Double-Electrode Arc Welding Process: Principle, Variants, Control and Developments

Double-electrode gas metal arc welding (DE-GMAW) is a novel welding process in which a second electrode, non-consumable or consumable, is added to bypass part of the wire current. The bypass current reduces the heat input in non-consumable DE-GMAW or increases the deposition rate in consumable DE-GMAW. The fixed correlation of the heat input with the deposition in conventional GMAW and its variants is thus changed and becomes controllable. At the University of Kentucky, DE-GMAW has been tested/developed by adding a plasma arc welding torch, a GTAW (gas tungsten arc welding) torch, a pair of GTAW torches, and a GMAW torch. Steels and aluminum alloys are welded and the system is powered by one or multiple power supplies with appropriate control methods. The metal transfer has been studied at the University of Kentucky and Shandong University resulting in the desirable spray transfer be obtained with less than 100 A base current for 1.2 mm diameter steel wire. At Lanzhou University of Technology, pulsed DE-GMAW has been successfully developed to join aluminum/magnesium to steel. At the Adaptive Intelligent Systems LLC, DE-GMAW principle has been applied to the submerged arc welding (SAW) and the embedded control systems needed for industrial applications have been developed. The DE-SAW resulted in 1/3 reduction in heat input for a shipbuilding application and the weld penetration depth was successfully feedback controlled. In addition, the bypass concept is extended to the GTAW resulting in the arcing-wire GTAW which adds a second arc established between the tungsten and filler to the existing gas tungsten arc. The DE-GMAW is extended to double-electrode arc welding (DE-AW) where the main electrode may not necessarily to be consumable. Recently, the Beijing University of Technology systematically studied the metal transfer in the arcing-wire GTAW and found that the desired metal transfer modes may always be obtained from the given wire feed speed by adjusting the wire current and wire position/orientation appropriately. A variety of DE-AW processes are thus available to suit for different applications, using existing arc welding equipment

ESTABLISHING THE FOUNDATION TO ROBOTIZE COMPLEX WELDING PROCESSES THROUGH LEARNING FROM HUMAN WELDERS BASED ON DEEP LEARNING TECHNIQUES

As the demand for customized, efficient, and high-quality production increases, traditional manufacturing processes are transforming into smart manufacturing with the aid of advancements in information technology, such as cyber-physical systems (CPS), the Internet of Things (IoT), big data, and artificial intelligence (AI). The key requirement for integration with these advanced information technologies is to digitize manufacturing processes to enable analysis, control, and interaction with other digitized components. The integration of deep learning algorithm and massive industrial data will be critical components in realizing this process, leading to enhanced manufacturing in the Future of Work at the Human-Technology Frontier (FW-HTF). This work takes welding manufacturing as the case study to accelerate its transition to intelligent welding by robotize a complex welding process. By integrate process sensing, data visualization, deep learning-based modeling and optimization, a complex welding system is established, with the systematic solution to generalize domain-specific knowledge from experienced human welder. Such system can automatically perform complex welding processes that can only be handled by human in the past. To enhance the system\u27s tracking capabilities, we trained an image segmentation network to offer precise position information. We incorporated a recurrent neural network structure to analyze dynamic variations during welding. Addressing the challenge of human heterogeneity in data collection, we conducted experiments illustrating that even inaccurate datasets can effectively train deep learning models with zero mean error. Fine-tuning the model with a small portion of accurate data further elevates its performance

Development of an acoustic emission monitoring system for crack detection during arc welding

