Experimental investigation and optimization of weld bead characteristics during submerged arc welding of AISI 1023 steel

Choudhary, Ankush; Kumar, Manoj; Unune, Deepak Rajendra

doi:10.1016/j.dt.2018.08.004

Cited by 56 publications

(16 citation statements)

References 10 publications

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“…It is found that weld bead characteristics and grain size are governed by power density for a given energy conditions. Choudhary et al [6] carried out bead on plate experiments to study the effect of direct and indirect weld input parameters on weld width, reinforcement and penetration in submerged arc welding of AISI 1023 steel. The effects were studied by means of genetic algorithm, Jaya algorithm and desirability approach.…”

Section: Introductionmentioning

confidence: 99%

Effect of process parameters on bead geometry, tensile and microstructural properties of double-sided butt submerged arc welding of SS 304 austenitic stainless steel

Sridhar

Biswas

Mahanta

2020

J Braz. Soc. Mech. Sci. Eng.

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The purpose of this investigation is to study the effect of welding variables on bead geometry, fusion zone profile and tensile properties of double-sided submerged arc welding on SS 304 austenitic stainless steel. The individual effect of welding parameters, i.e., welding current, voltage and traverse speed, on bead geometry and tensile properties was investigated. Both microstructural and mechanical characterizations of the weldment were carried out in the present study. It was observed that the operating parameters have significant effect on bead geometry and tensile properties. The double-sided welding effectively enhanced the weld seam ductility. The percentage of elongation is improved by 19.48% to 50.2% for double-sided samples as compared to base material. A comparative study between single and double-sided butt welding was also carried out. For single-sided sample the percentage of elongation is lower than the base material. The percentage reduction in angular distortion of 55.17% is observed with double-sided weld sample as compared to single-sided weld sample.

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Section: Introductionmentioning

confidence: 99%

Effect of process parameters on bead geometry, tensile and microstructural properties of double-sided butt submerged arc welding of SS 304 austenitic stainless steel

Sridhar

Biswas

Mahanta

2020

J Braz. Soc. Mech. Sci. Eng.

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“…In regression analysis [12] as expressed by Eq. (1), an experimental mathematical model was generated in between the toughness, hardness, and independents variable [13] and check for its adequacy. Response surface methodology is also used based on central composite design (CCD) to develop a model to predict the mechanical quality and checked by ANOVA for its adequacy [14].…”

Section: Development Of Mathematical Modelmentioning

confidence: 99%

Development of mathematical model and optimization of GMA welding parameters of IS 2062 grade A steel weldments

Rizvi

Ali

2021

Frattura Ed Integrità Strutturale

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In this experimental works, the effect of GMA welding process parameters, such as arc voltage, wire feed speed, and gas flow rate on the mechanical quality of IS 2062 structural steel of grade A has been studied. Process parameters play an important role in determining the weld quality. In this research work response surface methodology (RSM) technique via design expert (DOE) 12 version software was applied to determining the weld quality and also to develop a mathematical model that can predict the main effect of the above said parameters on weld quality i.e. toughness and hardness. A set of experiments has been conducted to collect the data using a central composite design and ANOVA was used to predict the impact of welding parameters on toughness and hardness and Comparison also made between the actual result and predicted value and from the result that is clear that toughness and hardness of weldment is significantly affected by arc voltage, wire feed speed, and follow by gas flow rate.

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“…Because to the complex interrelationship between these welding parameters, it is hard to deduce an appropriate physics model in continuous welding process with changing parameters [ 5 ]. In recent decades, researchers have applied various mathematical models to build the relationship between multi-input and multi-output parameters, e.g., factorial design, linear and nonlinear regression, response surface methodology, and artificial neural network (ANN) [ 4 , 6 , 7 , 8 , 9 , 10 , 11 ]. These design of experiments (DOE) techniques apply to different areas according to the complex relationship between input and output parameters, and they achieve high accuracy and efficiency in modeling.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Bead Geometry with Changing Welding Speed Using Artificial Neural Network

Dong

Gao

2021

Materials

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Bead size and shape are important considerations for industry design and quality detection. It is hard to deduce an appropriate mathematical model for predicting the bead geometry in a continually changing welding process due to the complex interrelationship between different welding parameters and the actual bead. In this paper, an artificial neural network model for predicting the bead geometry with changing welding speed was developed. The experiment was performed by a welding robot in gas metal arc welding process. The welding speed was stochastically changed during the welding process. By transient response tests, it was indicated that the changing welding speed had a spatial influence on bead geometry, which ranged from 10 mm backward to 22 mm forward with certain welding parameters. For this study, the input parameters of model were the spatial welding speed sequence, and the output parameters were bead width and reinforcement. The bead geometry was recognized by polynomial fitting of the profile coordinates, as measured by a structured laser light sensor. The results showed that the model with the structure of 33-6-2 had achieved high accuracy in both the training dataset and test dataset, which were 99% and 96%, respectively.

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Experimental investigation and optimization of weld bead characteristics during submerged arc welding of AISI 1023 steel

Cited by 56 publications

References 10 publications

Effect of process parameters on bead geometry, tensile and microstructural properties of double-sided butt submerged arc welding of SS 304 austenitic stainless steel

Effect of process parameters on bead geometry, tensile and microstructural properties of double-sided butt submerged arc welding of SS 304 austenitic stainless steel

Development of mathematical model and optimization of GMA welding parameters of IS 2062 grade A steel weldments

Prediction of Bead Geometry with Changing Welding Speed Using Artificial Neural Network

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