Visual Sensing and Defect Detection of Gas Tungsten Arc Welding

Weld imperfections or defects such as incomplete penetration and lack of fusion are critical issues that affect the integration of welding components. The molten weld pool geometry is the major source of information related to the formation of these defects. In this dissertation, a new visual sensing system has been designed and set up to obtain weld pool images during GTAW. The weld pool dynamical behavior can be monitored using both active and passive vision method with the interference of arc light in the image significantly reduced through the narrow band pass filter and laser based auxiliary light source.Computer vision algorithms based on passive vision images were developed to measure the 3D weld pool surface geometry in real time. Specifically, a new method based on the reversed electrode image (REI) was developed to calculate weld pool surface height in real time. Meanwhile, the 2D weld pool boundary was extracted with landmarks detection algorithms. The method was verified with bead-on-plate and butt-joint welding experiments.Supervised machine learning was used to develop the capability to predict, in real-time, the incomplete penetration on thin SS304 plate with the key features extracted from weld pool images. An integrated self-adaptive close loop control system consisting the non-contact visual sensor, machine learning based defect predictor, and welding power source was developed for real-time welding penetration control for bead on plate welding. Moreover, the data driven methods were first applied to detect incomplete penetration and LOF in multi-pass U groove welding. New features extracted from reversed electrode image played the most important role to predict these defects. Finally, real time welding experiments were conducted to verify the feasibility of the developed models

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

Welding Processes

Despite the wide availability of literature on welding processes, a need exists to regularly update the engineering community on advancements in joining techniques of similar and dissimilar materials, in their numerical modeling, as well as in their sensing and control. In response to InTech's request to provide undergraduate and graduate students, welding engineers, and researchers with updates on recent achievements in welding, a group of 34 authors and co-authors from 14 countries representing five continents have joined to co-author this book on welding processes, free of charge to the reader. This book is divided into four sections: Laser Welding; Numerical Modeling of Welding Processes; Sensing of Welding Processes; and General Topics in Welding

MACHINE VISION RECOGNITION OF THREE-DIMENSIONAL SPECULAR SURFACE FOR GAS TUNGSTEN ARC WELD POOL

Observing the weld pool surface and measuring its geometrical parameters is a key to developing the next-generation intelligent welding machines that can mimic a skilled human welder who observes the weld pool to adjust welding parameters. It also provides us an effective way to improve and validate welding process modeling. Although different techniques have been applied in the past few years, the dynamic specular weld pool surface and the strong weld arc complicate these approaches and make the observation /measurement difficult. In this dissertation, a novel machine vision system to measure three-dimensional gas tungsten arc weld pool surface is proposed, which takes advantage of the specular reflection. In the designed system, a structured laser pattern is projected onto the weld pool surface and its reflection from the specular weld pool surface is imaged on an imaging plane and recorded by a high-speed camera with a narrow band-pass filter. The deformation of the molten weld pool surface distorts the reflected pattern. To derive the deformed surface of the weld pool, an image processing algorithm is firstly developed to detect the reflection points in the reflected laser pattern. The reflection points are then matched with their respective incident rays according to the findings of correspondence simulations. As a result, a set of matched incident ray and reflection point is obtained and an iterative surface reconstruction scheme is proposed to derive the three-dimensional pool surface from this set of data based on the reflection law. The reconstructed results proved the effectiveness of the system. Using the proposed surface measurement (machine vision) system, the fluctuation of weld pool surface parameters has been studied. In addition, analysis has been done to study the measurement error and identify error sources in order to improve the measurement system for better accuracy. The achievements in this dissertation provide a useful guidance for the further studies in on-line pool measurement and welding quality control

Machine-human Cooperative Control of Welding Process

An innovative auxiliary control system is developed to cooperate with an unskilled welder in a manual GTAW in order to obtain a consistent welding performance. In the proposed system, a novel mobile sensing system is developed to non-intrusively monitor a manual GTAW by measuring three-dimensional (3D) weld pool surface. Specifically, a miniature structured-light laser amounted on torch projects a dot matrix pattern on weld pool surface during the process; Reflected by the weld pool surface, the laser pattern is intercepted by and imaged on the helmet glass, and recorded by a compact camera on it. Deformed reflection pattern contains the geometry information of weld pool, thus is utilized to reconstruct its $3$D surface. An innovative image processing algorithm and a reconstruction scheme have been developed for (3D) reconstruction. The real-time spatial relations of the torch and the helmet is formulated during welding. Two miniature wireless inertial measurement units (WIMU) are mounted on the torch and the helmet, respectively, to detect their rotation rates and accelerations. A quaternion based unscented Kalman filter (UKF) has been designed to estimate the helmet/torch orientations based on the data from the WIMUs. The distance between the torch and the helmet is measured using an extra structure-light low power laser pattern. Furthermore, human welder\u27s behavior in welding performance has been studied, e.g., a welder`s adjustments on welding current were modeled as response to characteristic parameters of the three-dimensional weld pool surface. This response model as a controller is implemented both automatic and manual gas tungsten arc welding process to maintain a consistent full penetration

Engineering Principles

Over the last decade, there has been substantial development of welding technologies for joining advanced alloys and composites demanded by the evolving global manufacturing sector. The evolution of these welding technologies has been substantial and finds numerous applications in engineering industries. It is driven by our desire to reverse the impact of climate change and fuel consumption in several vital sectors. This book reviews the most recent developments in welding. It is organized into three sections: “Principles of Welding and Joining Technology,” “Microstructural Evolution and Residual Stress,” and “Applications of Welding and Joining.” Chapters address such topics as stresses in welding, tribology, thin-film metallurgical manufacturing processes, and mechanical manufacturing processes, as well as recent advances in welding and novel applications of these technologies for joining different materials such as titanium, aluminum, and magnesium alloys, ceramics, and plastics

