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

Real-time detection of the aluminium contribution during laser welding of Usibor1500 tailor-welded blanks

The identification and intensity estimation of some aluminium emission lines have been proposed to perform an on-line quantification of the Al contribution to the laser-welding process of Usibor blanks. This boron steel is protected by an Al-Si coating that is removed by laser ablation before welding. If this process fails to remove Al from the joint surface, its contribution may affect the final properties of the resulting seams, therefore compromising their quality. Experimental tests have been performed, some of them in a real production scenario. They have been analysed and compared to the results of welding test specimens, analysis of the associated tensile properties and fracture locations and seam macrographs. These studies have indicated that on-line quantification of the Al contribution to the process is feasible and that a correlation can be established between the Al content estimated in real-time and the results derived from the off-line tests considered.The authors would like to thank the staff of Autotech Engineering and Solblank (both Gestamp companies) for their valuable help during the design, implementation and test of the monitoring system. This work has been supported by the project TEC2013- 47264-C2-1-

Sensor fusion to estimate the depth and width of the weld bead in real time in GMAW processes

The arc welding process is widely used in industry but its automatic control is limited by the difficulty in measuring the weld bead geometry and closing the control loop on the arc, which has adverse environmental conditions. To address this problem, this work proposes a system to capture the welding variables and send stimuli to the Gas Metal Arc Welding (GMAW) conventional process with a constant voltage power source, which allows weld bead geometry estimation with an open-loop control. Dynamic models of depth and width estimators of the weld bead are implemented based on the fusion of thermographic data, welding current and welding voltage in a multilayer perceptron neural network. The estimators were trained and validated off-line with data from a novel algorithm developed to extract the features of the infrared image, a laser profilometer was implemented to measure the bead dimensions and an image processing algorithm that measures depth by making a longitudinal cut in the weld bead. These estimators are optimized for embedded devices and real-time processing and were implemented on a Field-Programmable Gate Array (FPGA) device. Experiments to collect data, train and validate the estimators are presented and discussed. The results show that the proposed method is useful in industrial and research environments

Prédiction des attributs géométriques du joint de soudure dans le cas de soudage au laser par recouvrement de tôles en acier galvanisé : modèle 3D et réseaux de neurones