Condition monitoring techniques are employed to monitor the structural integrity of a structure or the performance of a process. They are used to evaluate the structural integrity including damage initiation and propagation in engineering components. Early damage detection, maintenance and repairs can prevent structural failures, reduce maintenance and replacement costs, and guarantee that the structure runs securely during its service life. Acoustic emission (AE) technology is one of the condition monitoring methods widely employed in the industry. AE is an attractive option for condition monitoring purposes, the number of industrial applications where is used is rising. AE signals are elastic stress waves created by the fast release of energy from local sources occurring inside of materials, e.g. crack initiating and propagating. The AE technique includes recording this phenomenon with piezoelectric sensors, which is mounted on the surface of a structure. The signals are subsequently analysed in order to extract useful information about the nature of the AE source. AE has a high sensitivity to crack propagation and able to locate AE activity sources. It is a passive approach. It listens to the elastic stress waves releasing from material and able to operate in real-time monitoring to detect both cracks initiating and propagating. In this study, the use of AE technology to detect and monitor the possible occurrence of cracking during the arc welding process has been investigated. Real-time monitoring of the automated welding operation can help increase productivity and reliability while reducing cost. Monitoring of welding processes using AE technology remains a challenge, especially in the field of real-time data analysis, since a large amount of data is generated during monitoring. Also, during the welding process, many interferences can occur, causing difficulties in the identifications of the signals related to cracking events. A significant issue in the practical use of the AE technique is the existence of independent sources of a signal other than those related to cracking. These spurious AE signals make the discovering of the signals from the cracking activity difficult. Therefore, it is essential to discriminate the signal to identify the signal source. The need for practical data analysis is related to the three main objectives of monitoring, which is where this study has focused on. Firstly, the assessment of the noise levels and the characteristics of the signal from different materials and processes, secondly, the identification of signals arising from cracking and thirdly, the study of the feasibility of online monitoring using the AE features acquired in the initial study. Experimental work was carried out under controlled laboratory conditions for the acquisition of AE signals during arc welding processing. AE signals have been used for the assessment of noise levels as well as to identify the characteristics of the signals arising from different materials and processes. The features of the AE signals arising from cracking and other possible signal sources from the welding process and environment have also collected under laboratory conditions and analysed. In addition to the above mentioned aspects of the study, two novel signal processing methods based on signal correlation have been developed for efficiently evaluating data acquired from AE sensors. The major contributions of this research can be summarised as follows. The study of noise levels and filtering of different arc welding processes and materials is one of the areas where the original contribution is identified with respect to current knowledge. Another key contribution of the present study is the developing of a model for achieving source discrimination. The crack-related signals and other signals arising from the background are compared with each other. Two methods that have the potential to be used in a real-time monitoring system have been considered based on cross-correlation and pattern recognition. The present thesis has contributed to the improvement of the effectiveness of the AE technique for the detection of the possible occurrence of cracking during arc welding

Analyses of Online Monitoring Signals for a GMAW Process Before and After Improvement

The ability to detect the onset of welding instability is a very powerful tool in welding process monitoring and control. Toward this goal, this study investigates a gas metal arc welding (GMAW) process by analyzing online monitoring signals. Two separate data sets are obtained from the process, which correspond to (a) a stable process after improvement and (b) a relatively unstable process which tends to exhibit spatter and poor weld bead geometry. Voltage, current, wire-feeding speed and line speed signals for both data sets are analysed and features are extracted from the raw signals using different signal processing techniques. Specifically, phase diagrams, signal distributions, Fast Fourier transform (FFT) and Wavelet Transform methodologies are implemented. The process parameters differ for the data corresponding to the stable and unstable processes rendering the two data sets incomparable. As such, an overlapping region of parameters is selected and this data is used to develop a multi-layer neural network model. The model uses the features extracted to distinguish between the two data sets under the similar input conditions. The trained model is then used to classify data as being from a stable process or an unstable process. Finally, an ant colony optimization algorithm is used to select the optimal subset of features for the classification model. For this task, fuzzy k-nearest neighbor algorithm is used as the classifier instead due to the computational simplicity. The results indicate that more than one single feature is able to yield 100% classification accuracy alone. A way to rank those features is discussed. Moreover, the effect of window size is also investigated

