Resistance spot welding of AISI-316L SS and 2205 DSS for predicting parametric influences on weld strength – Experimental and FEM approach

Krishnan, Vignesh; Perumal, A.; Velmurugan, P.

doi:10.1016/j.acme.2019.05.002

Cited by 23 publications

(19 citation statements)

References 22 publications

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“…Hayat 22 and Vignesh et al. 32 reports that the material having higher thermal conductivity produces larger nugget thickness. This present study also explores the same result.…”

Section: Resultsmentioning

confidence: 99%

Influence of resistance spot welding process parameters on dissimilar austenitic and duplex stainless steel welded joints

Krishnan

Elayaperumal

Paramasivam

2020

Proceedings of the Institution of Mechanical Engineers, Part E:

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This paper examines the impact of welding parameters on tensile shear fracture load, nugget geometry and microstructure of resistance spot welds (RSW) of austenitic stainless steel AISI 316 L and duplex stainless steel 2205 under lap shear loading condition. The macroscopic examination resulted that many of the nugget lengths were nearer to and higher than the AWS recommended value 4√t and failed at higher tensile shear load. Nugget height for DSS 2205 side was higher in comparison with AISI 316 L due to higher thermal conductivity of duplex stainless steel. Three welding parameters mainly welding current of 9 kA, heating cycle of 9 and electrode tip diameter of 6 mm were discovered as most effectual parameters on the tensile shear load and microstructure of weldments. Heterogeneous hardness was observed in the fusion zone due to the transition of equiaxed to columnar grains takes place in the both sides of nugget edge. DSS HAZ nearby BM observed higher hardness and ASS HAZ nearby BM reported lower hardness. WMZ Microstructure confirmed that thickness of austenite layers increased with heat input. Also, an unmixed zone in the microstructure identified as HAZ which contains delta ferrite. Scanning Electron Microscope (SEM) images in the nugget zone for different welding parameters confirmed that Intra-Granular Austenite (IGA) highly developed at higher welding current. SEM fractrograph for the tensile sheared specimens at higher and lower heat input confirmed the ductile type fracture even failed at Inter-Facial (IF) mode. Nugget area and nugget hardness were positively correlated with Tensile Shear Fracture Load (TSFL).

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“…Hayat 22 and Vignesh et al. 32 reports that the material having higher thermal conductivity produces larger nugget thickness. This present study also explores the same result.…”

Section: Resultsmentioning

confidence: 99%

Influence of resistance spot welding process parameters on dissimilar austenitic and duplex stainless steel welded joints

Krishnan

Elayaperumal

Paramasivam

2020

Proceedings of the Institution of Mechanical Engineers, Part E:

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“…The damage parameters and material properties of AISI 316LN stainless steel are listed in Tables 5 and 6, along with those of previous reports. 32–34…”

Section: Finite Element Modelmentioning

confidence: 99%

Simulation of the round insert face milling process of AISI 316LN stainless steel with machining-based plastic behavior modeling

Kim

Kwon³

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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A proper plastic behavioral model is required to simulate metal cutting. Here, we model the plastic behavior of AISI 316LN stainless steel during face milling. We used a numerical approach to derive a plasticity model appropriate for machining; a two-dimensional cutting force prediction and a genetic algorithm were conducted for that. The force prediction was performed considering a geometrical relationship between the work material and cutting tool. We used the Johnson-Cook (JC) constitutive material model, and initial model parameters were obtained via tension testing at low strain rates (0.001–1 s−1). The genetic algorithm optimized the model parameters; the predictive accuracy with respect to cutting force was high in the model with optimized parameters. We used the optimized JC model for finite element analysis and simulated face milling with a round insert. We measured the cutting forces to validate our modeling approach; the simulated and measured principal forces were in good agreement (error rate ≤ 3.9% under all machining conditions). Our model improved the accuracy of plastic behavior prediction by 93.0% versus the original model. The high accuracy was retained even when the machining environment changed.

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“…The variety of stainless steel sheet metals can be welded by using RS Welding process. Among several stainless steel sheets the SS steel grade of 316L is a great choice among global manufacturers owing to its superior corrosion resistance due to the existence of molybdenum which avoids chloride corrosion, higher in ductility and strength [3]. However, due to the presence of high nickel content makes 316L SS costlier [4].…”

Section: Introductionmentioning

confidence: 99%

Predicting Parametric Influence of Resistance Spot Welding Process Parameters on Tensile Shear Load Using Optimization Technique

Krishnan¹

2022

Preprint

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The stainless steels such as Austenitic Stainless Steel (ASS) and Duplex Stainless Steel (DSS) are joined to optimize the Resistance Spot Welding (RSW) process parameters and predict the parametric influence on the response of Tensile Shear Load (TSL). The Response Surface Methodology (RSM) is an optimization technique which is used for optimizing the process parameters on TSL and with the help of these results a satisfactory quadratic mathematical model has been developed to predict the response. The optimal parameters and their levels are found and reported as follows: welding current = 9 kA, welding time = 0.18 seconds and electrode tip radius = 3 mm. The actual and predicted values of TSL for the optimized parameters are 17.6 kN and 17.9 kN respectively. The developed quadratic model is efficiently predicted the response with an average error percentage of 2.18. The significant and insignificant terms in the models has been identified by 95% confidence level using ‘p’ test. The insignificant terms are removed from the model and the ANOVA table is formulated only with the significant terms. Significance or effect of each term in the ANOVA table is identified by calculating the percentage of contribution and noticed that welding current has the highest significance (46%) on TSL. The macroscopic examination confirmed that the nugget is initially formed in DSS side due to its higher thermal conductivity and resistivity. The tensile shear-strain graphs are observed with the percentage of elongation varies from the minimum of 5.18 to the maximum of 8.57 which confirm the ductile failure.

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Resistance spot welding of AISI-316L SS and 2205 DSS for predicting parametric influences on weld strength – Experimental and FEM approach

Cited by 23 publications

References 22 publications

Influence of resistance spot welding process parameters on dissimilar austenitic and duplex stainless steel welded joints

Influence of resistance spot welding process parameters on dissimilar austenitic and duplex stainless steel welded joints

Simulation of the round insert face milling process of AISI 316LN stainless steel with machining-based plastic behavior modeling

Predicting Parametric Influence of Resistance Spot Welding Process Parameters on Tensile Shear Load Using Optimization Technique

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