Fatigue behaviour of friction welded medium carbon steel and austenitic stainless steel dissimilar joints

Paventhan, R.; Lakshminarayanan, P.R.; Balasubramanian, V.

doi:10.1016/j.matdes.2010.12.011

Cited by 77 publications

(46 citation statements)

References 15 publications

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“…When a similar combination of austenitic stainless steel is performed, the value of tensile strength is decreased with increase in friction pressure [15]. Similarly, Paventhan et al [16] studied a fatigue behavior by joining medium carbon steel and austenitic stainless steel by conducting experiments using bending fatigue testing. Further, experimental investigation was done on the friction welding of 6063 aluminum alloy with AISI 304 austenitic stainless steel by Sammaiah et al [17] to determine the correlation between the microstructure and the joint strength.…”

Section: Research Findings On Friction Welding Of Austenitic Stainlesmentioning

confidence: 99%

Friction Welding of Austenitic Stainless Steel with Copper Material

Chinnakannan¹

2017

Austenitic Stainless Steels - New Aspects

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Austenitic stainless steels are most preferred over other types of stainless steel families. Welding of stainless steel using friction welding is widely seen in the current scenario. Since the time consumed for friction welding is very less, metallurgical defects are almost reduced without pre-and postheat treatment. The problems encountered in friction welding during joining of austenitic stainless steel are very limited when compared to fusion welding process. The studies have undergone with joining of austenitic stainless steel and copper material to evaluate the friction welding parameter for finding the good bond strength.

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Section: Research Findings On Friction Welding Of Austenitic Stainlesmentioning

confidence: 99%

Friction Welding of Austenitic Stainless Steel with Copper Material

Chinnakannan¹

2017

Austenitic Stainless Steels - New Aspects

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“…Joints of dissimilar metal combinations are employed in different applications requiring certain special combination of properties as well as to save cost 3,4 . Final properties of dissimilar joints depend on the base materials, filler metals, microstructural evolutions in fusion zone, and utilized welding technique [5][6][7] .…”

Section: Introductionmentioning

confidence: 99%

“…Based on literature, many attempts had been done in dissimilar metal joining by researchers through fusion and solid-state welding. Among the solidstate techniques, diffusion welding, explosive welding, friction and friction stir welding are known as the most applied methods 3,4 . In the fusion category, beam-assisted techniques and arc weldings are the most interested methods.…”

Section: Introductionmentioning

confidence: 99%

Designing of CK45 carbon steel and AISI 304 stainless steel dissimilar welds

et al. 2013

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Gas tungsten arc welding of CK45 and AISI304 stainless steel was performed through preparation of different types of samples using ER308L and ERNi-1 wires. Welded samples were studied by different techniques including optical metallography, scanning electron microscopy equipped with energy dispersive X-ray spectroscopy (SEM-EDS), X-ray diffraction, hardness measurements and impact test. It was observed that in the buttered specimen, the structure of the weld metal was completely austenitic while the microstructure of unbuttered sample was duplex ferritic-austenitic. M 23 C 6 -type carbides were observed within the weld metal of both as-weld specimen types. Effects of different post-weld heat treatments (PWHTs) were investigated. The experimented results concluded that simultaneous grain growth and carbides precipitation were competitive during PWHT. Also, there were not any indication related to sigma-phase in as-welded and PWHTed specimens due to their lower Cr/Ni ratios and insufficient preservation times.

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“…Meshram et al [13] reported the microstructures and tensile properties of the weld region in friction welding of dissimilar pure metals. Paventhan et al [14] studied fatigue behavior of friction-welded joints of the medium carbon steel and austenitic stainless steel. Satyanarayana et al [1] obtained sound welds at certain combinations of weld process parameters only.…”

Section: Introductionmentioning

confidence: 99%

Process modeling and parameter optimization using radial basis function neural network and genetic algorithm for laser welding of dissimilar materials

Shao

Jiang

et al. 2015

Appl. Phys. A

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The welded joints of dissimilar materials have been widely used in automotive, ship and space industries. The joint quality is often evaluated by weld seam geometry, microstructures and mechanical properties. To obtain the desired weld seam geometry and improve the quality of welded joints, this paper proposes a process modeling and parameter optimization method to obtain the weld seam with minimum width and desired depth of penetration for laser butt welding of dissimilar materials. During the process, Taguchi experiments are conducted on the laser welding of the low carbon steel (Q235) and stainless steel (SUS301L-HT). The experimental results are used to develop the radial basis function neural network model, and the process parameters are optimized by genetic algorithm. The proposed method is validated by a confirmation experiment. Simultaneously, the microstructures and mechanical properties of the weld seam generated from optimal process parameters are further studied by optical microscopy and tensile strength test. Compared with the unoptimized weld seam, the welding defects are eliminated in the optimized weld seam and the mechanical properties are improved. The results show that the proposed method is effective and reliable for improving the quality of welded joints in practical production.

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Fatigue behaviour of friction welded medium carbon steel and austenitic stainless steel dissimilar joints

Cited by 77 publications

References 15 publications

Friction Welding of Austenitic Stainless Steel with Copper Material

Friction Welding of Austenitic Stainless Steel with Copper Material

Designing of CK45 carbon steel and AISI 304 stainless steel dissimilar welds

Process modeling and parameter optimization using radial basis function neural network and genetic algorithm for laser welding of dissimilar materials

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