Microstructure, Tensile and Fatigue Behaviour of Resistance Spot Welded Zinc Coated Dual Phase and Interstitial Free Steel

Kishore, Kaushal; Kumar, Pankaj; Mukhopadhyay, Goutam

doi:10.1007/s12540-020-00939-8

Cited by 15 publications

(9 citation statements)

References 53 publications

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“…Therefore, proper management of mechanical property changes is crucial to reduce the risk of damage [20]. Incompatibility between welded joints and base metals, along with imbalances in welding parameters, can further reduce strength and hardness [21]. Careful parameter control is required to maintain structural integrity.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Optimization of welding direction model parameters to enhance the tensile strength of ST 42 steel joints through variation of current strength simulation

Muddin,

Ariyanto,

Sriwati

et al. 2024

EEJET

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The welding process plays a central role in the welding industry, where the joint zone undergoing the welding process experiences structural and mechanical property changes. This research evaluates the comparison of current strength and parameters in the directional welding joint model, a critical aspect of addressing common weaknesses in welded joints. The research objects include four current levels (100, 120, 140, 160 A) and three types of welding directions (longitudinal, transversal, and combination). The aim of this study is to detect the optimal combination in welded joints that can produce a maximum tensile strength ratio. The research method involves tensile testing on various specimen models of joint types at specific current strength levels. The research results indicate that at the current strength level of 120 A, the combined directional welding joint model (longitudinal+transverse) provides a maximum tensile strength reaching 335.370 MPa. This finding stands out significantly, surpassing the tensile strength values at other current levels and welding model types, such as at 100 A (331.574 MPa), 140 A (332.315 MPa), and 160 A (332.685 MPa). This discovery highlights that the combined joint model yields a substantial improvement in joint strength, making it an optimal solution for various current strength levels and joint models. The key feature of this research involves specific recommendations for the welding industry, including guidelines on selecting optimal parameters to enhance the tensile strength of joints. The directional welding joint models can be a reference in designing welding procedure specifications to incorporate construction elements using ST 42 material. This research contributes both theoretically and practically, offering opportunities for improving efficiency and structural safety in the welding process, thus positively impacting the quality of joints in construction and manufacturing applications

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

Optimization of welding direction model parameters to enhance the tensile strength of ST 42 steel joints through variation of current strength simulation

Muddin,

Ariyanto,

Sriwati

et al. 2024

EEJET

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“…This complex phenomenon substantially impacts the overall quality and integrity of the welded joints. The quality of the weld joint is assessed by nugget size, weld strength (Tensile shear strength, Cross tensile strength, peel strength, fatigue strength), modes of failures, microhardness, and metallographic evaluation (Jagadeesha 2017;Kishore et al 2021;Rao et al 2017). Several methods are available to estimate the quality of welds classified as destructive and non-destructive.…”

Section: H= I 2 Rtmentioning

confidence: 99%

Machine Learning-Based Weldability Assessment and Optimization of Resistance Spot Welding Joints: A Data-Driven Framework

Gurav,

Shrivastava

2023

Proceedings of the International Conference on Industrial Engineering and Operations Management

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Resistance spot welding (RSW) is a crucial metal joining process across diverse industries, with a prominent role in the automotive industry. This paper proposes a conceptual framework of data-driven welding analytics by building a machine learning (ML) model and parameter-less optimization algorithm to assess the weldability of spot welded joints. It leverages experimental and simulation data to train the ML model, enabling it to predict weld quality accurately. Various regression-based Machine learning algorithms are implemented to predict weld nugget size. The performance of these algorithms is evaluated by three performance measures: Root Mean Square Error (RMSE), Mean Square Error (MSE), and R-squared. The result shows that Gaussian process regression outperforms other ML algorithms in predicting weld quality. Furthermore, in addition to welding analytics, the proposed framework incorporates a Teaching learning-based optimization (TLBO) algorithm to optimize expected weld quality. The robustness of this optimization algorithm is confirmed through 10 independent runs, maintaining consistent population size and termination criteria.

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“…The prestraining levels extend significant effect on high cycle fatigure life of joint. Kishore et al [14] studied the influcen of spot welding current on strength and nugget diameter of Zn coated DP and interstitial free steel and reported that the strength and nugget diameter of joints increases at increased levels of welding up to the evolution of expuilsion. Sevim [15] investigated fracture toughness of galvanized DP450 steel spot joints and concluded that the fracture toughness of spot joints depends on the diameter of nugget, thickness of sheet, tensile force of rupture, hardness and ratios of nucleus size.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Resistance Spot Welding Parameters on Microstructure and Strength of DP800 Steel Joints Using Response Surface Methodology

Rajarajan¹,

Sonar²,

Sivaraj

et al. 2022

Advances in Materials Science

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The main objective of this paper is to analyze the direct and interaction effect of resistance spot welding (RSW) parameters on microstructure and strength of DP800 steel joints using response surface methodology (RSM). The DP800 steel sheets were spot welded in straight lap and cross lap joint configuration using RSW. The relationship between the RSW parameters, tensile shear fracture load (TSFL) and nugget zone hardness (NZH) was established employing statistical regression analysis and validated using Analysis of Variance (ANOVA). The DP800 steel joints made using welding current of 5.0 kA, electrode pressure of 4.0 MPa, and welding time of 1.50 s displayed maximum STRAIGHT-TSFL of 21.7 kN, CROSS-TSFL of 17.65 kN, and NZH of 589 HV0.5 respectively.

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Microstructure, Tensile and Fatigue Behaviour of Resistance Spot Welded Zinc Coated Dual Phase and Interstitial Free Steel

Cited by 15 publications

References 53 publications

Optimization of welding direction model parameters to enhance the tensile strength of ST 42 steel joints through variation of current strength simulation

Optimization of welding direction model parameters to enhance the tensile strength of ST 42 steel joints through variation of current strength simulation

Machine Learning-Based Weldability Assessment and Optimization of Resistance Spot Welding Joints: A Data-Driven Framework

The Effect of Resistance Spot Welding Parameters on Microstructure and Strength of DP800 Steel Joints Using Response Surface Methodology

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