REAL-TIME IMAGE PATTERN SENSOR FOR WELD POOL PENETRATION THROUGH REFLECTION IN GTAW

In gas tungsten arc welding (GTAW), weld pool surface contains crucial information for welding development. In this research, simulate skilled welders to control the welding process and determine the penetration stages based on the weld pool reaction. This study focuses on solving the uncertainty of the liquid weld pool in joint bases. The weld pool penetration process is highly depending on how the weld pool surface shape. To observe the weld pool, reflect the weld pool surface by the laser and image on the shield glass. The experiments show that the penetration can’t be determine by the reflecting grayness due to the variability of base metal. To control the joint bases diversity, fed a tip of the wire after the arc is established. Crate the new pattern of the weld pool penetration. Experiments verified the feasibility of this method

REAL-TIME SENSING AND CONTROL OF DEVELOPING WELD PENETRATION THROUGH REFLECTION VIBRATION IN GTAW

GTAW (Gas Tungsten Arc Welding) weld pool surface is believed to contain sufficient information to determine the weld penetration, from which skilled welders are able to control the welding process to desired penetration states. However, it is unclear how human welders extract the weld penetration from the observed weld pool surface. In this research, a novel method is studied to determine the weld penetration based on the dynamic change of the weld pool surface. This study observes/measures/analyzes the development of a weld pool from partial to full penetration and correlates such change to the weld penetration. Similar trends in the weld pool surface are observed when the weld penetration changes from partial to full penetration despite the amperage used and material welded. Correlating the weld pool surface reflecting grayness and the development of the weld penetration from experiments shows: (1) the weld pool reflection intensity will increase while the weld penetration is increasing; (2) the increasing trends of weld pool reflection intensity will decrease when the full penetration is achieved; (3) the weld pool reflection intensity will increase after the full penetration is achieved. Such trend in the weld pool surface reflection intensity when the weld penetration develops is used as feedback signal to detect the weld pool penetration. To control the weld pool penetration, a first-order dynamic model is identified. Model Predictive Control (MPC) is used to control the weld penetration. Experiments verified the feasibility of this proposed method and established system

A Tutorial on Learning Human Welder\u27s Behavior: Sensing, Modeling, and Control

Human welder\u27s experiences and skills are critical for producing quality welds in manual GTAW process. Learning human welder\u27s behavior can help develop next generation intelligent welding machines and train welders faster. In this tutorial paper, various aspects of mechanizing the welder\u27s intelligence are surveyed, including sensing of the weld pool, modeling of the welder\u27s adjustments and this model-based control approach. Specifically, different sensing methods of the weld pool are reviewed and a novel 3D vision-based sensing system developed at University of Kentucky is introduced. Characterization of the weld pool is performed and human intelligent model is constructed, including an extensive survey on modeling human dynamics and neuro-fuzzy techniques. Closed-loop control experiment results are presented to illustrate the robustness of the model-based intelligent controller despite welding speed disturbance. A foundation is thus established to explore the mechanism and transformation of human welder\u27s intelligence into robotic welding system. Finally future research directions in this field are presented

Gas Tungsten Arc Welding with Synchronized Magnetic Oscillation

The search for improvements in mechanized/automated welding techniques has been intense due to skilled labour shortage. In this line, the combination of operational modes (polarity and/or metal transfer mode) within a process has gained attention, since it expands the adjustability of the arc energy. By combining this feature with arc motion, the arc energy delivered to the workpiece can be optimally distributed. Therefore, this work exploits the synchronization between arc magnetic oscillation and gas tungsten arc welding (GTAW) process to control weld bead formation. A system was devised to control the magnetic oscillation and a welding power source synchronously. Characterization of the synchronized magnetic oscillation was carried out based on high-speed filming and electrical data. The welding process was then synchronized with the magnetic oscillation varying the level of welding current according to the arc time-position, being the effect on weld bead width considered for analysis. Welding without oscillation and with unsynchronized magnetic oscillation were taken as references. The synchronized magnetic oscillation made possible to achieve larger weld bead width on the side with higher current level and longer lateral stop time and vice versa. This technique might be beneficial to applications where extreme weld bead control is required

