reached the fuzzy controller in real time. The fuzzy controller decides which intersection should continue to pass, and finally realize the traffic signal optimization control.