A keyhole model in penetration welding with a laser

Dowden, John; Postacıoğlu, