Real-Time Sensing of Sag Geometry During GTA Welding

1997 ◽  
Vol 119 (2) ◽  
pp. 151-160 ◽  
Author(s):  
Y. M. Zhang ◽  
R. Kovacevic
Keyword(s):  
Real Time ◽  
Vision System ◽  
Structured Light ◽  
Gta Welding ◽  
Seam Tracking ◽  
Image Processing Algorithm ◽  
3D Vision ◽  
Full Penetration ◽  
Laser Stripe ◽  
Penetration Control

Seam tracking and weld penetration control are two fundamental issues in automated welding. Although the seam tracking technique has matured, the latter still remains a unique unsolved problem. It was found that the full penetration status during GTA welding can be determined with sufficient accuracy using the sag depression. To achieve a new full penetration sensing technique, a structured-light 3D vision system is developed to extract the sag geometry behind the pool. The laser stripe, which is the intersection of the structured-light and weldment, is thinned and then used to acquire the sag geometry. To reduce possible control delay, a small distance is selected between the pool rear and laser stripe. An adaptive dynamic search for rapid thinning of the stripe and the maximum principle of slope difference for unbiased recognition of sag border were proposed to develop an effective real-time image processing algorithm for sag geometry acquisition. Experiments have shown that the proposed sensor and image algorithm can provide reliable feedback information of sag geometry for the full penetration control system.

Real-Time Projection Mapping Using High-Frame-Rate Structured Light 3D Vision

2015 ◽  
Vol 8 (4) ◽  
pp. 265-272 ◽  
Author(s):  
Jun CHEN ◽  
Takashi YAMAMOTO ◽  
Tadayoshi AOYAMA ◽  
Takeshi TAKAKI ◽  
Idaku ISHII
Keyword(s):  
Real Time ◽  
Structured Light ◽  
Frame Rate ◽  
3D Vision ◽  
Projection Mapping ◽  
High Frame Rate ◽  
Time Projection

scholarly journals Indoor Key Point Reconstruction Based on Laser Illumination and Omnidirectional Vision

2020 ◽  
Vol 24 (7) ◽  
pp. 864-871
Author(s):  
Yang Qi ◽  
◽  
Yuan Li
Keyword(s):  
Vision System ◽  
Structured Light ◽  
Three Dimensional ◽  
Mathematical Representation ◽  
Omnidirectional Vision ◽  
Wide Field ◽  
3D Vision ◽  
Imaging Model ◽  
Key Points ◽  
Indoor Scenes

Efficient and precise three-dimensional (3D) measurement is an important issue in the field of machine vision. In this paper, a measurement method for indoor key points is proposed with structured lights and omnidirectional vision system and the system can achieve the wide field of view and accurate results. In this paper, the process of obtaining indoor key points is as follows: Firstly, through the analysis of the system imaging model, an omnidirectional vision system based on structured light is constructed. Secondly, the full convolution neural network is used to estimate the scene for the dataset. Then, according to the geometric relationship between the scenery point and its reference point in structured light, for obtaining the 3D coordinates of the unstructured light point is presented. Finally, combining the full convolution network model and the structured light 3D vision model, the 3D mathematical representation of the key points of the indoor scene frame is completed. The experimental results proved that the proposed method can accurately reconstruct indoor scenes, and the measurement error is about 2%.

High-Frame-Rate Structured Light 3-D Vision for Fast Moving Objects

2014 ◽  
Vol 26 (3) ◽  
pp. 311-320 ◽  
Author(s):  
Yongjiu Liu ◽  
◽  
Hao Gao ◽  
Qingyi Gu ◽  
Tadayoshi Aoyama ◽  
...  
Keyword(s):  
Fast Moving ◽  
Moving Objects ◽  
Vision System ◽  
Structured Light ◽  
Gray Code ◽  
Frame Rate ◽  
3D Vision ◽  
High Frame Rate ◽  
Depth Images ◽  
Structured Light Vision

<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00260003/04.jpg"" width=""300"" />HFR 3D vision system</span></div> This paper presents a fast motion-compensated structured-light vision system that realizes 3-D shape measurement at 500 fps using a high-frame-rate camera-projector system. Multiple light patterns with an 8-bit gray code, are projected on the measured scene at 1000 fps, and are processed in real time for generating 512 × 512 depth images at 500 fps by using the parallel processing of a motion-compensated structured-light method on a GPU board. Several experiments were performed on fast-moving 3-D objects using the proposed method. </span>

Machine Vision Recognition of Weld Pool in Gas Tungsten Arc Welding

1995 ◽  
Vol 209 (2) ◽  
pp. 141-152 ◽  
Author(s):  
R Kovacevic ◽  
Y M Zhang
Keyword(s):  
Machine Vision ◽  
Real Time ◽  
Weld Pool ◽  
Visual Information ◽  
Vision System ◽  
Tracking Error ◽  
Seam Tracking ◽  
Front Face ◽  
Welding Parameters ◽  
Weld Penetration

The weld pool and its surrounding area can provide a human welder with sufficient visual information to control welding quality. Seam tracking error and pool geometry can be recognized by a skilled human welder and then utilized to adjust the welding parameters. However, for machine vision, accurate real-time recognition of weld pool geometry is a difficult task due to the high intensity arc light, even though seam tracking errors can be detected. A novel vision system is, therefore, used to acquire quality images against the arc. A real-time recognition algorithm is proposed to analyse the image and recognize the pool geometry based on the pattern recognition technique. Despite surface impurity and other influences, the pool geometry can always be recognized with sufficient accuracy in 150 ms under different welding conditions. To explore the potential application of machine vision in weld penetration control, experiments are conducted to show the correlation between pool geometry and weld penetration state. Thus, pool recognition also provides a possible technique for front-face sensing of the weld penetration.

scholarly journals Vision/Position Hybrid Control for a Hexa Robot Using Bacterial Foraging Optimization in Real-time Pose Adjustment

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 564
Author(s):  
Ba-Phuc Huynh ◽  
Shun-Feng Su ◽  
Yong-Lin Kuo
Keyword(s):  
Real Time ◽  
Optimization Problem ◽  
Closed Loop ◽  
Hybrid Control ◽  
Vision System ◽  
Parallel Robot ◽  
Bacterial Foraging Optimization ◽  
3D Vision ◽  
End Effector ◽  
Bacterial Foraging

This paper presents a novel architecture of the vision/position hybrid control for a Hexa parallel robot. The 3D vision system is combined with the Proportional-Integral-Derivative (PID) position controller to form a two-level closed-loop controller of the robot. The 3D vision system measures the pose of the end-effector after the PID control. The measurement of the 3D vision system is used as a feedback of the second closed-loop control. The 3D vision system has a simple structure using two fixed symmetric cameras at the top of the robot and four planar colored markers on the surface of the end-effector. The 3D vision system detects and reconstructs the 3D coordinates of colored markers. Based on the distance and coplanarity constraints of the colored markers, the optimization problem is modeled for the real-time adjustment, which is implemented during the operation of the robot to minimize the measurement error of the 3D vision system due to both the initial calibration of the stereo camera and the external noise affecting image processing. The bacterial foraging optimization is appropriately configured to solve the optimization problem. The experiment is performed on a specific Hexa parallel robot to assess the effectiveness and feasibility of the proposed real-time adjustment using the bacterial foraging optimization. The experimental result shows that it has high accuracy and fast computation time although the experiment is conducted on a laptop with an average hardware configuration. An experimental comparison of the performance between the proposed method and another control method is also implemented. The results show the superiority and application potential of the proposed method.

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