Modelling and prediction of the mechanical properties of TIG welded joint for AISI 4130 low carbon steel plates using Artificial Neural Network (ANN) approach

The mechanical properties (Ultimate Tensile Strength (UTS), modulus of elasticity (E), elongation and strain (e)) for twenty samples of AISI 4130 Low carbon steel plate were studied in this paper. Statistical design of experiment (DOE) using the central composite design method (CCD) was employed in Design Expert 7.01 software to generate DOE for twenty (20) experimental runs as input variables (current, voltage and gas flowrate) which were used in predicting and optimizing the output parameters (maximum UTS and maximum modulus of elasticity with corresponding elongation and strain). One out of the 20 welding runs was found to be optimum using the Artificial Neural Network (ANN) optimization approach. The same twenty (20) predicted variables were subjected to TIG welding experimentation which showed close proximity between the predicted and experimental values. Optimized ANN predicted output parameters were UTS of 421 MPa, modulus of elasticity of 793 MPa, strain of 0.61 and elongation of 61% while experimental values using the optimized input variables produced output parameters of 427 MPa for UTS of 421 MPa, 806 MPa for modulus of elasticity, strain of 0.62 and 62% elongation. Visuals of the weldment obtained from Scanning Electron Microscopy with Energy Dispersive Spectroscopy (SEM/EDS) revealed a uniformly distributed grain sizes in the weldment primarily composing of iron (Fe), chromium (Cr), molybdenum (Mo), and nickel (Ni). To save time, energy and resources required for welding experimentation processes, conventional software such as ANN can be used to obtain accurate results.Keywords: Modelling, Prediction, Low carbon steel, TIG welding, Welding variable

Investigations on quality characteristics in gas tungsten arc welding process using artificial neural network integrated with genetic algorithm

Gas tungsten arc welding (GTAW) technology is widely used in industry and has advantages, including high precision, excellent welding quality, and low equipment cost. However, the inclusion of a large number of process parameters hinders its application on a wider scale. Therefore, there is a need to implement the prediction and optimization models that effectively enhance the process performance of the GTAW process in different applications. In this study, a five-factor five-level central composite design (CCD) matrix was used to conduct GTAW experiments. AISI 1020 steel blank was used as a substrate; UTP AF Ledurit 60 and UTP AF Ledurit 68 were used as the materials of two tubular wires. Further, an artificial neural network (ANN) was used to simulate the GTAW process and then combined with a genetic algorithm (GA) to determine welding parameters that can provide an optimal weld. In welding experiments, five different welding current levels, welding speed, distance to the nozzle, angle of movement, and frequency of the wire feed pulses were used. Using GA, optimal welding parameters were determined: welding current = 222 A, welding speed = 25 cm/min, nozzle deflection distance = 8 mm, travel angle = 25°, wire feed pulse frequency = 8 Hz. The determination coefficient (R2) and RMSE value of all response parameters are satisfactory, and the R2 of all the data remained higher than 0.65

Advances in Plasma Arc Welding: A Review

The nature of welding in the aeronautical industry is characterized by low unit production, high unit cost, extreme reliability and severe service conditions. These characteristics point towards more expensive and more concentrated heat sources such as plasma arc, laser beam and electron beam welding as the processes of choice for welding of critical components. Among various precision welding processes, Plasma Arc welding has gained importance in small and medium scale industries manufacturing bellows , diaphragms etc because of less expensive and easy to operate. This paper reviews the works on Plasma Arc welding and associated phenomena such as Micro Plasma Arc Welding, Variable Polarity Plasma Arc welding and Keyhole Plasma Arc Welding. The review covers works carried out by various researchers on various metals using different modes of plasma arc

Optimisation of welding parameters to mitigate the effect of residual stress on the fatigue life of nozzle–shell welded joints in cylindrical pressure vessels.

Doctoral Degree. University of KwaZulu-Natal, Durban.The process of welding steel structures inadvertently causes residual stress as a result of thermal cycles that the material is subjected to. These welding-induced residual stresses have been shown to be responsible for a number of catastrophic failures in critical infrastructure installations such as pressure vessels, ship’s hulls, steel roof structures, and others. The present study examines the relationship between welding input parameters and the resultant residual stress, fatigue properties, weld bead geometry and mechanical properties of welded carbon steel pressure vessels. The study focuses on circumferential nozzle-to-shell welds, which have not been studied to this extent until now. A hybrid methodology including experimentation, numerical analysis, and mathematical modelling is employed to map out the relationship between welding input parameters and the output weld characteristics in order to further optimize the input parameters to produce an optimal welded joint whose stress and fatigue characteristics enhance service life of the welded structure. The results of a series of experiments performed show that the mechanical properties such as hardness are significantly affected by the welding process parameters and thereby affect the service life of a welded pressure vessel. The weld geometry is also affected by the input parameters of the welding process such that bead width and bead depth will vary depending on the parametric combination of input variables. The fatigue properties of a welded pressure vessel structure are affected by the residual stress conditions of the structure. The fractional factorial design technique shows that the welding current (I) and voltage (V) are statistically significant controlling parameters in the welding process. The results of the neutron diffraction (ND) tests reveal that there is a high concentration of residual stresses close to the weld centre-line. These stresses subside with increasing distance from the centre-line. The resultant hoop residual stress distribution shows that the hoop stresses are highly tensile close to the weld centre-line, decrease in magnitude as the distance from the weld centre-line increases, then decrease back to zero before changing direction to compressive further away from the weld centre-line. The hoop stress distribution profile on the flange side is similar to that of the pipe side around the circumferential weld, and the residual stress peak values are equal to or higher than the yield strength of the filler material. The weld specimens failed at the weld toe where the hoop stress was generally highly tensile in most of the welded specimens. The multiobjective genetic algorithm is successfully used to produce a set of optimal solutions that are in agreement with values obtained during experiments. The 3D finite element model produced using MSC Marc software is generally comparable to physical experimentation. The results obtained in the present study are in agreement with similar studies reported in the literature

