Real-Time Weld Penetration Depth Monitoring With Laser Ultrasonic Sensing System

2005 ◽  
Vol 128 (1) ◽  
pp. 280-286 ◽  
Author(s):  
Bao Mi ◽  
Charles Ume
Keyword(s):  
Real Time ◽  
Penetration Depth ◽  
Weld Pool ◽  
Phased Array ◽  
Welding Process ◽  
Analytical Relationship ◽  
Correlation Technique ◽  
Weld Penetration ◽  
Weld Quality ◽  
Sensing System

A real-time ultrasound-based system for controlling robotic weld quality by monitoring the weld pool is presented. The weld penetration depth is one of the most important geometric parameters that define weld quality, hence, remains a key control quantity. The sensing system is based on using a laser phased array technique to generate focused and steered ultrasound, and an electromagnetic acoustic transducer (EMAT) as a receiver. When a pulsed laser beam is incident on the surface of a condensed matter, either the thermoelastic expansion or ablation induces mechanical vibrations that propagate as ultrasound within the specimen. Both the ultrasound generation by the laser phased array and the reception by the EMAT are noncontact, which eliminates the need for a couplant medium. They are capable of operating at high temperatures involved in the welding process. The ultrasound generated by the laser phased array propagates through the weld pool and is picked up by the EMAT receiver. A signal-processing algorithm based on a cross-correlation technique has been developed to estimate the time-of-flight (TOF) of the ultrasound. The relationship between the TOF and the penetration depth of the weld has been established experimentally and analytically. The analytical relationship between the TOF and the penetration depth, which is obtained by the ray-tracing algorithm and geometric analysis, agrees well with the experimental measurements.

scholarly journals Deep Learning-Based Detection of Penetration from Weld Pool Reflection Images

2020 ◽  
Vol 99 (9) ◽  
pp. 239s-245s
Author(s):  
CHAO LI ◽  
◽  
QIYUE WANG ◽  
WENHUA JIAO ◽  
MICHAEL JOHNSON ◽  
...  
Keyword(s):  
Weld Pool ◽  
Data Augmentation ◽  
Welding Process ◽  
Descent Method ◽  
Machine Learning Techniques ◽  
Gradient Descent Method ◽  
Neural Net ◽  
Weld Penetration ◽  
Process Data ◽  
Pool Surface

An innovative method was proposed to determine weld joint penetration using machine learning techniques. In our approach, the dot-structured laser images reflected from an oscillating weld pool surface were captured. Experienced welders typically evaluate the weld penetration status based on this reflected laser pattern. To overcome the challenges in identifying features and accurately processing the images using conventional machine vision algorithms, we proposed the use the raw images without any processing as the input to a convolutional neural network (CNN). The labels needed to train the CNN were the measured weld penetration states, obtained from the images on the backside of the workpiece as a set of discrete weld penetration categories. The raw data, images, and penetration state were generated from extensive experiments using an automated robotic gas tungsten arc welding process. Data augmentation was performed to enhance the robustness of the trained network, which led to 270,000 training examples, 45,000 validation examples, and 45,000 test examples. A six-layer convolutional neural net-work trained with a modified mini-batch gradient descent method led to a final testing accuracy of 90.7%. A voting mechanism based on three continuous images increased the classification accuracy to 97.6%.

On-Line Estimation of Laser Weld Penetration

1997 ◽  
Vol 119 (4) ◽  
pp. 791-801 ◽  
Author(s):  
Jay F. Tu ◽  
Kishore N. Lankalapalli ◽  
Mark Gartner ◽  
Keng H. Leong
Keyword(s):  
Penetration Depth ◽  
Finite Size ◽  
Depth Estimation ◽  
Process Models ◽  
Bottom Surface ◽  
Weld Penetration ◽  
Weld Quality ◽  
Laser Weld ◽  
Production Environment ◽  
On Line

High-power CO2 laser welding has been widely used in the industry because of its high productivity and excellent weld quality. In order to tap the potential of this process completely, it is important to have on-line weld quality inspection methods to improve the process productivity and reliability by achieving 100 percent weld inspection. Weld penetration is one of the most important factors critical to the quality of a laser weld. However, it is very difficult to directly measure the extent of penetration without sectioning the workpiece. In this paper a model-based penetration depth estimation technique suitable for the production environment is developed. The proposed model relates the temperature measured on the bottom surface of the workpiece, weld bead width, laser beam power and welding speed to penetration depth. The closed-loop depth estimator combines the model and a model-error compensator to compensate for the uncertainty in the measurement of the laser power and absorptivity. Other effects considered are the averaging due to the finite size of the sensor, delay based on the sensor location and the process and sensor dynamics. Several bead-on-plate and butt welds were made on low carbon steel plates to validate the static process models and the depth estimation scheme. Temperatures on the bottom surface of the workpiece during welding were measured using infrared thermocouples. The welds were sectioned longitudinally to obtain the penetration profile. The penetration profiles estimated by the depth estimator matched satisfactorily with the measured penetration profiles. The results validate the capability of the proposed depth estimator to estimate penetration depth and its ability to trace the dynamic changes in penetration depth.

scholarly journals In-Process Monitoring of Lack of Fusion in Ultra-Thin Sheets Edge Welding Using Machine Vision

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2411 ◽  
Author(s):  
Yuxiang Hong ◽  
Baohua Chang ◽  
Guodong Peng ◽  
Zhang Yuan ◽  
Xiangchun Hou ◽  
...  
Keyword(s):  
Real Time ◽  
Weld Pool ◽  
Process Monitoring ◽  
Welding Process ◽  
Thin Sheets ◽  
Real Time Control ◽  
Image Processing Algorithm ◽  
Monitoring Method ◽  
Lack Of Fusion ◽  
Time Control

Lack of fusion can often occur during ultra-thin sheets edge welding process, severely destroying joint quality and leading to seal failure. This paper presents a vision-based weld pool monitoring method for detecting a lack of fusion during micro plasma arc welding (MPAW) of ultra-thin sheets edge welds. A passive micro-vision sensor is developed to acquire clear images of the mesoscale weld pool under MPAW conditions, continuously and stably. Then, an image processing algorithm has been proposed to extract the characteristics of weld pool geometry from the acquired images in real time. The relations between the presence of a lack of fusion in edge weld and dynamic changes in weld pool characteristic parameters are investigated. The experimental results indicate that the abrupt changes of extracted weld pool centroid position along the weld length are highly correlated with the occurrences of lack of fusion. By using such weld pool characteristic information, the lack of fusion in MPAW of ultra-thin sheets edge welds can be detected in real time. The proposed in-process monitoring method makes the early warning possible. It also can provide feedback for real-time control and can serve as a basis for intelligent defect identification.

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.

Auto-Control of Defocus Distance during Filling Laser Welding of Ti-Alloy with Wire

2011 ◽  
Vol 287-290 ◽  
pp. 2456-2459 ◽  
Author(s):  
Xin Song Chen ◽  
Wei Yao ◽  
Ai Qin Duan
Keyword(s):  
Control System ◽  
Laser Welding ◽  
Real Time ◽  
Welding Process ◽  
Numerical Control ◽  
Ti Alloy ◽  
Numerical Control System ◽  
Weld Quality ◽  
The Stability

The defocus distance is one of the most important factors during filling laser welding with wire. Keeping defocus distances stability is the pre-condition to acquire the stable weld quality. In this paper, a QUANTA laser camera was used to measure the defocus distance, and it is compensated in real time through the numerical control system. The results reveal that the fixed defocus distances can be acquired using this system. And the experiments by using auto-controlling method of the defocus distance show that the stability of welding process can be improved greatly and the good weld quality can be easy to be obtained.

scholarly journals CHARACTERIZATION OF PHOTODIODES FOR DETECTION OF VARIATIONS IN PART-TO-PART GAP AND WELD PENETRATION DEPTH DURING REMOTE LASER WELDING OF COPPER-TO-STEEL BATTERY TAB CONNECTORS

2021 ◽  
pp. 1-34
Author(s):  
Giovanni Chianese ◽  
Pasquale Franciosa ◽  
Jonas Nolte ◽  
Dariusz Ceglarek ◽  
Stanislao Patalano
Keyword(s):  
Laser Welding ◽  
Penetration Depth ◽  
Energy Intensity ◽  
Low Cost ◽  
Welding Process ◽  
Thin Foil ◽  
Supervised Machine Learning ◽  
Weld Penetration ◽  
Thin Foils ◽  
Remote Laser Welding

Abstract This paper addresses sensor characterization to detect variations in part-to-part gap and weld penetration depth using photodiode-based signals during Remote Laser Welding (RLW) of battery tab connectors. Photodiode-based monitoring has been implemented largely for structural welds due to its relatively low cost and ease of automation. However, research in sensor characterization, monitoring and diagnosis of weld defects during joining of battery tab connectors is at an infancy and results are inconclusive. Motivated by the high variability during the welding process of dissimilar metallic thin foils, this paper aims to characterize the signals generated by a photodiode-based sensor to determine whether variations in weld quality can be isolated and diagnosed. Photodiode-based signals were collected during RLW of copper-to-steel thin-foil lap joint (Ni-plated copper 300 μm to Ni-plated steel 300 μm). The presented methodology is based on the evaluation of the energy intensity and scatter level of the signals. The energy intensity gives information about the amount of radiation emitted during the welding process, and the scatter level is associated with the accumulated and un-controlled variations. Findings indicated that part-to-part gap variations can be diagnosed by observing the step-change in the plasma signal, with no significant contribution given by the back-reflection. Results further suggested that over-penetration corresponds to significant increment of the scatter level in the sensor signals. Opportunities for automatic isolation and diagnosis of defective welds based on supervised machine learning are discussed.

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