Monitoring Of Welding Research Articles

Study on welding phenomena observation method based on arc and molten pool light emission characteristics in visible and infrared wavelength region – development of image sensing technology for in-process welding monitoring technology

ABSTRACT Arc welding is important technology for manufacturing that is directly linked to quality and productivity. Especially GMAW with Ar or CO2 shielding gas is widely used in various industries because of its high productivity. To achieve high quality and productivity, in-process welding quality control technology will be required. In this study, the authors study the welding phenomena observing technology for online detection of welding defects by using image sensing method. By selecting the transmission wavelength of band-path filter 1320 nm, which is in the wavelength range where the brightness of arc light is weak and the brightness of molten pool light is high, welding phenomena just near arc and molten pool can be observed clearly.

수직 GMA 용접부에서 화상처리를 위한 추출 알고리즘에 대한 연구

A welding monitoring system is indispensable for ensuring high-quality weldments in the vertical gas metal arc (GMA) process, and techniques for analyzing the arc characteristics by extracting welding arc lines from the welds are required. In this study, we have developed intelligent and effective algorithms for image processing in GMA welding based on thermal high-speed cameras. The proposed algorithms were verified and compared, to obtain the optimal one for each image-processing step. Finally, the validity of the proposed algorithms was examined using welding arc line images obtained under different welding environments. The results proved that the performance of the proposed algorithm was excellent in eliminating variable noise and extracting the feature points and centerline for vertical GMA welding, and can be employed for general industrial applications.

Modelling and control of weld height reinforcement in the GMAW process

In a recent research related to technological application in monitoring and control of welding, the use of various approaches to improve the productivity and quality through the development of techniques and automatic control systems is observed. This paper presents a methodology for modelling and control of the weld height reinforcement, allowing adjusting the process parameters, which can be implemented in the field of research or, in certain cases, in the industrial sector as an approach to the control of weld bead geometries. This was developed for the geometric characteristic studied in an integrated system of images acquisition, modelling and control system. The weld bead formation is measured through an acquisition system and processing of images using a low-cost web cam. The wire feed speed, the welding speed and voltage are predicted using an artificial neural network based on the desired weld height reinforcement. The control system is based on fuzzy logic.

Instant detection of porosity in gas metal arc welding by using probability density distribution and control chart

A novel porosity detection technique from the voltage and current transients is introduced in this paper. An online weld monitoring that detects the porosity at an earlier stage is much demanding in the industry due to their adverse effects on structural integrity. In this research work, control chart and probability density distribution have been employed as tools to detect arc instability and weld porosity. The results showed that the pattern of probability density distribution changes for the defect and defect-free welds significantly. The mean and standard deviation control charts plotted with voltage clearly distinguished the quality of the weld based on sample points spread within or outside the control limits. For minute internal porosities, the sample points at the corresponding region in the standard deviation control chart were outside the limits whereas it is well within the control limits in the mean control chart. Inspector can predict the presence and near location of porosity using these tools by simple mathematical calculations easily and instantly. The results proved that the developed approach is successful and promising for the weld inspection.

Real-time monitoring of high-power disk laser welding based on support vector machine

In this paper, an efficient quality monitoring system for monitoring high-power disk laser welding in real time was developed. Fifteen features of laser-induced metal vapor plume and spatters were extracted and support vector machine was adopted to establish a classifier to evaluate the welding quality. Feature selection method was employed to choose suitable features. The experiment results demonstrated that this method had satisfactory performance and could be applied to real-time monitoring application.

