Effect of Weld Consumable Conditioning on the Diffusible Hydrogen and Subsequent Residual Stress and Flexural Strength of Multipass Welded P91 Steels

Pandey, Chandan; Mahapatra, Manas Mohan; Kumar, Pradeep; Saini, Nitin

doi:10.1007/s11663-018-1314-8

Cited by 57 publications

(16 citation statements)

References 40 publications

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“…The hydrogen sources are water vapor and covering of electrodes [18]. The hydrogen content in the joints made in wet welding conditions is in the range of 50-80 ml/100g of deposited metal in comparison with H5 or H10 levels for onshore welds [19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Temper Bead Welding of S460N Steel in Wet Welding Conditions

Tomków

Fydrych

Łabanowski

2018

Advances in Materials Science

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Wet welding is the most common method of welding in water environment. It is most often used for repairing of underwater parts of offshore structures. However, the water as a welding environment causes an increase of susceptibility of steels to cold cracking. For underwater constructions high strength low alloy (HSLA) steel are widely used. In wet welding condition a HSLA steel is characterized by high susceptibility to cold cracking. Temper Bead Welding (TBW) was chosen as a method to improve the weldability of S460N steel. The studies showed that TBW technique causes significant decrease of maximum hardness of heat affected zone (HAZ). The largest decrease in hardness occurred in specimens with the pitches in range 66-100%.

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Section: Introductionmentioning

confidence: 99%

Temper Bead Welding of S460N Steel in Wet Welding Conditions

Tomków

Fydrych

Łabanowski

2018

Advances in Materials Science

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“…The residual stresses were investigated in the TIGwelded joint and a minimum value of residual stress was measured in the HAZ, whereas compressive residual stresses were measured in the root section of the welded joint, the tensile residual stresses were observed in the weld zone, and HAZ. 49,50 Figure 17(a) and (b) shows the residual stress profile measured on the bottom and top of the SZ weldment. These residual stress profiles were used for measuring the residual stress measurements by the cosa method.…”

Section: Residual Stressmentioning

confidence: 99%

Microstructure and mechanical characterization of tungsten inert gas-welded joint of AA6061 and AA7075 by friction stir processing

Mehdi

Mishra

2021

Proceedings of the Institution of Mechanical Engineers, Part L:

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In this work, the effect of friction stir processing (FSP) on tungsten inert gas welding (TIG) has been observed to improve mechanical properties and wear resistance behavior of TIG-welded joint of AA7075 and AA6061. The TIG-welded joints were processed by FSP at various tool rotational speeds of 700, 800, 900, 1000, and 1100 r/min with a constant feed rate of 70 mm/min and tilt angle of 1°. The maximum joint efficiency of 92.81% was observed in TIG + FSP-welded joint with filler ER5356 at a rotational tool speed of 1100 r/min. The maximum tensile strength of 284 MPa was observed in TIG + FSP-welded joint with filler ER5356, whereas the minimum tensile strength of 124 MPa was observed in the TIG weldment with filler ER4043. The cleavage facets, tears ridges, and large dimples were observed in fractured specimens of TIG-welded joints, whereas fine and equiaxed dimples were observed in TIG + FSP-welded joints. The maximum micro-hardness of 137 HV in the stir zone was observed in TIG + FSP-welded joint at a rotational tool speed of 1100 r/min. Due to intense precipitate (MgZn2) kept in the stir zone with filler ER5356, the TIG + FSP-welded joints using filler ER5356 have superior wear resistance compared to TIG and TIG + FSP-welded joint with filler ER4043.

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“…The measurements of strain gauge rosettes during tensile testing were also validated through FEM. The influence of the diffusible hydrogen content during shielded metal arc welding of P91 steel plates were investigated by Pandey et al 23 They also utilized blind hole drilling technique to measure the weld-induced residual stresses and validated the same through FEM, with a reasonable accuracy between them.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of residual stress in electron beam welding of stainless steel from process parameters and natural frequency of vibrations using machine-learning algorithms

Das

Pratihar

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part C:

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In the present study, machine learning algorithms have been used to predict residual stress during electron beam welding of stainless steel using the information of input process parameters and natural frequency of vibrations. Accelerating voltage, beam current and welding speed have been considered as input process parameters. Both residual stress and natural frequencies of vibration of the weld obtained using each set of the input parameters are measured experimentally. A number of machine learning algorithms, namely M5 algorithm-based Model Trees Regression, Random forest, Support Vector Regression, Reduced Error Pruning Tree, Multi-layer perceptron, Instance-based k-nearest neighbor algorithm, and Locally weighted learning have been used for the said purpose. Support vector regression and Locally weighted learning are found to perform consistently good and bad, respectively. The predicted welding residual stresses have been validated experimentally through X-ray diffraction (XRD) and good agreements are obtained. In addition, statistical tests are conducted, and the estimated reliability values of the employed models are analyzed through Monte-Carlo simulations.

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Effect of Weld Consumable Conditioning on the Diffusible Hydrogen and Subsequent Residual Stress and Flexural Strength of Multipass Welded P91 Steels

Cited by 57 publications

References 40 publications

Temper Bead Welding of S460N Steel in Wet Welding Conditions

Temper Bead Welding of S460N Steel in Wet Welding Conditions

Microstructure and mechanical characterization of tungsten inert gas-welded joint of AA6061 and AA7075 by friction stir processing

Prediction of residual stress in electron beam welding of stainless steel from process parameters and natural frequency of vibrations using machine-learning algorithms

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