Neuro-fuzzy control modelling for gas metal arc welding process

Weld quality features are difficult or impossible to directly measure and control during welding, therefore indirect methods are necessary. Penetration is the most important geometric feature since in most applications it is the most significant factor affecting joint strength. Observation of penetration is only possible from the back face of the full penetration weld. In all other cases, since direct measurement of depth of penetration is not possible, real time control of penetration in the Gas Metal Arc Welding (GMAW) process by sensing conditions at the top surface of the joint is necessary. This continues to be a major area of interest for automation of the process. The objective of this research has been to develop an on-line intelligent process control model for GMAW, which can monitor and control the welding process. The model uses measurement of the temperature at a point on the surface of the workpiece to predict the depth of penetration being achieved, and to provide feedback for corrective adjustment of welding variables. Neural Network and Fuzzy Logic technologies have been used to achieve a reliable Neuro-Fuzzy control model for GMAW of a typical closed butt joint having 60° Vee edge preparation. The neural network model predicts the surface temperature expected for a set of fixed and adjustable welding variables when a prescribed level of penetration is achieved. This predicted temperature is compared with the actual surface temperature occurring during welding, as measured by an infrared sensor. If there is a difference between the measured temperature and the temperature predicted by the neural network, a fuzzy logic model will recommend changes to the adjustable welding variables necessary to achieve the desired weld penetration. Large scale experiments to obtain data for modelling and for model validation, and various other modelling studies are described. The results are used to establish the relationships between the output surface temperature measurement, welding variables and the corresponding achieved weld quality criteria. The effectiveness of the modelling methodology in dealing with fixed or variable root gap has also been tested. The result shows that the Neuro-fuzzy models are capable of providing control of penetration to an acceptable degree of accuracy, and a potential control response time, using modestly powerful computing hardware, of the order of one hundred milliseconds. This is more than adequate for real time control of GMAW. The application potential for control using these models is significant since, unlike many other top surface monitoring methods, it does not require sensing of the highly transient weld pool shape or surface

Additive Manufacturing Research and Applications

This Special Issue book covers a wide scope in the research field of 3D-printing, including: the use of 3D printing in system design; AM with binding jetting; powder manufacturing technologies in 3D printing; fatigue performance of additively manufactured metals, such as the Ti-6Al-4V alloy; 3D-printing methods with metallic powder and a laser-based 3D printer; 3D-printed custom-made implants; laser-directed energy deposition (LDED) process of TiC-TMC coatings; Wire Arc Additive Manufacturing; cranial implant fabrication without supports in electron beam melting (EBM) additive manufacturing; the influence of material properties and characteristics in laser powder bed fusion; Design For Additive Manufacturing (DFAM); porosity evaluation of additively manufactured parts; fabrication of coatings by laser additive manufacturing; laser powder bed fusion additive manufacturing; plasma metal deposition (PMD); as-metal-arc (GMA) additive manufacturing process; and spreading process maps for powder-bed additive manufacturing derived from physics model-based machine learning

Control using neural networks and adaptive control for cooling rate in GMA welding

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1994.Includes bibliographical references (leaves 220-223).by Isamu Okamura.M.S