Defects diagnosis in laser brazing using near-infrared signals based on empirical mode decomposition

Real-time monitoring of laser welding plays a very important role in the modern automated production and online defects diagnosis is necessary to be implemented. In this study, the status of laser brazing was monitored in real time using an infrared photoelectric sensor. Four kinds of braze seams (including healthy weld, unfilled weld, hole weld and rough surface weld) along with corresponding near-infrared signals were obtained. Further, a new method called Empirical Mode Decomposition (EMD) was proposed to analyze the near-infrared signals. The results showed that the EMD method had a good performance in eliminating the noise on the near-infrared signals. And then, the correlation coefficient was developed for selecting the Intrinsic Mode Function (IMF) more sensitive to the weld defects. A more accurate signal was reconstructed with the selected IMF components. Simultaneously, the spectrum of selected IMF components was solved using fast Fourier transform, and the frequency characteristics were clearly revealed. The frequency energy of different frequency bands was computed to diagnose the defects. There was a significant difference in four types of weld defects. This approach has been proved to be an effective and efficient method for monitoring laser brazing defects.

Real-time Monitoring for Disk Laser Welding Based on Feature Selection and SVM

In order to automatically evaluate the welding quality during high-power disk laser welding, a real-time monitoring system was developed. The images of laser-induced metal vapor during welding were captured and fifteen features were extracted. A feature selection method based on a sequential forward floating selection algorithm was employed to identify the optimal feature subset, and a support vector machine (SVM) classifier was built to recognize the welding quality. The experiment results demonstrated that this method had satisfactory performance, and could be applied in laser welding monitoring applications.

Quality assessment of resistance spot welding process based on dynamic resistance signal and random forest based

A scheme for online quality monitoring of resistance spot welding (RSW) process is proposed to effectively determine the rate of spot weld quality. In this work, the random forest (RF) classification featuring with dynamic resistance (DR) signals which were collected and processed in the production environment was carried out. The obtained results demonstrated that the constructed RF model based on DR profile features adequately distinguished high-quality welds from the other unacceptable welds such as inadequate sized welds and expulsions. Variable importance evaluation of RF was implemented against the input features. It showed that two DR slopes for nugget nucleation and growth (v 2 , v 3 ) and dynamic resistance (R γ ) in the final half cycle play the most significant roles in achieving more accurate results of classification, while absolute gradient ∇ max is useful in detecting minor expulsion from pull-out failure. In addition, shunting effect in consecutive welds was tentatively investigated via the DR curves, accounting for noticeable declines in the stage I of DR. The results revealed that shunted welds beyond minimum weld spacing do not significantly undermine the accuracy of classification. The implementation of RF based on the combination of welding parameters and DR features improves the accuracy of classification (98.8%) with ntree = 1000 and mtry = 4, as weld current significantly distinguished situations where DR features solely achieve accuracy (93.6%). The incorporation of the RF technique into online monitoring system attains a satisfying RSW quality classification accuracy and reduces the workload on destructive tests.

Quality monitoring of spot welding with advanced signal processing and data-driven techniques

As resistance spot welding is a crucial and widely used metal joining technique nowadays, a cheap and highly accurate online quality monitoring scheme is strongly demanded in industry. In this paper, a novel framework for welding quality examination is proposed by using advanced signal processing and artificial intelligence techniques. Our proposed framework consists of two objective schemes, a general welding quality classification scheme and a more detailed and advanced welding quality estimation scheme. To achieve a fast, convenient and cheap monitoring strategy, only the easily obtained electrical signals are monitored for data acquisition. For the welding quality classification, a self-organizing map under a windowed feature extraction is applied. Results of the spot welding experiment from a portable welding machine show that the classification accuracy can be 92.9%. For welding quality estimation, variation of the welding time and detailed aspects of welding quality are introduced. In particular, a modified recurrent neural network is utilized for the size estimation of the heat affected zone, while a novel self-organizing map type classifier is used to detect the expulsion condition along with its occurrence time. During the spot welding experiments, the average error percentage of the heat affected zone size estimation is around 6.7% and the accuracy on expulsion detection reaches 93.3%.

