The Effect of Welding Heat Input on the Structure–Property Relationship of a New Grade Super Duplex Stainless Steel

Vahman, Milad; Shamanian, M.; Golozar, M.A.; Jalali, Ali; Sarmadi, M. Ahl; Kangazian, Jalal

doi:10.1002/srin.201900347

Cited by 28 publications

(6 citation statements)

References 39 publications

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“…As in previous research [26], the arc energy as a function of the welding process has a remarkable effect on the ferrite/austenite ratio. The lower arc energy (AE<1.09 kJ/mm) has resulted in a faster rapid cooling rate through the transformation temperature range, which influences the austenite content in this case by decreasing it [27]. Payares et al also established that the arc energy has the strongest impact in the yield strength [19].…”

Section: Resultsmentioning

confidence: 93%

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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Section: Resultsmentioning

confidence: 93%

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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“…The welding current is the main parameter to suppress the impact force of the weld joint followed by the welding voltage [20]. This was confirmed by Arora et al [19] that heat input changes the microstructure of the base material into HAZ and weld pool. This parameter setting also minimizes the possibility of service failure of welded joints in dissimilar materials [21].…”

Section: Introductionmentioning

confidence: 76%

“…Welding current, voltage, speed settings, type of electrode, electrode angle, bevel angle, and root gap in the GMAW process can be used to obtain optimal welding strength and determine metal deposition rate [19]. The welding current is the main parameter to suppress the impact force of the weld joint followed by the welding voltage [20].…”

Section: Introductionmentioning

confidence: 99%

Effect of Electrode Diameter and Current on Dissimilar Metal Welding (Stainless Steel - Galvanized Steel) in Bus Body Construction: Microstructure and Properties Evaluation

Suntari

Purwanto

Respati

et al. 2022

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This study evaluates the macrostructure, microstructure, hardness, and tensile strength in dissimilar metal welding applied to bus body construction. The process involved joining hollow stainless steel and galvanized steel at the dimensions of 80 x 40 x 3.2 mm through Gas Metal Arc Welding (GMAW). The current was varied at 90, 100, and 110 A while ER70S-6 electrodes with diameters of 0.8 and 1.0 mm were used. The results showed that electrode diameter and welding current affect the capping area, penetration depth, and hardness. Moreover, the formation of the widmasatten ferrite phase was increased and the coarse grain boundaries in the weld zone were detected. It was also observed that an increase in the diameter of the electrode and the welding current which indicates an increment in the heat reduced the rate of solidification and cooling. The average tensile strength for all the samples investigated was found to be lower than the value for the base metal. Therefore, further research is recommended to improve the tensile strength.

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“…The GTAW process is one of the most popular DSS welding methods 31 . This process outstands for providing a better ferrite-austenite ratio (around 50% of each) when compared to other welding processes, according to the standards TAPPI TIP 0402-29 and Norsok M-601, 2016 25,29,32 .…”

Section: Introductionmentioning

confidence: 99%

Pitting and Crevice Corrosion Behavior of the Duplex Stainless Steel UNS S32205 Welded by Using the GTAW Process

et al. 2022

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This study investigated the pitting and crevice corrosion behavior of the gas tungsten arc welding (GTAW) process in the UNS S32205 according to industrial parameters. Results revealed that the welding process presented a weld metal chemical composition similar to the base metal and an adequate balance of the austenite and ferrite phases. No relevant variation in the hardness was observed and XRD spectra did not identify the presence of deleterious phases in the weld bead. Cyclic polarization tests revealed similarities between welded and base metal samples (20±2°C, NaCl 3.5% wt.). When comparing the behavior obtained in the crevice, and pitting tests, a decrease in the corrosion resistance was observed in the presence of a crevice former. The SEM-EDS proved that the attack occurred mainly in secondary austenites. Profilometry measurements revealed that the crevice corrosion in the weld region was deeper than in the base metal. However, considering the welded samples as a unit, making no difference between regions: weld metal, HAZ, and base metal, the average crevice corrosion depth was comparable to that of the base metal samples. Finally, it was concluded that the welding process used for the UNS S32205 steel did not harm its corrosion resistance.

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The Effect of Welding Heat Input on the Structure–Property Relationship of a New Grade Super Duplex Stainless Steel

Cited by 28 publications

References 39 publications

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

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

Effect of Electrode Diameter and Current on Dissimilar Metal Welding (Stainless Steel - Galvanized Steel) in Bus Body Construction: Microstructure and Properties Evaluation

Pitting and Crevice Corrosion Behavior of the Duplex Stainless Steel UNS S32205 Welded by Using the GTAW Process

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