A three-dimensional wire-feeding model for heat and metal transfer, fluid flow, and bead shape in wire plasma arc additive manufacturing

A three-dimensional wire-feeding model has been developed to study the transient coupling behaviour of heat and metal transfer, fluid flow, and solidified bead shape in wire plasma arc additive manufacturing (WPAAM). A novel surface heat source model considering the arc energy shading effect is proposed and adopted. An improved momentum source of the arc force considering the arc pressure shading effect is also developed and used. This model has been used to study the metal transfer dynamics, flow patterns, and bead shape of the WPAAM process with a wire-feeding speed (WFS) of 1–5 m/min. The simulated results agreed reasonably with the experimental data. As the WFS increased from 1 to 5 m/min, three different metal transfer modes were observed, which changed from globular droplet mode to droplet-liquid bridge mode and solid-liquid bridge mode. Detailed metal transfer information was analysed, including metal transfer position, shape, average temperature, and main driving force. The effects of the arc shading and metal transfer on the melt pool dynamics and bead shape were simulated and discussed. A periodic flow pattern of the melt pool produced by the metal transfer impact causes ripples or even humping defects. As the WFS increased, the melt pool depression gradually disappeared due to the arc pressure shading effect. When the WFS increased to 5 m/min, a temperature drop of about 140 K in the central melt pool, caused by the arc energy shading effect and cold metal transfer, weakened the lateral flow significantly, which explained the decrease of bead width at a large WFS. The results demonstrate that the developed wire-feeding model and findings could be used as a theoretical tool and basis to better understand the underlying physical mechanisms and achieve bead shape control in the WAAM process

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

Monitoração e análises da penetração do cordão de solda atraves da observação da oscilação da poça de fusão no processo GMAW-S

Tese (doutorado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2018.A busca por métodos de produção com melhor controle da qualidade e maior produtividade tem impulsionado o uso de sistemas automatizados em processos industriais como a soldagem. Porém, tornar a soldagem eficiente e econômica, é necessário para reduzir o desperdício de material e tempo gasto na produção e ensaios de verificação de qualidade. Isso pode ser conseguido por meio de sistemas automatizados que substituam os soldadores especialistas e sejam capazes de prever a geometria do cordão de solda a partir dos parâmetros de soldagem – permitindo que um processo realizado com os parâmetros determinados forneça uma junta com as propriedades mecânicas desejadas. Durante anos, muito se tem feito no sentido de prever os problemas na soldagem com o intuito de torná-la um processo estável, capaz de efetuar uniões de peças com o mínimo de interferência humana. Dos vários sensores utilizados em processos de soldagem, ainda não há uma opção eficaz capaz de identificar, diretamente, as características do cordão obtido durante o processo. Esse é um fator limitante no controle do processo, pois somente é possível determinar as características do cordão após a realização da solda através de ensaios (destrutivos ou não), quando nenhuma ação de controle pode ser tomada. Este trabalho propõe o desenvolvimento de um sistema de monitoramento da poça de fusão em tempo real usado para obter imagens do comportamento da oscilação da poça durante a solda. Uma nova abordagem para este tipo de imagens é a utilização de um sistema de iluminação por laser do processo, de modo que uma imagem de alta qualidade natural da poça de fusão, eletrodo e cordão de solda possa ser obtida, dando detalhes da poça e arredores. Essa estratégia, independente de modelos pré-definidos do processo, permite controlar a penetração dos cordões de solda no processo GMAW no modo de transferência metálica por curto-circuito (GMAW-S). Para o modelo e controlador definiu-se a utilização de sistemas inteligentes focados diretamente nas medições da oscilação da poça de fusão e a estimação da penetração dos cordões de solda a partir dos parâmetros de processo. Finalmente, um modelo para relacionar a profundidade da penetração, a frequência de oscilação da poça com a formação e o padrão das escamas na superfície do cordão de solda é proposto.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPQ).The search for methods of production with better quality control and greater productivity has promoted the use of automated systems in industrial processes such as welding. However, make the welding efficient and economical; it is necessary to reduce the waste and time spent on the production and quality tests. This can be achieved by means of automated systems to replace those skilled welders and be able to predict the geometry of the weld bead as welding parameters - allowing a process performed with the determined parameters provide a joint with the desired mechanical properties. For years, much has been done to predict problems in welding in order to make it a stable process capable of making unions parts with minimal human interference. The various sensors used in welding processes, there is still no effective option able to identify, directly, the weld bead characteristics obtained during the process. This is a limiting factor in the process control, because only can be determined the weld bead characteristics after the completion of welding through testing (destructive or not) when no control action can be taken. This work proposes the development of a real-time weld pool monitoring system to obtain the images of the weld pool oscillation behavior during welding. A novel approach to this type of images is the use of a laser lighting system for illumination of the process, so that a high quality natural image of the weld pool, electrode and weld bead can be obtained, giving details of the weld pool and surrounding area. This strategy, regardless of predefined models, can control the weld bead penetration in the GMAW-S process. For the proposed model and controller is defined the use of intelligent systems focused on the measurements of the weld pool oscillations and the estimation of the weld bead penetration from the process parameters. Finally, a model to relate the weld penetration depth, the weld pool oscillation frequency with the formation and the pattern of the ripples on the weld bead surface is proposed