Torque based defect detection and weld quality modelling in friction stir welding process

Efforts have been made towards the monitoring of friction stir welding (FSW) process using real-time torque signals in this research work. Signals were analyzed using discrete wavelet transform and statistical features namely dispersion, asymmetry and excess are computed. The computed features are further processed to develop effective methodology for internal defect identification in FSW process. A new indicator has been proposed combining the computed statistical features. The proposed indicator shows appreciable deviations for defective and defect free welds. Apart from defect detection using the computed signal features, they are also presented as inputs to a support vector machine learning based modelling tool for the prediction of ultimate tensile strength of the welded joints. The prediction accuracy of the model with computed signal features are found to be more than the model developed with process parameters. The comparison of the developed support vector regression (SVR) model with artificial neural network (ANN) model and general regression model yields that prediction performance of SVR is superior to ANN and general regression model. The proposed work can be modified for its successful use in real-time modelling of friction stir welding process.

奥氏体不锈钢光纤激光同轴保护焊接的实时监测

奥氏体不锈钢光纤激光同轴保护焊接的实时监测

Optical system for on line monitoring of welding: a machine learning approach for optimal set up

<p class="Abstract">In this paper a methodology is described for continuous checking of the settings of a low cost vision system for automatic geometrical measurement of welding embedded on components of complicated shape. The measurement system is based on a laser sheet. Measuring conditions and the corresponding uncertainty are analyzed by evaluating their p-value and its closeness to an optimal measurement configuration also when working conditions are changed. The method aims to check the holding of optimal measuring conditions by using a machine learning approach for the vision system: based on a such methodology single images can be used to check the settings, therefore allowing a continuous and on line monitoring of the optical measuring system capabilities.</p><p class="Abstract">According to this procedure, the optical measuring system is able to reach and to hold uncertainty levels adequate for automatic dimensional checking of welding and of defects, taking into account the effects of system hardware/software incorrect settings and environmental effects, like varying lighting conditions. The paper also studies the effects of process variability on the method for quantitative evaluation, in order to propose on line solutions for this system.</p>

Prediction of the Vickers Microhardness and Ultimate Tensile Strength of AA5754 H111 Friction Stir Welding Butt Joints Using Artificial Neural Network.

A simulation model was developed for the monitoring, controlling and optimization of the Friction Stir Welding (FSW) process. This approach, using the FSW technique, allows identifying the correlation between the process parameters (input variable) and the mechanical properties (output responses) of the welded AA5754 H111 aluminum plates. The optimization of technological parameters is a basic requirement for increasing the seam quality, since it promotes a stable and defect-free process. Both the tool rotation and the travel speed, the position of the samples extracted from the weld bead and the thermal data, detected with thermographic techniques for on-line control of the joints, were varied to build the experimental plans. The quality of joints was evaluated through destructive and non-destructive tests (visual tests, macro graphic analysis, tensile tests, indentation Vickers hardness tests and t thermographic controls). The simulation model was based on the adoption of the Artificial Neural Networks (ANNs) characterized by back-propagation learning algorithm with different types of architecture, which were able to predict with good reliability the FSW process parameters for the welding of the AA5754 H111 aluminum plates in Butt-Joint configuration.

Weldability and Monitoring of Resistance Spot Welding of Q&P and TRIP Steels

This work aims at investigating the spot weldability of a new advanced Quenching and Partitioning (Q&P) steel and a Transformation Induced Plasticity (TRIP) steel for automotive applications by evaluating the effects of the main welding parameters on the mechanical performance of their dissimilar spot welds. The welding current, the electrode tip voltage and the electrical resistance of sheet stack were monitored in order to detect any metal expulsion and to evaluate its severity, as well as to clarify its effect on spot strength. The joint strength was assessed by means of shear and cross tension tests. The corresponding fracture modes were determined through optical microscopy. The welding current is the main process parameter that affects the weld strength, followed by the clamping force and welding time. Metal expulsion can occur through a single large expulsion or multiple expulsions, whose effects on the shear and cross tension strength have been assessed. Longer welding times can limit the negative effect of an expulsion if it occurs in the first part of the joining process. The spot welds exhibit different fracture modes according to their strengths. Overall, a proper weldability window for the selected process parameters has been determined to obtain sound joints.