RÉSUMÉ: Le soudage au laser est une des techniques d'assemblage qui a révolutionné de nombreux secteurs industriels, y compris le secteur de l'industrie automobile, grâce à sa productivité et à sa flexibilité. En raison de la nature focalisée du faisceau laser et de sa puissance élevée, le soudage au laser se distingue des autres procédés conventionnels par un apport de chaleur, bref et localisé, favorisant la production de soudures étroites profondes et esthétiques avec des vitesses d'exécution pouvant atteindre plusieurs cm/s, une zone affectée par la chaleur très étroite et des distorsions thermiques limitées. Pour faire face aux contraintes de positionnement précis imposé dans le cas de soudage bout à bout et de soudage d'angle, la configuration de soudage par recouvrement s'avère être mieux adaptée pour la fabrication en grande séries. Cependant, le soudage par recouvrement des aciers galvanisés peut être instable et à cause de l'évaporation prématurée du recouvrement du zinc à l'interface des tôles superposées. Des précautions additionnelles sont nécessaires pour mettre en œuvre ce procédé de façon adéquate. Le choix d'un écart optimal entre les tôles à souder combiné à une sélection adéquate des paramètres du laser peuvent résoudre le problème de l'évaporation du zinc et produire des soudures de très grande qualité. Les propriétés mécaniques d'une soudure réalisée au laser découlent généralement de la forme et des dimensions de sa section transversale, qui dépendent elles-mêmes des paramètres du laser et des conditions de soudage telles que la puissance du laser, la vitesse d'avance du faisceau laser, le diamètre focal et l'écart entre les tôles. Pour exploiter efficacement les avantages du procédé, il faut développer une stratégie qui permet de contrôler les paramètres et les conditions de soudage pour obtenir des soudures avec les caractéristiques désirées, sans avoir recours à la lente et couteuse méthode traditionnelle essai-erreur. L'objectif principal de ce projet consiste à développer des modèles prédictifs permettant d'estimer les attributs géométriques du joint de soudure dans le cas de soudage au laser par recouvrement de tôles en acier galvanisé. L'approche proposée combine expérimentation, modélisation numérique, analyse statistique et modélisation par réseau de neurones pour produire le meilleur modèle prédictif possible. Cette approche est structurée en trois phases. La première phase a permis de réaliser une investigation expérimentale du procédé dans le but faire une l'évaluation qualitative et quantitative des effets des paramètres et conditions de soudage sur la variation des caractéristiques géométriques de la soudure. Les expériences ont été réalisées à l'aide d'un laser Nd-YAG 3KW à émission continue selon une planification d'expériences basée sur la méthode Taguchi. La seconde phase a permis de développer un modèle de simulation numérique 3D du procédé de soudage au laser basé sur la méthode des éléments finis dans le but de simuler le comportement du procédé dans des conditions difficiles à réaliser expérimentalement. Le modèle numérique s'appuie sur les équations de transfert thermique en tenant compte des propriétés du matériau dépendant de la température et de l'enthalpie de changement de phase. Le modèle de source de chaleur utilisé a été adapté de manière à modéliser simultanément le soudage en mode conduction et en mode trou de serrure. Les résultats de la première phase ont été utilisés pour la validation du modèle numérique 3D. Dans la troisième phase, on a développé et testé un modèle prédictif en utilisant les réseaux de neurones artificiels. Une large base de données combinant données expérimentales et données de simulation a servi à l'entrainement et à la validation de plusieurs versions de modèles. Plusieurs critères ont été utilisés pour sélectionner le meilleur modèle, pour l'évaluation de la qualité de ses prédictions et sa capacité de généralisation. Les résultats montrent que le modèle obtenu est un modèle de prédiction rapide et robuste présentant des prédictions compat bles avec les mesures expérimentales générant une erreur de prédiction moyenne ne dépassant pas les 7%. -- Mot(s) clé(s) en français : Soudage au laser par recouvrement, laser Nd-YAG, acier galvanisé à faible teneur en carbone, modèles prédictifs, planification d'expériences, méthode des éléments finis, réseau de neurones. -- ABSTRACT: Laser welding becomes more and more popular in many industrial fields, including the automotive industry, thanks to its high productivity and flexibility. Due to the focused nature of the laser beam and its high incident power, laser welding is well-known for its high and fast heat input, localized in a very small area, thus promoting the production of deep narrow and aesthetic welds with speeds of up to several cm /s, a very narrow heat affected zone and limited thermal distortions. To deal with the positioning constraints imposed on butt welding and fillet welding, the overlap welding configuration is best suited for large-scale fabrication, but the welding of galvanized steels in this configuration becomes unstable and requires additional precautions, because of the premature vaporization of the zinc coating at the interface of the overlapped parts. An optimal gap between the parts and a better combination of laser parameters can overcome this situation and produce defect free welds. The mechanical properties of a laser weld seam depend on the shape and dimensions of its cross-section, which themselves depend on the laser parameters and the welding conditions, namely laser power, welding speed, focal diameter and gap. To effectively exploit the benefits of the process, a strategy must be developed to control welding parameters and conditions to achieve welds with desired characteristics, avoiding the slow and expensive traditional test-fail method. The main purpose of this dissertation is to provide a deep understanding of the dependency relationships between welding parameters and weld characteristics. To be able to predict accurately and instantly these characteristics, a three-phase approach is adopted. The first phase is an experimental investigation of the process, its objective is the qualitative and quantitative evaluation of laser welding parameters effect on the variation of the weld geometry. The experiments are planned according to Taguchi method and conducted using a 3KW continuous Nd-YAG laser on specimens of overlapped galvanized steel sheets. The second phase is the modeling of laser welding process using finite element method, to simulate the process behavior under conditions difficult to perform experimentally. The developed model is based on heat transfer equations and considers temperature-dependent properties of the material and phase change enthalpy. A heat source model is adapted to simulate both laser welding in conduction mode and in keyhole mode. The experimental results are used to validate the 3D finite element model. In the third phase, a large database consisting of experimental results and simulation results is used to train and test a predictive model based on artificial neural networks. Several criteria are used to evaluate the prediction quality of the model and its capacity for future predictions. The obtained results showed a perfect agreement with the experimental measurements, the average prediction error observed is less than 7%. -- Mot(s) clé(s) en anglais : Overlap laser welding, Nd-YAG laser, low carbon galvanized steed, predictive modelling, design of experiments, finite elements method, neural networks