VISION BASED REAL-TIME MONITORING AND CONTROL OF METAL TRANSFER IN LASER ENHANCED GAS METAL

Laser enhanced gas metal arc welding (GMAW) is a novel welding process where a laser is applied to provide an auxiliary detaching force to help detach the droplet such that welds may be made in gas tungsten arc welding high quality at GMAW high speeds. The current needed to generate the electromagnetic (detaching) force is thus reduced. The reduction in the current helps reduce the impact on the weld pool and over-heat fumes/smokes. However, in the previous studies, a continuous laser is applied. Since the auxiliary is only needed each time the droplet needs to be detached and the detachment time is relatively short in the transfer cycle, the laser energy is greatly wasted in the rest of the transfer cycle. In addition, the unnecessary application of the laser on the droplet causes additional over-heat fumes. Hence, this study proposes to use a pulsed laser such that the peak pulse is applied only when the droplet is ready to detach. To this end, the state of the droplet development needs to be closely monitored in real-time. Since the metal transfer is an ultra-high speed process and the most reliable method to monitor should be based on visual feedback, a high imaging system has been proposed to monitor the real-time development of the droplet. A high-speed image processing system has been developed to real-time extract the developing droplet. A closed-loop control system has been established to use the real-time imaging processing result on the monitoring of the developing droplet to determine if the laser peak pulse needs to be applied. Experiments verified the effectiveness of the proposed methods and established system. A controlled novel process – pulsed laser-enhanced GMAW - is thus established for possible applications in producing high-quality welds at GMAW speeds

An Overview of Arc Welding Control Systems

In this paper an overview of arc welding control systems is given. At first some basic physical background of the process is given. Then the two most important subtypes Gas tungsten arc welding (GTAW) and Gas metal arc welding (GMAW) are presented in some detail. In order to understand the logic of feedback control systems, the most essential control theory is outlined shortly. In the overview of control systems a feedback signal is used a means of division. The analysis of recent research papers in the area has shown that recently image processing based control systems seem to be the most popular ones

REAL-TIME MODEL PREDICTIVE CONTROL OF QUASI-KEYHOLE PIPE WELDING

Quasi-keyhole, including plasma keyhole and double-sided welding, is a novel approach proposed to operate the keyhole arc welding process. It can result in a high quality weld, but also raise higher demand of the operator. A computer control system to detect the keyhole and control the arc current can improve the performance of the welding process. To this effect, developing automatic pipe welding, instead of manual welding, is a hot research topic in the welding field. The objective of this research is to design an automatic quasi-keyhole pipe welding system that can monitor the keyhole and control its establishment time to track the reference trajectory as the dynamic behavior of welding processes changes. For this reason, an automatic plasma welding system is proposed, in which an additional electrode is added on the back side of the workpiece to detect the keyhole, as well as to provide the double-side arc in the double-sided arc welding mode. In the automatic pipe welding system the arc current can be controlled by the computer controller. Based on the designed automatic plasma pipe welding system, two kinds of model predictive controller − linear and bilinear − are developed, and an optimal algorithm is designed to optimize the keyhole weld process. The result of the proposed approach has been verified by using both linear and bilinear model structures in the quasi-keyhole plasma welding (QKPW) process experiments, both in normal plasma keyhole and double-sided arc welding modes

CONTROL OF METAL TRANSFER AT GIVEN ARC VARIABLES

Gas Metal Arc Welding (GMAW) is one of the most important welding processes in industrial application. To control metal transfer at given variables is a focus in the field of research and development in welding community. In this dissertation, laser enhanced GMAW is proposed and developed by adding a lower power laser onto the droplet to generate an auxiliary detaching force. The electromagnetic force needed to detach droplets, thus the current that determines this force, is reduced. Wire feed speed, arc voltage, and laser intensity were identified as three major parameters that affect the laser enhanced metal transfer process and a systematic series of experiments were designed and conducted to test these parameters. The behaviors of the laser enhanced metal transfer process observed from high speed images were analyzed using the established physics of metal transfer. In all experiments, the laser was found to affect the metal transfer process as an additional detaching force that tended to change a short-circuiting transfer to drop globular or drop spray, reduce the diameter of the droplet detached in drop globular transfer, or decrease the diameter of the droplet such that the transfer changed from drop globular to drop spray. The enhancement of the laser was found to increase as the laser intensity increased. The larger laser intensity tended to help reduce the size of the droplet detached. The arc voltage affected the metal transfer process through changing the current and changing the gap and possible time interval of the droplet development. A larger arc voltage helped reduce the size of the droplet detached through an increased electromagnetic force. Desired heat input and current/arc pressure waveforms may thus be both delivered and controlled by GMAW through laser enhancement. Laser recoil pressure force was estimated based on the difference of gravitational force with and without laser pulse, and the result was with an acceptable accuracy. Good formation of welds and full penetration of thin plate could be obtained using laser enhanced GMAW. A nonlinear model was established to simulate the dynamic metal transfer in laser enhanced GMAW, and the results agree with the experimental one