OCT for Process Monitoring of Laser Transmission Welding

AbstractNon‐destructive monitoring of laser transmission welding of polymers is a crucial topic in different production areas. Process parameter deviation may result in product failure. Current monitoring often relies on post process or inline methods which are not able to give a qualitative process feedback. Optical coherence tomography (OCT) enables tomographic measurements of transparent material structures (weld seam and defects; Fig. 1) with a few microns resolution. It provides high accuracy for monitoring and quality assurance of pre, inline and post process situations.

Process monitoring of friction stir welding via the frequency of the spindle motor current

Friction stir welding is a solid-state process that is gaining preference for the joining of metals with low melting points. Despite the clear advantages of friction stir welding over traditional fusion welding, voids within the weld seam arise when improper conditions are present. The work presented in this article examines the development of an automated process monitoring system for friction stir welding. The system indirectly monitors the welding torque through the supplied current to the spindle motor. To measure the current, a clamp-on current meter was used. Our results have shown that using a simple and inexpensive clamp-on current meter provides good insight into the welding torque. Examination focused on the frequency spectrum of the current. A Fourier transform decomposed the signal into various frequencies present. The results consistently showed that when no void was present, there was a component of the current’s frequency at 14 Hz. However, when the tool encountered a void, the frequency spectrum changed. The component at 14 Hz went away while content in the range of 1–4 Hz increased.

Embedded processing methods for online visual analysis of laser welding

Online monitoring and closed-loop control of laser welding offer great possibilities for achieving better weld quality. Earlier work on visual laser welding monitoring has mainly focused on aluminum and fairly thin steel used, for example, in car production. We extend this work by focusing on the automated analysis of the phenomena present in the laser welding of thick steel, where all of the phenomena related to the weld quality are still not well understood or controlled. This paper presents the implementation, test results and analysis for weld monitoring methods implemented on a compact smart camera system. The applied embedded sensor–processor platform allows for high-speed implementation of image capture and dynamic range compression, real-time seam tracking and spatter feature extraction. The paper describes experimental results from implemented real-time algorithms for seam tracking and spatter extraction and additional off-line analysis of methods for spatter tracking and seam widening detection, which are also feasible for future online hardware implementation. The results suggest that it is possible to integrate a compact laser welding analysis system, which achieves analysis rates that are sufficient for real-time process control.

Implementation of Welding Defects Detection and Monitoring in Arc Welding Robots

Implementation of Welding Defects Detection and Monitoring in Arc Welding Robots

Monitoring of laser welding using source localization and tracking processing by microphone array

Microphone is subject to the interference of working acoustic sources from surroundings. It is often difficult to acquire useful acoustic signal from the hostile environment. This problem has become the obstacle to use the acoustic signal for monitoring laser welding process. To monitor the weld quality effectively, a novel monitoring strategy on source localization and tracking of laser source is investigated in this work. A plane microphone array system composed of eight microphones has been applied to capture the acoustic signal. A time delay recognition has been employed to source localization and tracking. These results reveal that this system with the processing method is capable of distinguishing welding defects in laser welding process. By visualizing the position of laser source, it can directly judge and distinguish the weld quality.

A simple and accurate solution based on a laser sheet system for on line position monitoring of welding

A methodology is presented able to check on line that optimal settings of a vision system are reached and maintained for automatic geometrical measurement welding having a complicated shape. The simple optical measurement system is based on a laser sheet. The set calibration methodology appears very sensitive to little deviations from optimum setting and allows the measurement system to reach level of uncertainty adequate for automatic dimensional checking of welding, even though a simple measurement system is used. The effects of process variability on the method are also studied and quantitatively evaluated; practical solutions for on line use of the methodology are finally discussed.