Submerged Arc Welding Process Parameters Research Articles

Influence of submerged arc welding process parameters and metallurgical behaviour in a thick carbon steel section for boiler application

AbstractThe aim of the research is to investigate the weldability of thick sections of carbon steel using submerged arc welding with varying process parameters. The experimental design for joining thick carbon steel involves manipulating input weld process parameters such as current (A), voltage (V), weld speed (mm/h), and weld electrode diameter (mm). The quality of the weld material is assessed based on transformations in microstructure, weld hardness measured by Brinell hardness number, and tensile strength (MPa). It is crucial to note that the grain structure and metallurgical behavior of other hard materials may differ significantly. The presence of carbon in the ferrite phase has led to the formation of bainite, martensite, and composites of martensite and austenite within the weld zone, as confirmed by high‐resolution microscopy. The weldment‘s hardness has increased from 242.5 HV 30 in the base metal to 316.4 HV 30 in the weldment due to metallurgical alterations in the microstructure. Weld energy input emerges as the primary factor influencing weld quality. With increasing weld current and voltage, there is a corresponding increase in the joined metal, resulting in improved characteristics in the weld structure, particularly at maximal energy input and welding speed. In the context of boiler applications, understanding the weldability of thick carbon steel sections is paramount for ensuring structural integrity and longevity under high‐temperature and high‐pressure conditions. The optimized welding parameters identified in this study can contribute to enhancing the reliability and performance of boilers, thereby promoting safety and efficiency in industrial settings.

Submerged Arc Welding of SA 516 Gr70 Material: Parametric Optimization of Mechanical Properties UT-Strength and Hardness

In this study, the Taguchi technique was employed to optimize the process parameters of submerged arc welding (SAW) in terms of mechanical properties such as Ultimate Tensile Strength (UTS) and hardness. The study considered a variety of weld quality factors and provided a detailed explanation of the procedure and results using the Taguchi method, which is a statistical analysis approach that requires fewer trials. The experiment was conducted on SA 516 GR70 material using a L9 orthogonal array and a range of current, voltage, and speed settings. The comparison of anticipated and experimental values with the mathematical model of regression analysis-ANOVA with and without interaction helped in achieving the desired results. Lastly, a verification experiment confirmed that the Taguchi technique is reliable in predicting UTS and hardness performance. Overall, this research provides valuable insights into optimizing the SAW process parameters for mechanical properties and demonstrates the effectiveness of the Taguchi technique in predicting and achieving optimal welding performance.

Analyzing the response of submerged arc welding process parameters on Form factor and dilution

Stainless steel (316) possesses higher strength to weight ratio, high toughness and high tensile strength. It has wide range of applications in fabrication of heat exchangers, boat fittings as wire rope clips, in making of tanks, springs and screws, Medical implants, Sinks and splash backs. Welding of these materials has always been a challenging task. Submerged arc welding due to its various properties and specification has been found suitable for this type of work. In this research work, weld bead geometry parameters; Dilution, Form Factor were optimized by using Response Surface Methodology (RSM). Welding current has been found to be the most crucial parameter among all the selected parameters.

Evaluation of mechanical and metallurgical properties of submerged arc welded plate joint

Submerged Arc Welding (SAW) is one of the major welding processes in industry because of its inherent advantages, including deep penetration and a smooth bead. In this study, mild steel (IS2062) 6 mm plates were welded by SAW process. The experiments were designed with three factors and three levels such as current, voltage and welding speed. Optimum process parameters were identified through Taguchi L9 orthogonal array and also studied the influence of SAW process parameters. Mechanical and metallurgical properties of the weld joint were investigated through Scanning Electron Microscopy (SEM) and Energy Dispersive X-Ray Analysis (EDX). Microstructure of the weld zone resulting fine grains of pearlite and ferritic structure along with moderate precipitates were observed.

Parametric Optimization of Submerged Arc Welding Process Parameters by Response Surface Methodology

Abstract Submerged Arc Welding (SAW) is recognized as widely accepted process for joining of plates, pressure vessels, boilers, pipes, huge structures, rail tanks, heat exchangers, marine vessels. SAW finds numerous application in industries because of high current density, easy applicability and its tendency to deposit weld metal in excess quantity by using one or more wire. In the current manufacturing scenario, SAW process has been commonly applied for welding of Stainless Steel 316 (SS 316), High Strength low alloyed Steel and SS 304. Among them, SS 316 is one of the mostly used metal in industries. SAW has widespread applications in plates, bars, pipes, sections and hollow sections. These steels are graded by specifications, chemical composition, technical properties and quality standards. In this work, optimization of input factors of SAW like Voltage, current and speed has been done. In this study height of bead and width of bead are selected as the output parameters. A well-established design of experiment technique, Response Surface Methodology (RSM) has been employed for optimizing input factors. From the outcome of the results, it has been observed that the arc voltage and welding current are significant parameters affecting width of bead and height of bead

