Model-free adaptive iterative learning control of melt pool width in wire arc additive manufacturing

© 2020, Springer-Verlag London Ltd., part of Springer Nature. Wire arc additive manufacturing (WAAM) is a Direct Energy Deposition (DED) technology, which utilize electrical arc as heat source to deposit metal material bead by bead to make up the final component. However, issues like the lack of assurance in accuracy, repeatability and stability hinder the further application in industry. Therefore, a Model Free Adaptive Iterative Learning Control (MFAILC) algorithm was developed to be applied in WAAM process in this study. The dynamic process of WAAM is modelled by adaptive neuro fuzzy inference system (ANFIS). Based on this ANFIS model, simulations are performed to demonstrate the effectiveness of MFAILC algorithm. Furthermore, experiments are conducted to investigate the tracking performance and robustness of the MFAILC controller. This work will help to improve the forming accuracy and automatic level of WAAM

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

Automatic Control of the Weld Bead Geometry

Automatic control of the welding process is complex due to its nonlinear and stochastic behavior and the difficulty for measuring the principal magnitudes and closing the control loop. Fusion welds involve melting and subsequent solidification of one or more materials. The geometry of the weld bead is a good indicator of the melting and solidification process, so its control is essential to obtain quality junctions. Different sensing, modeling, estimation, and control techniques are used to overcome this challenge, but most of the studies are using static single-input/single-output models of the process and focusing on the flat welding position. However, theory and practice demonstrate that dynamic models are the best representation to obtain satisfactory control performance, and multivariable techniques reduce the effect of interactions between control loops in the process. Also, many industrial applications need to control orbital welding. In this chapter, the above topics are discussed

END-TO-END PREDICTION OF WELD PENETRATION IN REAL TIME BASED ON DEEP LEARNING

Welding is an important joining technique that has been automated/robotized. In automated/robotic welding applications, however, the parameters are preset and are not adaptively adjusted to overcome unpredicted disturbances, which cause these applications to not be able to meet the standards from welding/manufacturing industry in terms of quality, efficiency, and individuality. Combining information sensing and processing with traditional welding techniques is a significant step toward revolutionizing the welding industry. In practical welding, the weld penetration as measured by the back-side bead width is a critical factor when determining the integrity of the weld produced. However, the back-side bead width is difficult to be directly monitored during manufacturing because it occurs underneath the surface of the welded workpiece. Therefore, predicting back-side bead width based on conveniently sensible information from the welding process is a fundamental issue in intelligent welding. Traditional research methods involve an indirect process that includes defining and extracting key characteristic information from the sensed data and building a model to predict the target information from the characteristic information. Due to a lack of feature information, the cumulative error of the extracted information and the complex sensing process directly affect prediction accuracy and real-time performance. An end-to-end, data-driven prediction system is proposed to predict the weld penetration status from top-side images during welding. In this method, a passive-vision sensing system with two cameras to simultaneously monitor the top-side and back-bead information is developed. Then the weld joints are classified into three classes (i.e., under penetration, desirable penetration, and excessive penetration) according to the back-bead width. Taking the weld pool-arc images as inputs and corresponding penetration statuses as labels, an end-to-end convolutional neural network (CNN) is designed and trained so the features are automatically defined and extracted. In order to increase accuracy and training speed, a transfer learning approach based on a residual neural network (ResNet) is developed. This ResNet-based model is pre-trained on an ImageNet dataset to process a better feature-extracting ability, and its fully connected layers are modified based on our own dataset. Our experiments show that this transfer learning approach can decrease training time and improve performance. Furthermore, this study proposes that the present weld pool-arc image is fused with two previous images that were acquired 1/6s and 2/6s earlier. The fused single image thus reflects the dynamic welding phenomena, and prediction accuracy is significantly improved with the image-sequence data by fusing temporal information to the input layer of the CNN (early fusion). Due to the critical role of weld penetration and the negligible impact on system implementation, this method represents major progress in the field of weld-penetration monitoring and is expected to provide more significant improvements during welding using pulsed current where the process becomes highly dynamic

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

Towards Intelligent Telerobotics: Visualization and Control of Remote Robot

Human-machine cooperative or co-robotics has been recognized as the next generation of robotics. In contrast to current systems that use limited-reasoning strategies or address problems in narrow contexts, new co-robot systems will be characterized by their flexibility, resourcefulness, varied modeling or reasoning approaches, and use of real-world data in real time, demonstrating a level of intelligence and adaptability seen in humans and animals. The research I focused is in the two sub-field of co-robotics: teleoperation and telepresence. We firstly explore the ways of teleoperation using mixed reality techniques. I proposed a new type of display: hybrid-reality display (HRD) system, which utilizes commodity projection device to project captured video frame onto 3D replica of the actual target surface. It provides a direct alignment between the frame of reference for the human subject and that of the displayed image. The advantage of this approach lies in the fact that no wearing device needed for the users, providing minimal intrusiveness and accommodating users eyes during focusing. The field-of-view is also significantly increased. From a user-centered design standpoint, the HRD is motivated by teleoperation accidents, incidents, and user research in military reconnaissance etc. Teleoperation in these environments is compromised by the Keyhole Effect, which results from the limited field of view of reference. The technique contribution of the proposed HRD system is the multi-system calibration which mainly involves motion sensor, projector, cameras and robotic arm. Due to the purpose of the system, the accuracy of calibration should also be restricted within millimeter level. The followed up research of HRD is focused on high accuracy 3D reconstruction of the replica via commodity devices for better alignment of video frame. Conventional 3D scanner lacks either depth resolution or be very expensive. We proposed a structured light scanning based 3D sensing system with accuracy within 1 millimeter while robust to global illumination and surface reflection. Extensive user study prove the performance of our proposed algorithm. In order to compensate the unsynchronization between the local station and remote station due to latency introduced during data sensing and communication, 1-step-ahead predictive control algorithm is presented. The latency between human control and robot movement can be formulated as a linear equation group with a smooth coefficient ranging from 0 to 1. This predictive control algorithm can be further formulated by optimizing a cost function. We then explore the aspect of telepresence. Many hardware designs have been developed to allow a camera to be placed optically directly behind the screen. The purpose of such setups is to enable two-way video teleconferencing that maintains eye-contact. However, the image from the see-through camera usually exhibits a number of imaging artifacts such as low signal to noise ratio, incorrect color balance, and lost of details. Thus we develop a novel image enhancement framework that utilizes an auxiliary color+depth camera that is mounted on the side of the screen. By fusing the information from both cameras, we are able to significantly improve the quality of the see-through image. Experimental results have demonstrated that our fusion method compares favorably against traditional image enhancement/warping methods that uses only a single image

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

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