Prediction and Optimization of Weld Bead Geometry of MIG Welded Stainless Steel 202 Plates

Sood, Shruti; Mishra, Prithu; Pandit, Mayank; Khanna, Pradeep

doi:10.1007/978-981-16-2794-1_64

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…Sood et al forecasted and optimized the weld bead geometry employing a central composite facecentered (CCF) design method during MIG welding of stainless steel. RSM was used to visualize the findings, and the ANOVA approach was used to ensure that the model was appropriate [17]. When the number of contributing process parameters are large (6)(7)(8)(9)(10)(11)(12)(13)(14)(15), it is required to adopt a screening method for selecting significant factors for modeling and optimizing purpose.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Process Variables for Prediction of Penetration Depth of HSLA Steel Welds Using Response Surface Methodology

Pathak

Kumar

Singh

et al. 2022

KEM

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The statistical model is created for predicting penetration depth in an alternating current-based additional axial magnetic field controlled shielded metal arc welding of ASTM A 516 Gr.70 steel. The design for the trials is developed using the Placket-Burman design and response surface methodology. The created model determines the optimum process variables for getting excellent penetration depth. The input variables (current, magnetic field density, and magnetic frequency) are chosen for a response like penetration depth. This model can predict the main effects and the interacting effects of three process variables. The findings reveal that a higher current value with a low magnetic field density leads to deeper penetration and vice versa. Furthermore, a greater penetration depth is achieved at lower magnetic field density and higher magnetic frequency. With a desirability of 98.8%, the optimum process variables are 110 A, 0 mT, and 60 Hz. The predicted response values produced from the regression equation based upon process variables are extremely similar to the observed output, demonstrating the usefulness of second-order regression equations. For improved joint efficiency, a high level of penetration is needed.

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