Correction to: Mathematical modeling and intelligent optimization of submerged arc welding process parameters using hybrid PSO-GA evolutionary algorithms

Correction to: Mathematical modeling and intelligent optimization of submerged arc welding process parameters using hybrid PSO-GA evolutionary algorithms

Design of experiment based statistical approaches to optimize submerged arc welding process parameters

Joining of metals is very useful concept which is being utilized since Bronze Age and then gradual advancement gave rise to development of modern welding. And now welding is increasingly used in the fields of fabrication, manufacturing and construction. But productivity is the main concern in many manufacturing and industrial welding applications. Therefore selection of a welding process and its variables/parameters without sacrificing weld quality with respect to productivity and its quality is very important because an optimum blend of parameters which inevitably develop minimum or no defect will result in high productivity. For this study Submerge Arc Welding (SAW) process is selected for optimization because this versatile welding process is the first choice whenever good productivity with high quality requires in fabrication and manufacturing of Marine & pressure vessels, pipelines and offshore structures. Here Signal to noise (S/N) ratio analyses are used to find significant effects of key parameters on selected responses and then for optimization design of experiment based both quality loss function (OFM) and desirability function along with variance analyses by ANOVA are utilized. Moreover codes and standards provide a range for weld process parameters but author experienced that still there is a window to further optimize these parameters to produce the quality weld. Therefore this study is also useful to contribute in welding related research work by enhancing the knowledge of welding process and its analysis by utilizing advance statistical optimization techniques to find optimum zone within the acceptable zone from Code & Standard based tolerance Zone.

Experimentation and weld parameter optimisation using grey fuzzy analysis

The most popular steel grade material highly used in the fabrication of industrial pressure vessels and boilers is SA516 grade 70. Submerged Arc Welding is a metal joining technique widely used in heavy fabrication industries for its high quality, reliability, attaining deeper penetration and a smooth appearance of weld bead. In this paper, Taguchi's L27 orthogonal array is used to perform welding of SA 516 grade 70 steel plates. To improve the weld quality, the optimal combination of welding parameters is chosen using grey relational analysis. Grey fuzzy optimisation of welding parameters is based on four different output performance characteristics, namely, bead reinforcement, bead width, bead penetration and dilution. From analysis of variance it is evident that electrode stick out is the most influential factor in submerged arc welding process parameters. Mechanical and metallurgical studies are performed to confirm the effectiveness of the approach.

Investigating effects of resistance wire heating on AISI 1023 weldment characteristics during ASAW

ABSTRACTIn this article, effects of advanced submerged arc welding process parameters on weld bead geometry have been investigated. A novel water-cooled torch has been developed which allow a use of higher preheat current values for a continuous advanced submerged arc welding (ASAW) operation. Weld beads have been deposited on AISI 1023 steel plates by varying open circuit voltage, wire feed rate, welding speed, nozzle to plate distance, and preheat current as per central composite design. The relationships between welding parameters and weldment characteristics namely bead width, penetration, reinforcement, and dilution have been developed using multiple linear regression. The effects of individual process parameters and their interactions on response parameters were examined. Finally, single and multiobjective optimization of process parameters were performed using desirability approach and Jaya algorithm. The results reveal that a smaller bead width and lower dilution can be achieved with the developed torch by allowing the use of higher preheat current values.

Optimization of submerged arc welding process parameters for overlay welding

In this work, we evaluated the influence of welding variables on weld beads applied by the submerged arc process (SAW) with conventional current, aiming future application in overlays against corrosion. Segments of steel API 5L Gr B pipe as substrate, a 1.13 mm-diameter electrode wire of nickel alloy, classification AWS ERNiCrMo-4 (Hastelloy C-276) as filler metal, and a flux neutral, basic and crowded were used. The variables voltage, wire feed speed and contact tip to work distance (CTWD) were analysed, with the remaining parameters constant by an experimental design with full factorial design in two-level and central points. Statistically significant and predictor mathematical models for response dilution and average current were obtained. However, for the response reinforcement/width, the model was characterized as statistically significant, but not predictive, and containing a lack of fit. The CTWD was the most significant variable reducing the dilution.