Bacterial Memetic Algorithm Trained Fuzzy System-Based Model of Single Weld Bead Geometry

This article presents a fuzzy system-based modeling approach to estimate the weld bead geometry (WBG) from the welding process variables (WPVs) and to achieve a specific weld bead shape. The bacterial memetic algorithm (BMA) is applied to solve these problems in two different roles, as a supervised trainer, and as an optimizer. As a supervised trainer, the BMA is applied to tune two different WBG models. The bead geometry properties (BGP) model follows a traditional approach providing the WBG properties as outputs. The direct profile measurement (DPM) model describes the bead profiles points by a non-linear function realized in the form of fuzzy rules. As an optimizer, the BMA utilizes the developed fuzzy systems to find the solution sets of WPVs to acquire the desired WBG. The best performance is achieved by applying six rules in the BGP model and eleven rules in the DPM model. The results indicate that the normalized root means square error for the validation data set lies in the range of 0:40 - 1:56% for the BGP model and 4:49 - 7:52% for the DPM model. The comparative analysis suggests that the BGP model estimates the BWG in a superior manner when several WPVs are altered. The developed fuzzy systems provide a tool for interpreting the effects of the WPVs. The developed optimizer provides multiple valid set of WPVs to produce the desired WBG, thus supporting the selection of those process variables in applications

Development of an Intelligent Quality Management System for Micro Laser Welding: An Innovative Framework and Its Implementation Perspectives

Copyright: © 2021 by the authors. Laser micro-welding manufacturers face substantial challenges in verifying weldment quality, as the industry and applications are requiring increasingly the miniaturization and compactness of products. The problem is compounded by new stringent demands for personalized products at competitive, low costs and the highest quality levels. High-pressure equipment manufacturers, in particular, rely on ISO 3834:2021 to assure and demonstrate best welding practices but also to manage risks associated with liability issues. ISO 3834:2021, like all conventional quality management systems, offers a one-dimensional, quasi-static overview of welding quality that may fail to deal with these new challenges and underlying complexities required to deal effectively with process variability. This paper presents a framework for welding companies to integrate horizontally their suppliers and customers with their processes and products, which are also integrated vertically in the context of Smart Manufacturing or Industry 4.0. It is focused on the development of a smart quality management system for intelligent digitization of all company manufacturing and business processes. Furthermore, an innovative data-based welding quality management framework is described for laser micro-welding applications and their implementation perspectives. The research is driven by an inductive methodology and based on a seamless integration of engineering-oriented heuristic and empirical approaches that is appropriate for intelligent and autonomous quality management, given the lack of research in this niche, but increasingly important topic area