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

Challenges towards Structural Integrity and Performance Improvement of Welded Structures

Welding is a fabrication process that joint materials, is extensively utilized in almost every field of metal constructions. Heterogeneity in mechanical properties, metallurgical and geometrical defects, post-weld residual stresses and distortion due to non-linear welding processes are prime concerns for performance reduction and failures of welded structures. Consequently, structural integrity analysis and performance improvement of weld joints are important issues that must be considered for structural safety and durability under loading. In this study, an extensive experimental program and analysis were undertaken on the challenges towards structural integrity analysis and performance improvement of different welded joints. Two widely used welding techniques including solid-state “friction- stir- welding (FSW)” and fusion arc “gas tungsten arc welding (GTAW)” were employed on two widely utilized materials, namely aluminum alloys and structural steels. Various destructive and non-destructive techniques were utilized for structural integrity analysis of the welded joints. Furthermore, various “post-weld treatment (PWT)” techniques were employed to improve mechanical performances of weld joints. The work herein is divided into six different sections including: (i) Establishment of an empirical correlation for FSW of aluminum alloys. The developed empirical correlation relates the three critical FSW process parameters and was found to successfully distinguish defective and defect-free weld schedules; (ii) Development of an optimized “adaptive neuro-fuzzy inference system (ANFIS)” model utilizing welding process parameters to predict ultimate tensile strength (UTS) of FSW joints; (iii) Determination of an optimum post-weld heat treatment (PWHT) condition for FS-welded aluminum alloys; (iv) Exploration on the influence of non-destructively evaluated weld-defects and obtain an optimum PWHT condition for GTA-welded aluminum alloys; (v) Investigation on the influence of PWHT and electrolytic-plasma-processing (EPP) on the performance of welded structural steel joints; and finally, (vi) Biaxial fatigue behavior evaluation of welded structural steel joints. The experimental research could be utilized to obtain defect free weld joints, establish weld acceptance/rejection criteria, and for the better design of welded aluminum alloy and steel structures. All attempted research steps mentioned above were carried out successfully. The results obtained within this effort will increase overall understanding of the structural integrity of welded aluminum alloys and steel structures

Investigations on quality characteristics in gas tungsten arc welding process using artificial neural network integrated with genetic algorithm

Gas tungsten arc welding (GTAW) technology is widely used in industry and has advantages, including high precision, excellent welding quality, and low equipment cost. However, the inclusion of a large number of process parameters hinders its application on a wider scale. Therefore, there is a need to implement the prediction and optimization models that effectively enhance the process performance of the GTAW process in different applications. In this study, a five-factor five-level central composite design (CCD) matrix was used to conduct GTAW experiments. AISI 1020 steel blank was used as a substrate; UTP AF Ledurit 60 and UTP AF Ledurit 68 were used as the materials of two tubular wires. Further, an artificial neural network (ANN) was used to simulate the GTAW process and then combined with a genetic algorithm (GA) to determine welding parameters that can provide an optimal weld. In welding experiments, five different welding current levels, welding speed, distance to the nozzle, angle of movement, and frequency of the wire feed pulses were used. Using GA, optimal welding parameters were determined: welding current = 222 A, welding speed = 25 cm/min, nozzle deflection distance = 8 mm, travel angle = 25°, wire feed pulse frequency = 8 Hz. The determination coefficient (R2) and RMSE value of all response parameters are satisfactory, and the R2 of all the data remained higher than 0.6

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

Prediction of bead geometry using a two-stage SVM–ANN algorithm for automated tungsten inert gas (TIG) welds

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Prediction of weld bead geometry is critical for any welding process, since several mechanical properties of the weldment depend on this. Researchers have used artificial neural networks (ANNs) to predict the bead geometry based on the input parameters for a welding process; however, the number of hidden layers used in these ANNs are limited to one due to the small amount of data usually available through experiments. This results in a reduction in the accuracy of prediction. Such ANNs are also incapable of capturing sudden changes in the input–output trends; for example, where a wide range of heat inputs results in flat crown (zero crown height), but any further reduction in the current sharply increases the crown height. In this study, it was found that above mentioned issues can be resolved on using a two-stage algorithm consisting of support vector machine (SVM) and an ANN. The two-stage SVM–ANN algorithm significantly improved the accuracy of prediction and could be used as a replacement for the multiple hidden layer ANN, without requiring additional data for training. The improvement in prediction was evident near regions of sudden changes in the input–output correlation and can lead to a better prediction of mechanical properties