Optimization of submerged arc welding process parameters using quasi-oppositional based Jaya algorithm

Submerged arc welding (SAW) is characterized as a multi-input process. Selection of optimum combination of process parameters of SAW process is a vital task in order to achieve high quality of weld and productivity. The objective of this work is to optimize the SAW process parameters using a simple optimization algorithm, which is fast, robust and convenient. Therefore, in this work a very recently proposed optimization algorithm named Jaya algorithm is applied to solve the optimization problems in SAW process. In addition, a modified version of Jaya algorithm with oppositional based learning, named “Quasi-oppositional based Jaya algorithm” (QO-Jaya) is proposed in order to improve the performance of the Jaya algorithm. Three optimization case studies are considered and the results obtained by Jaya algorithm and QO-Jaya algorithm are compared with the results obtained by well-known optimization algorithms such as Genetic algorithm (GA), Particle swarm optimization (PSO), Imperialist competitive algorithm (ICA) and Teaching learning based optimization (TLBO).

Investigation of the Effects of Submerged Arc Welding Process Parameters on the Mechanical Properties of Pressure Vessel Steel ASTM A283 Grade A

The pressure vessel steel is used in boilers and pressure vessel structure applications. This research studied the effects of submerged arc welding (SAW) process parameters on the mechanical properties of this steel. The weld sample originated from ASTM A283 grade A sheet of 6.00-millimeter thickness. The welding sample was treated using SAW with the variation of three process factors. For the first factor, welding currents of 260, 270, and 280 amperes were investigated. The second factor assessed the travel speed, which was tested at both 10 and 11 millimeters/second. The third factor examined the voltage parameter, which was varied between 28 and 33 volts. Each welding condition was conducted randomly, and each condition was tested a total of three times, using full factorial design. The resulting materials were examined using tensile strength and hardness tests and were observed with optical microscopy (OM) and scanning electron microscopy (SEM). The results showed that the welding current, voltage, and travel speed significantly affected the tensile strength and hardness (P value < 0.05). The optimum SAW parameters were 270 amperes, 33 volts, and 10 millimeters/second travel speed. High density and fine pearlite were discovered and resulted in increased material tensile strength and hardness.

Effect of Submerged Arc Welding Process Parameters on Weld Bead Geometry and Hardness of ASME SA516 Grade 70 Steel Weld Metal

Effect of Submerged Arc Welding Process Parameters on Weld Bead Geometry and Hardness of ASME SA516 Grade 70 Steel Weld Metal

Multi-Objective Optimization of HAZ Characteristics for Submerged Arc Welding of Micro-Alloyed High Strength Pipeline Steel using GRA-PCA Approach

AbstractIn any type of welding, its heat affected zone (HAZ) is the most critical section where the probability of hydrogen or solidification cracking is prominent. Continuous efforts are being applied by researchers and welders to minimize HAZ and its alterations. For the same purpose, by varying the factors of submerged arc welding (SAW) process which directly influences the heat input and cooling rate of the weld, experiments are carried out on the plates of micro-alloyed high strength pipeline steel. Voltage, welding speed, contact tube to work distance, wire feed rate and preheating temperature are taken as process variables. The experiments are designed according to the central composite rotatable design approach of response surface methodology (RSM). Multi-objective optimization of HAZ characteristics (HAZ area and its hardness) is carried out using grey relational analysis (GRA). To un-correlate the correlated characteristics of HAZ, principal component analysis (PCA) approach is coupled with GRA. The results of the confirmatory test have shown an improvement of 28.51 % and 5.94 % in area and hardness value of HAZ respectively at optimal setting combination of SAW process parameters.

Regression Analysis of Submerged Arc Welding Process Parameters with Respect to Different Electrode Angle as Well as Welding Direction

Regression Analysis of Submerged Arc Welding Process Parameters with Respect to Different Electrode Angle as Well as Welding Direction

Modeling and Optimizing of Submerged Arc Welding Process by Taguchi Design of Experiments in Presence of Magnesium Oxide Nano-Particles

The submerged arc welding (SAW) process is similar to other welding methods, the quality of welded joints plays role as improves strength, stiffness and toughness of products. The purpose of this paper, modeling and optimization of the submerged arc welding process parameters is using Taguchi's method analysis of experiments. All analyses were carried out using Minitab 14 Statistical Software and the interaction parameters in this study are ignored. The experimental results show that this model can predict the output parameters with reasonable accuracy.