Kaynak dikiş formunun yapay sinir ağı ve vokselleme yöntemleriyle modellenmesi

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Bu çalışmada, kaynakçı adaylarının eğitimi amacıyla geliştirilen düşük maliyetli sanal kaynak simülatörü için gerçek zamanlı ve üç boyutlu bir kaynak dikiş formu modellenmiştir. Adaylar bu simülatör vasıtasıyla kaynak tekniklerini herhangi bir iş kazasına neden olmadan güvenli bir ortamda öğrenebilir ve kısa sürede normalden daha fazla uygulama yaparak becerilerini geliştirebilirler. Geliştirilen simülatörde, Flock of Birds konum ve oryantasyon sensörü ile başa takılan ekran gibi özel sanal gerçeklik aygıtları kullanılmıştır. Simülasyon, torcun konumunu izleyen Flock of Birds sensör cihazından gelen verilere dayanarak, kaynak dolgu şeklini ve nufuziyet miktarını belirler. Kaynak dolgu şekli oluşturulurken, kaynak dikiş kesitinin parabol ile benzerliği nedeniyle bu şekil temel dolgu birimi olarak kullanılmıştır. Kaynak dikişimizi oluşturacak temel dolgu şeklinin yükseklik, genişlik ve nufuziyet parametrelerine ait değerler literatürdeki kaynak dikişi deneylerinden elde edilmiştir. Sanal kaynak işlemi esnasında, kaynak dolgu şekli parametre değerleri belirli zaman aralıklarında, ileri beslemeli geri yayılımlı yapay sinir ağı kullanılarak hesaplanır. Ağ kurgusu yapılırken eğitim fonksiyonu olarak TrainLM (Levenberg-Marquardt) referans alınmıştır. En uygun transfer fonksiyonu belirlenirken de en iyi sonucu LogSig() fonksiyonunun verdiği saptanmıştır. Ara katman sayısı ve her ara katmandaki proses elemanı (nöron) sayısının kaç olacağına deneme/yanılma yöntemiyle karar verilmiştir. Aynı zaman aralığında voksel haritası ve buna karşılık gelen hash tabanlı sekizli ağaç veri yapısı gerçek zamanlı olarak oluşturulur. Voksellenen veriler kullanılarak, kaynak dolgusunun üçgenlerden oluşan eş yüzeyleri, yürüyen küpler algoritması ile yeniden oluşturulur. Bu sayede daha gerçekçi bir kaynak dikiş görüntüsü elde edilir. Bu görüntü ve sanal sahne devamlı olarak başa takılan ekrana yollanarak sanal ortam içindeki gerçeklik hissi devam ettirilir. Vokselleme ve eş yüzey oluşturma işlemleri için yüksek çözünürlüklü sanal sahnelerde işlem süresini kısaltmak için de çok iş parçacıklı programlama tekniği kullanılmıştır. Farklı iş parçacığı sayıları için eş yüzey oluşturma süreleri de gösterilmiştir.In this study, a real time and three dimensional weld seam form was modeled for a low cost virtual welding simulator developed for training welder candidates. Through this simulator, candidates can learn welding techniques in a safe environment without causing any work accidents and improve their skills by performing more applications than usual in a short time. In the developed simulator, special virtual reality devices such as Flock of Birds position and orientation sensor and head mounted display are used. The simulation determines the weld bead shape and amount of penetration based on data from the Flock of Birds sensor device monitoring the position of the torch. When forming the weld bead shape, parabola was used as the basic bead shape unit due to the similarity of the weld bead slice with the parabola. The values of the height, width and penetration parameters of the basic weld bead shape that will form our weld seam were obtained from the weld seam experiments in the literature. During the virtual welding process, the weld bead shape parameter values are calculated at specified time intervals using the feed-forward back-propagation artificial neural network. TrainLM (Levenberg-Marquardt) was used as the training function for network design. While determining the most appropriate transfer function, it was found that LogSig () function gave the best result. The number of hidden layers and the number of process elements (neurons) in each hidden layer were determined by trial and error method. In the same time interval, the voxel map and the corresponding hash-based octree data structure are generated in real time. By using voxelized data, the triangular isosurfaces of the weld bead are reconstructed using the marching cubes algorithm. This results a more realistic weld seam appearance. This image and virtual scene are continuously sent to the head mounted display to maintain the sense of reality in the virtual environment. Multi-threaded programming technique is also used to shorten the processing time in high resolution virtual scenes for voxelization and isosurface extraction processes. The isosurface extraction times for different number of threads are also shown