Arc behaviour and metal transfer of the VP-GMAW process

This project evaluated the metal transfer behaviour of the variable polarity (VP) GMAW process. Analysis was performed using high speed video that was synchronised with high speed data acquisition. Melting rate measurements were found to be very dependent on current waveform, polarity, and droplet size, and metal transfer if it occurred, for each waveform period. The transient conditions of current waveform and metal transfer produced rapid changes in arc behaviour which influenced the melting at the electrode tip and growing droplet. The concentrated melting theory was developed to explain the significant increase in electrode extension burnoff and droplet growth rate that occurred at short EN time as a function of current, and during EP peak pulse when the pre-pulse droplet volume was small. The highest electrode extension burnoff and droplet growth rate occurred when the arc was permitted to climb over the solid electrode tip producing rapid concentrated melting. Likewise, large molten droplets were found to promote a negative electrode extension burnoff and a decreased droplet growth rate. The arc rooted on large droplets providing additional heating but limited electrode melting. The droplet burnoff rate (DBR) method was developed and found to yield good experimental measurements for the arc and resistive heating coefficients used in a 2nd order melting rate equation developed for a complex waveform process, like VP-GMAW. For the EN period, the EN time affected the melting rate as a function of EN current. The greater melting rate that occurred at low EN time was measured by the changes in the resistive heating coefficient. Concentrated arc melting of the electrode extension at low EN time caused the slope of the burnoff diagram to increase, which represented the resistive heating coefficient. The melting rate of the EP pulse was related to the pre-pulse droplet volume. Large pre-pulse droplets decreased the arc heating coefficient, which could be negative, which meant the electrode extension was increasing and the arc length was decreasing in that waveform period. VP-GMAW power supplies offered stable operation for welding sheet structures on both carbon steel and stainless steel. Higher travel speeds were required as the %EN of the waveform increased to produce acceptable constant deposit area fusion. Welding speeds were up to 300% higher with VP-GMAW compared to the GMAW-P process when welding lap joints on 1.8 mm thick material with a 1.8 mm gap. VP-GMAW heat input was up to 47% less than GMAW-P for the same melting rate

The current state of research of wire arc additive manufacturing (WAAM): a review

Wire arc additive manufacturing is currently rising as the main focus of research groups around the world. This is directly visible in the huge number of new papers published in recent years concerning a lot of different topics. This review is intended to give a proper summary of the international state of research in the area of wire arc additive manufacturing. The addressed topics in this review include but are not limited to materials (e.g., steels, aluminum, copper and titanium), the processes and methods of WAAM, process surveillance and the path planning and modeling of WAAM. The consolidation of the findings of various authors into a unified picture is a core aspect of this review. Furthermore, it intends to identify areas in which work is missing and how different topics can be synergetically combined. A critical evaluation of the presented research with a focus on commonly known mechanisms in welding research and without a focus on additive manufacturing will complete the review

Real time defect detection in welds by ultrasonic means

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A computer controlled weld quality assurance system has been developed to detect weld defects ultrasonically whilst welding is in progress. This system, including a flash analogue to digital converter and built-in memories to store sampled data, a peak characters extractor and a welding process controller, enabled welding processes to be controlled automatically and welding defects to be detected concurrently with welding. In this way, the weld quality could be satisfactorily assured if no defect was detected and the welding cost was minimised either through avoiding similar defects to occur or by stopping the welding process if repair was necessary. This work demonstrated that the high temperature field around the weld pool was the major source of difficulties and unreliabilities in defect detection during welding and, had to be taken into account in welding control by ultrasonic means. The high temperatures not only influence ultrasonic characteristic parameters which are the defect judgement and assessment criterion, but also introduce noise into signals. The signal averaging technique and statistical analysis based on B-scan data have proved their feasibility to increase 'signal to noise ratio' effectively and to judge or assess weld defects. The hardware and the software for the system is explained in this work. By using this system, real-time 'A-scan' signals on screen display, and, A-scan, B-scan or three dimensional results can be printed on paper, or stored on disks, and, as a result, weld quality could be fully computerized.Sino-British Friendship Scholarship Schem

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

Τρισδιάστατη Θερμομηχανική Ανάλυση της Συγκόλλησης Ανοξείδοτων Ωστενιτικών Χαλύβων