Multi Objective Optimization of Weld Parameters of Boiler Steel Using Fuzzy Based Desirability Function

The high pressure differential across the wall of pressure vessels is potentially dangerous and has caused many fatal accidents in the history of their development and operation. For this reason the structural integrity of weldments is critical to the performance of pressure vessels. In recent years much research has been conducted to the study of variations in welding parameters and consumables on the mechanical properties of pressure vessel steel weldments to optimize weld integrity and ensure pressure vessels are safe. The quality of weld is a very important working aspect for the manufacturing and construction industries. Because of high quality and reliability, Submerged Arc Welding (SAW) is one of the chief metal joining processes employed in industry. This paper addresses the application of desirability function approach combined with fuzzy logic analysis to optimize the multiple quality characteristics (bead reinforcement, bead width, bead penetration and dilution) of submerged arc welding process parameters of SA 516 Grade 70 steels(boiler steel). Experiments were conducted using Taguchi’s L27 orthogonal array with varying the weld parameters of welding current, arc voltage, welding speed and electrode stickout. By analyzing the response table and response graph of the fuzzy reasoning grade, optimal parameters were obtained. Solutions from this method can be useful for pressure vessel manufacturers and operators to search an optimal solution of welding condition.

Multiple objective optimization of submerged arc welding process parameters using grey based fuzzy logic

In the present work, an attempt has been made to apply an efficient technique, Grey based fuzzy logic method to solve correlated multiple response optimization problems, in the field of submerged arc welding. This approach converts the complex multiple objectives into a single grey-fuzzy reasoning grade. Based on grey-fuzzy reasoning grade, optimum levels of parameters (Welding current, arc voltage and welding speed) are identified. Nine experiments based on an orthogonal array of Taguchi method were performed. Weld bead hardness and material deposition rate were selected as the quality targets. The optimal procedure is proposed and developed for solving the optimal multi-response problem, which applies the grey relational coefficient in each response and converts a grey-fuzzy reasoning grade so as to evaluate multiple responses. The significant contributions of parameters are estimated using Analysis Of Variance (ANOVA). Confirmation test is conducted and reported. It is found that the welding current is the most significant controlled factor for the process according to the weighted sum grade of the maximum weld bead hardness and material deposition rate. This evaluation procedure can be used in intelligent decisionmaking for a welding operator. The proposed and developed method has good accuracy and competency. The paper highlights a detailed methodology of the proposed scheme and its effectiveness. The proposed technique provides manufacturers to develop intelligent manufacturing system to achieve the highest level of automation.

USE OF DESIRABILITY FUNCTION AND PRINCIPAL COMPONENT ANALYSIS IN GREY-TAGUCHI APPROACH TO SOLVE CORRELATED MULTI-RESPONSE OPTIMIZATION IN SUBMERGED ARC WELDING

Submerged arc welding (SAW) is an important metal fabrication technology specially applied to join metals of large thickness in a single pass. In order to obtain an efficient joint, several process parameters of SAW need to be studied and precisely selected to improve the bead quality. Many methodologies have been proposed in the past research to address this issue. However, a good number of past works seek to optimize SAW process parameters with only one response. In practical situations, not only influence of process parameters and their interactive effects on output responses are to be critically examined but also attempt is to be made to optimize more than one response simultaneously. To this end, the present study considers four process control parameters viz. voltage (OCV), wire feed rate, traverse speed and electrode stick-out. The selected weld quality characteristics related to features of bead geometry are depth of penetration, reinforcement and bead width. An integrated approach capable of overcoming the drawbacks of traditional optimization approaches has been suggested and results thereof analyzed. The proposed approach explores Orthogonal Array (OA) experiments for adaptation to grey-Taguchi method. Principal Component Analysis (PCA) has been used to convert correlated response values into uncorrelated quality indices called principal components. Based on desirability loss, grey relational coefficients of each quality indices have been calculated. These coefficients have been further accumulated to calculate overall grey relational grade. Individual response weights have been calculated using entropy measurement technique. Finally, Taguchi method has been adopted for optimization purpose. The study finally demonstrates the robustness and flexibility of the integrated approach in solving multi-criteria optimization problem in SAW process.

The use of grey-based Taguchi methods to determine submerged arc welding process parameters in hardfacing

In this paper, the use of grey-based Taguchi methods for the optimization of the submerged arc welding (SAW) process parameters in hardfacing with considerations of multiple weld qualities is reported. In this new approach, the grey relational analysis is adopted to solve the SAW process with multiple weld qualities. A grey relational grade obtained from the grey relational analysis is used as the performance characteristic in the Taguchi method. Then, optimal process parameters are determined by using the parameter design proposed by the Taguchi method. Experimental results have shown that optimal SAW process parameters in hardfacing can be determined effectively so as to improve multiple weld qualities through this new approach.