Quality assessment of resistance spot welding joints of AISI 304 stainless steel based on elastic nets

Martín, Óscar; Ahedo, Virginia; Santos, José Ignacio; Tiedra, Pilar De; Galán, José Manuel

doi:10.1016/j.msea.2016.08.112

Cited by 25 publications

(10 citation statements)

References 31 publications

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“…Tensile shear tests were carried out, on a Shimadzu UH-F500 kNA universal testing machine, at a crosshead speed of 2 mm/min [24,32,33], which, as explained by Marashi et al [34], allows to consider the test as static. The tensile shear test specimens were prepared according to ISO 14273 [35].…”

Section: Tensile Shear Testingmentioning

confidence: 99%

Effect of Widmanstätten η phase on tensile shear strength of resistance spot welding joints of A286 superalloy

Martín

Tiedra

Blanco

2019

Metall. Res. Technol.

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This work aims to study the effect of Widmanstätten h phase on tensile shear strength (TSS) of resistance spot welding joints (RSW) of A286 superalloy subjected to post-weld high temperature aging treatment. The tensile shear test specimens were welded in the solution treated condition, and then subjected to six different post-weld aging treatments (at an aging temperature of 840°C for six different aging times). The ascast dendritic microstructure of the weld nugget and the austenite equiaxed-grain microstructure of the base metal have a different response to the post-weld high temperature aging treatment. Growth of Widmanstätten h phase in the weld nugget is faster than in the base metal. While in the base metal the Widmanstätten h phase precipitates into the grain, in the weld nugget precipitates at the interdendritic region because of the segregation of Ti towards the last-to-solidify interdendritic regions (studied by performing SEM/EDX analysis). Although the hardness increases with the presence of Widmanstätten h phase, the increase in hardness does not necessarily imply an increase in the experimental TSS.

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Section: Tensile Shear Testingmentioning

confidence: 99%

Effect of Widmanstätten η phase on tensile shear strength of resistance spot welding joints of A286 superalloy

Martín

Tiedra

Blanco

2019

Metall. Res. Technol.

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“…A predictive model based on elastic nets with multi input welding parameters, including welding current (WC), welding time (WT), and electrode force (EF), and output as tensile shear load bearing capacity (TSLBC) was proposed. The result showed that it was an amenable tool in the design of the RSW [14]. J.D.…”

Section: Introductionmentioning

confidence: 98%

Expulsion Identification in Resistance Spot Welding by Electrode Force Sensing Based on Wavelet Decomposition with Multi-Indexes and BP Neural Networks

Chen

Xiao

et al. 2019

Applied Sciences

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Expulsion identification is of significance for welding quality assessment and control in resistance spot welding. In order to improve the identification accuracy, a novel wavelet decomposition and Back Propagation (BP) neural networks with the peak-to-peak amplitude and the kurtosis index were proposed to identify the expulsion from electrode force sensing signals. The rapid step impulse and resultant damping vibration of electrode force was determined as a robust indication of expulsion, and this feature was extracted from the electrode force waveform by seven-layer wavelet decomposition with Daubechies5 wavelets. Then, the energy distribution proportion of the decomposed detail signals were calculated, and the highest-energy one was selected as the target signal. Two statistical indexes were introduced in this paper to measure the target signal in overall situation and volatility. The bigger the peak-to-peak amplitude is, the more violent the fluctuation is. Moreover, the higher the kurtosis index is, the stronger the impact is, and the lower the dispersion degree of the data is. Experimental analysis showed that neither the peak-to-peak amplitude nor the kurtosis index could accurately judge the expulsion defect individually, because of the early signal fluctuation, likely affected by the work-piece clamping, work-piece clearance, or the oxide film thickness. Therefore, the BP neural networks were introduced to identify the expulsion defects, which is a mature and stable non-linear pattern recognition method. Testing experiments presented good results with the trained networks and improved the evaluable accuracy effectively in the quality assessment of the resistance spot welding.

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“…ECAUSE of its technological importance, 304 stainless steel (SS) has been the focus of a number of experimental studies [1][2][3][4][5][6][7]. 304 stainless steel was one of the Cr-Ni austenitic stain-less steel in 300 series stainless steel which is extensively used as it possess various applications such as house wear, chemical industry, building material, and food industry because of the excellent corrosion resistance, heat resistance and mechanical properties.…”

Section: Introductionmentioning

confidence: 99%

“…304 stainless steel was one of the Cr-Ni austenitic stain-less steel in 300 series stainless steel which is extensively used as it possess various applications such as house wear, chemical industry, building material, and food industry because of the excellent corrosion resistance, heat resistance and mechanical properties. [2][3] Austenitic stainless steels, characterized by a high work hardening rate and low thermal conductivity. [4] Austenitic stainless steel is thermodynamically stable over a wide range of temperatures and it can be hardened only by cold working.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Cold Work Hardening Behavior of AISI 304 Stainless Steel

2017

Bali 2017 International Conference Proceeding IPCET-17, CM3E-17, ABEMS-17, ICEEE-17, ICCEE-17, ICASE-17, ICAFE-17, EBHRM-17, LH

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In this present study, the cold work hardening behavior of AISI 304 austenitic stainless steel has been investigated as a function of stress rate. For this purpose, tensile specimens were cut from coiled 304 stainless steel stripe with 12 mm width and 0.8 mm thickness. Different stress loads performed on each specimen at room temperature. Microstructures of the specimens were characterized by optical microscope and hardness values were compared. Experimental results showed that, hardness of AISI 304 stainless steel is increased by cold deformation drastically. Because of long homogenous plastic deformation area and very short necking period, grain orientation was only visible close the tensile load. This behavior is evaluated usefully in both production and serving conditions.

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Quality assessment of resistance spot welding joints of AISI 304 stainless steel based on elastic nets

Cited by 25 publications

References 31 publications

Effect of Widmanstätten η phase on tensile shear strength of resistance spot welding joints of A286 superalloy

Effect of Widmanstätten η phase on tensile shear strength of resistance spot welding joints of A286 superalloy

Expulsion Identification in Resistance Spot Welding by Electrode Force Sensing Based on Wavelet Decomposition with Multi-Indexes and BP Neural Networks

Investigation of Cold Work Hardening Behavior of AISI 304 Stainless Steel

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