367 σ.Περιλαμβάνει εκτεταμένη ελληνική περίληψη σε ξεχωριστό τεύχος.Στην παρούσα διατριβή μελετάται η αριθμητική μοντελοποίηση της διαδικασίας συγκόλλησης ανοξείδωτων ωστενιτικών χαλύβων με τη μέθοδο των πεπερασμένων στοιχείων. Για την πραγματοποίηση αριθμητικών μοντελοποιήσεων απαραίτητη είναι η μελέτη της διαδικασίας συγκόλλησης αλλά και του υλικού. Η γνώση και κατανόηση της συμπεριφοράς του υλικού κατά τη θέρμανσή του, από το τόξο της συγκόλλησης, αλλά και κατά την ψύξη του είναι απαραίτητες για την κατασκευή του μοντέλου, την ακριβή εφαρμογή του θερμικού φορτίου και την πρόβλεψη της θερμομηχανικής ανάδρασής της συγκολλητής κατασκευής. Συνεπώς, καταστρώθηκαν και πραγματοποιήθηκαν μετωπικές συγκολλήσεις ελασμάτων, διαφόρων διαστάσεων, ανοξείδωτων ωστενιτικών χαλύβων και μετρήθηκαν κατά τη διάρκεια της συγκόλλησης οι θερμικοί κύκλοι, οι παραμορφώσεις και κατόπιν οι παραμένουσες τάσεις, ενώ ακολούθησε μεταλλογραφική μελέτη του προφίλ της συγκόλλησης. Η ολοκληρωμένη διερεύνηση της διαδικασίας συγκόλλησης επιτυγχάνεται κυρίως με την κατασκευή τρισδιάστατων μοντέλων. Η θερμομηχανική επίλυσή τους όμως με τη μέθοδο των πεπερασμένων στοιχείων απαιτεί αρκετό χρόνο σε σχέση με την αντίστοιχη δισδιάστατη ανάλυση, λόγω του αυξημένου αριθμού στοιχείων και κόμβων. Ο χρόνος επίλυσης έχει μειωθεί επιτυχώς χάρη στους σύγχρονους ισχυρούς υπολογιστές, αλλά οι προσπάθειες σήμερα επικεντρώνονται στη μείωση του χρόνου επίλυσης με διάφορες τεχνικές χωρίς όμως την πιθανότητα απώλειας της ακρίβειας των αποτελεσμάτων. Συνεπώς στην παρούσα διατριβή πραγματοποιούνται μια σειρά από αριθμητικές αναλύσεις που αποσκοπούν στη μείωση του χρόνου επίλυσης, ενώ ταυτοχρόνως ελέγχεται και η ακρίβεια των αποτελεσμάτων τους. Τελικώς, η εφαρμογή της τεχνικής «ομαδοποίησης» των περασμάτων επιτυγχάνει μείωση του χρόνου επίλυσης κατά 35% και εξαιρετική ακρίβεια των αποτελεσμάτων της.In the present thesis the numerical simulation of the austenitic stainless steel welding process is investigated via the finite element method. In order to proceed to the simulation of the thermo-mechanical process of austenitic stainless steel, the process itself and the material must be carefully studied. The knowledge and understanding of the material behavior during the heating by the welding arc, but also upon cooling, is crucial for the construction of the model, the accurate implementation of the thermal load and the prediction of the thermo-mechanical response of the welded joint. In order to acquire such knowledge, a series of welding experiments were conducted through the butt-welding of austenitic stainless steel plates with various dimensions. In-situ measurements of the thermo-mechanical response, along with stress measurements and metallographic investigation in the as-welded condition, provided sufficient information, thus allowing the accurate numerical modeling of the welding process. The complete insight in the case of the welding process investigation is achieved mostly with the construction of three-dimensional models. The thermo-mechanical analysis of solid models with the finite element method is a time-consuming process in comparison to two-dimensional analyses, since a much larger number of nodes and elements are required for the construction of the solid model. The required time for a solution to be achieved has been decreased with the ongoing improvement of computational efficiency of personal computers. However, efforts are focused on various techniques that would decrease computational time of three-dimensional analyses mostly in multi-pass welding simulation, without any sacrifice in the accuracy in prediction capability. Thus, in the present thesis a series of numerical welding simulations are presented where the accuracy of the predicted results is evaluated and techniques to minimize the computational time are employed. The employment of the “Grouping” technique clearly shows that accurate results can be acquired with a 35% reduction of computational time.Ανδρέας Π. Κυριακόγγονα

Numerical Modelling and Simulation of Metal Processing

This book deals with metal processing and its numerical modelling and simulation. In total, 21 papers from different distinguished authors have been compiled in this area. Various processes are addressed, including solidification, TIG welding, additive manufacturing, hot and cold rolling, deep drawing, pipe deformation, and galvanizing. Material models are developed at different length scales from atomistic simulation to finite element analysis in order to describe the evolution and behavior of materials during thermal and thermomechanical treatment. Materials under consideration are carbon, Q&T, DP, and stainless steels; ductile iron; and aluminum, nickel-based, and titanium alloys. The developed models and simulations shall help to predict structure evolution, damage, and service behavior of advanced materials