Prediction and optimization of process parameters to enhance the tensile strength of Nd: YAG laser welded super duplex stainless steel

Sivagurumanikandan, N.; Saravanan, S.; Kumar, Girendra; Raju, S.; Raghukandan, K.

doi:10.1016/j.ijleo.2017.11.146

Cited by 37 publications

(12 citation statements)

References 15 publications

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“…Plasma arc welding (PAW), electron beam welding (EBW), laser beam welding (LBW) and hybrid Laser arc welding (HLAW) may all be used to produce single pass DSS weldments. These welding methods, on the other hand, are only suitable for joints with a very limited gap or none at all, it is hard to put the right amount of filler metal, which contains a lot of Ni, to the steel weld to be able to get appropriate γ in δ matrix [9,10]. In addition to their extremely low heat capacity, keyhole welding that is fully or partially autogenous may result in an accumulation of δ-ferrite erecting in as welded DSS that prompts worsening of the toughness and resistance to corrosion of the material [11,12].…”

Section: Introductionmentioning

confidence: 99%

Investigation on Effect of Pulsed Current Micro Plasma Arc Weld Parameters on Uns S32750 Super Duplex Stainless Steel Sheets

Gurugubelli¹,

Kesava²

2022

IJMMT

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In this paper, a 0.33mm thick UNS S32750 super duplex stainless steel sheet is butt welded with welding with a micro-plasma arc. Peak current, base current, pulse width, and pulse rate are regarded welding input factors, whereas the tensile strength and hardness of the welded joint are known as output parameters. 31 distinct combinations of tests are carried out by integrating the response surface method (RSM) with central composite design (CCD) and taking into account four factors and five levels of weld process parameters. The main plots are used to visualize the variance in output responses as a function of weld input parameters. MINITAB software is used to establish mathematical models that take into account linear functions. At a 95% confidence level, ANOVA is used to compare two variables. The dominant parameter is identified using contour plots, and the optimum combination of welding parameters is determined using surface plots. Various welding parameters and their effect on stainless duplex steels have been comprehensively explored in this study.

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Section: Introductionmentioning

confidence: 99%

Investigation on Effect of Pulsed Current Micro Plasma Arc Weld Parameters on Uns S32750 Super Duplex Stainless Steel Sheets

Gurugubelli¹,

Kesava²

2022

IJMMT

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“…Ghosh et al [ 21 ] developed a three-dimensional FEM numerical model with non-stationary heat input to examine the laser butt welding method for 2205 duplex stainless steel, considering phase change, to discover the impact of laser power, scanning speed, and beam diameter on thermal properties and construction of weld bead geometry. Sivagurumanikandan et al [ 22 ] studied the impact of input parameters such as welding speed, laser power, focal position, and pulse frequency on strength of laser welded super duplex stainless steel and found the optimum input parameter with the help of response surface methodology. Prabakaran et al [ 23 ] investigated CO 2 laser beam welding of dissimilar metals, specifically austenitic stainless steel (AISI316) and low carbon steel (AISI1018), using Taguchi-based gray relational analysis, while laser power, welding speed, and focal distance were measured as the input parameter.…”

Section: Introductionmentioning

confidence: 99%

Mechanical Properties and Microstructural Characterization of Laser Welded S32520 Duplex Stainless Steel

Abdo

Seikh

2021

Materials

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This paper investigates an experimental design of laser butt welding of S32520 duplex stainless steel, which has been passed out with the help of a pulsed Nd: YAG laser supply. The intention of the present research is to learn the impact of beam diameter, welding speed, and laser power on the superiority of the butt weld. The individuality of butt joints has been characterized in terms of tensile properties, fractography, and hardness. It was noticed that unbalanced particle orientations indirectly produce a comparatively fragile quality in the laser welded joint. The outcome of varying process parameters and interaction effect of process parameters on ultimate tensile strength and micro hardness were studied through analysis of experimental data. With different process parameters, the heat energy delivered to the material was changed, which was reflected in tensile strength measurement for different welded samples. From this present research, it was shown that, up to a certain level, an increase in process parameters amplified the tensile strength, but after that, certain level tensile strength decreased with the increase in process parameters. When process parameters exceeded that certain level, the required amount of heat energy was not delivered to the material, resulting in low bead width and less penetration, thus producing less strength in the welded joint. Less strength leads to more ductile weld joints. Microhardness was higher in the weld zone than in the base region of welded samples. However, the heat affected zone had a high microhardness range.

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“…Sivagurumanikandan et al studied the effect of the process parameters viz., welding speed, laser power, focal position, and pulse frequency on the weld strength of Nd: YAG pulsed laser-welded super duplex stainless steel (SDSS). They found that Neural network models predict the tensile strength more preciously than the regression model [14]. Other studies established that ANN would help determine the required value of welding process parameters to yield a specific welding strength and suitable for online process monitoring and control using CO2 laser-MIG hybrid welding process on AA8011 grade aluminum alloy [15].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Mechanical Properties as a Function of Welding Variables in Robotic Gas Metal Arc Welding of Duplex Stainless Steels SAF 2205 Welds Through Artificial Neural Networks

Payares-Asprino

2021

Advances in Materials Science

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Dual-phase duplex stainless steel (DSS) has shown outstanding strength. Joining DSS alloy is challenging due to the formation of embrittling precipitates and metallurgical changes during the welding process. Generally, the quality of a weld joint is strongly influenced by the welding conditions. Mathematical models were developed to achieve high-quality welds and predict the ideal bead geometry to achieve optimal mechanical properties. Artificial neural networks are computational models used to address complex nonlinear relationships between input and output variables. It is one of the powerful modeling techniques, based on a statistical approach, presently practiced in engineering for complex relationships that are difficult to explain with physical models. For this study robotic GMAW welding process manufactured the duplex stainless steel welds at different welding conditions. Two tensile specimens were manufactured from each welded plate, resulting in 14 tensile specimens. This research focuses on predicting the yield strength, tensile stress, elongation, and fracture location of duplex stainless steel SAF 2205 welds using back-propagation neural networks. The predicted values of tensile strength were later on compared with experimental values obtained through the tensile test. The results indicate <2% of error between observed and predicted values of mechanical properties when using the neural network model. In addition, it was observed that the tensile strength values of the welds were higher than the base metal and that this increased when increasing the arc current. The welds’ yield strength and elongation values are lower than the base metal by 6%, ~ 9.75%, respectively. The yield strength and elongation decrease might be due to microstructural changes when arc energy increases during the welding.

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Prediction and optimization of process parameters to enhance the tensile strength of Nd: YAG laser welded super duplex stainless steel

Cited by 37 publications

References 15 publications

Investigation on Effect of Pulsed Current Micro Plasma Arc Weld Parameters on Uns S32750 Super Duplex Stainless Steel Sheets

Investigation on Effect of Pulsed Current Micro Plasma Arc Weld Parameters on Uns S32750 Super Duplex Stainless Steel Sheets

Mechanical Properties and Microstructural Characterization of Laser Welded S32520 Duplex Stainless Steel

Prediction of Mechanical Properties as a Function of Welding Variables in Robotic Gas Metal Arc Welding of Duplex Stainless Steels SAF 2205 Welds Through Artificial Neural Networks

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