TOWARD INTELLIGENT WELDING BY BUILDING ITS DIGITAL TWIN

To meet the increasing requirements for production on individualization, efficiency and quality, traditional manufacturing processes are evolving to smart manufacturing with the support from the information technology advancements including cyber-physical systems (CPS), Internet of Things (IoT), big industrial data, and artificial intelligence (AI). The pre-requirement for integrating with these advanced information technologies is to digitalize manufacturing processes such that they can be analyzed, controlled, and interacted with other digitalized components. Digital twin is developed as a general framework to do that by building the digital replicas for the physical entities. This work takes welding manufacturing as the case study to accelerate its transition to intelligent welding by building its digital twin and contributes to digital twin in the following two aspects (1) increasing the information analysis and reasoning ability by integrating deep learning; (2) enhancing the human user operative ability to physical welding manufacturing via digital twins by integrating human-robot interaction (HRI). Firstly, a digital twin of pulsed gas tungsten arc welding (GTAW-P) is developed by integrating deep learning to offer the strong feature extraction and analysis ability. In such a system, the direct information including weld pool images, arc images, welding current and arc voltage is collected by cameras and arc sensors. The undirect information determining the welding quality, i.e., weld joint top-side bead width (TSBW) and back-side bead width (BSBW), is computed by a traditional image processing method and a deep convolutional neural network (CNN) respectively. Based on that, the weld joint geometrical size is controlled to meet the quality requirement in various welding conditions. In the meantime, this developed digital twin is visualized to offer a graphical user interface (GUI) to human users for their effective and intuitive perception to physical welding processes. Secondly, in order to enhance the human operative ability to the physical welding processes via digital twins, HRI is integrated taking virtual reality (VR) as the interface which could transmit the information bidirectionally i.e., transmitting the human commends to welding robots and visualizing the digital twin to human users. Six welders, skilled and unskilled, tested this system by completing the same welding job but demonstrate different patterns and resulted welding qualities. To differentiate their skill levels (skilled or unskilled) from their demonstrated operations, a data-driven approach, FFT-PCA-SVM as a combination of fast Fourier transform (FFT), principal component analysis (PCA), and support vector machine (SVM) is developed and demonstrates the 94.44% classification accuracy. The robots can also work as an assistant to help the human welders to complete the welding tasks by recognizing and executing the intended welding operations. This is done by a developed human intention recognition algorithm based on hidden Markov model (HMM) and the welding experiments show that developed robot-assisted welding can help to improve welding quality. To further take the advantages of the robots i.e., movement accuracy and stability, the role of the robot upgrades to be a collaborator from an assistant to complete a subtask independently i.e., torch weaving and automatic seam tracking in weaving GTAW. The other subtask i.e., welding torch moving along the weld seam is completed by the human users who can adjust the travel speed to control the heat input and ensure the good welding quality. By doing that, the advantages of humans (intelligence) and robots (accuracy and stability) are combined together under this human-robot collaboration framework. The developed digital twin for welding manufacturing helps to promote the next-generation intelligent welding and can be applied in other similar manufacturing processes easily after small modifications including painting, spraying and additive manufacturing

Investigating the potential of magnetic are oscillated GMWA - welding for hard surfacing applications

The flux cored arc welding process has some characteristic features and is therefore established in the industry to fabricate hardfacing. One advantage of the process is the possibility to vary the alloy content of the weld metal by manipulating the chemical composition of the filler material. Moreover, it is possible to produce self shielding electrodes, which show advantages for outdoor applications. One the other hand this process creates high dilution rates. One possibility to avoid this effect is to apply a magnetic field in order to deflect the welding arc. In this thesis, the influence of a transversal magnetic field on the weld seam formation during FCAW was investigated. An increase of the weld width and a simultaneous decrease of the penetration depth was achieved at a certain deflection. The influence of the magnetic oscillation was found to be stronger for short circuit mode than for pulsed mode. Furthermore, high frequencies in combination with a high magnetic flux density caused a reduced process stability and consequently a worsening of the weld bead appearance. Apart from that, the drop detachment was inhibited, when a strong magnetic field was applied during pulsed and spray mode.Das Metallschutzgasschweißen mit Fülldrahtelektrode ist ein in der Industrie verbreitetes Verfahren zur Herstellung von Hartpanzerungen. Charakteristisch ist die gezielte Beeinflussung des Schweißprozesses und der chemischen Zusammensetzung des Schweißguts durch die Auswahl der Füllstoffe. Des Weiteren können dem Fülldraht Stoffe hinzugegeben werden, wodurch das Schweißen ohne Schutzgas durchgeführt werden kann. Nachteilig ist jedoch der hohe Aufmischungsgrad bei diesem Verfahren. Eine Möglichkeit, diesem Effekt entgegenzuwirken, besteht darin, den Lichtbogen durch das Anlegen eines Magnetfelds in eine oszillierende Bewegung zu versetzen. Der Einfluss eines oszillierenden transversalen Magnetfeldes auf die Nahtausbildung beim MSG-Schweißen mit Fülldraht wurde untersucht. Es zeigt sich dabei, dass der Einbrand verbreitert und die Einbrandtiefe durch die gezielte Auslenkung verringert werden konnte. Der Einfluss des magnetischen Pendelns ist beim Kurzschlusslichtbogen kleiner als beim Impulslichtbogen. Bei hohen Frequenzen und hoher Flussdichte wurde außerdem eine Verringerung der Prozessstabilität und eine daraus resultierende Verschlechterung der Schweißnahtausbildung festgestellt. Außerdem führte ein starker Einfluss des Magnetfelds zu einer erschwerten Tropfenablösung beim Impluls- und Sprühlichtbogen.Tesi

REFLECTED IMAGE PROCESSING FOR SPECULAR WELD POOL SURFACE MEASUREMENT

The surface of the weld pool contains information that can be exploited to emulate a skilled human welder to better understand and control the welding process. Of the existing techniques, the method that uses the pool\u27s specular nature to an advantage and which is relatively more cost effective, and suitable for welding environment is the one that utilizes reflected images to reconstruct 3D weld pool surface by using structured light and image processing techniques. In this thesis, an improvement has been made to the existing method by changing welding direction to obtain a denser reflected dot-matrix pattern allowing more accurate surface measurement. Then, the reflected images, obtained by capturing the reflection of a structured laser dot-matrix pattern from the pool surface through a high-speed camera with a narrow band-pass filter, are processed by a newly proposed algorithm to find the position of each reflected dot relative to its actual projection dot. This is a complicated process owing to the increased density of dots and noise induced due to the harsh environment. The obtained correspondence map may later be used by a surface reconstruction algorithm to derive the three-dimensional pool surface based on the reflection law

Virtualized Welding Based Learning of Human Welder Behaviors for Intelligent Robotic Welding

Combining human welder (with intelligence and sensing versatility) and automated welding robots (with precision and consistency) can lead to next generation intelligent welding systems. In this dissertation intelligent welding robots are developed by process modeling / control method and learning the human welder behavior. Weld penetration and 3D weld pool surface are first accurately controlled for an automated Gas Tungsten Arc Welding (GTAW) machine. Closed-form model predictive control (MPC) algorithm is derived for real-time welding applications. Skilled welder response to 3D weld pool surface by adjusting the welding current is then modeled using Adaptive Neuro-Fuzzy Inference System (ANFIS), and compared to the novice welder. Automated welding experiments confirm the effectiveness of the proposed human response model. A virtualized welding system is then developed that enables transferring the human knowledge into a welding robot. The learning of human welder movement (i.e., welding speed) is first realized with Virtual Reality (VR) enhancement using iterative K-means based local ANFIS modeling. As a separate effort, the learning is performed without VR enhancement utilizing a fuzzy classifier to rank the data and only preserve the high ranking “correct” response. The trained supervised ANFIS model is transferred to the welding robot and the performance of the controller is examined. A fuzzy weighting based data fusion approach to combine multiple machine and human intelligent models is proposed. The data fusion model can outperform individual machine-based control algorithm and welder intelligence-based models (with and without VR enhancement). Finally a data-driven approach is proposed to model human welder adjustments in 3D (including welding speed, arc length, and torch orientations). Teleoperated training experiments are conducted in which a human welder tries to adjust the torch movements in 3D based on his observation on the real-time weld pool image feedback. The data is off-line rated by the welder and a welder rating system is synthesized. ANFIS model is then proposed to correlate the 3D weld pool characteristic parameters and welder’s torch movements. A foundation is thus established to rapidly extract human intelligence and transfer such intelligence into welding robots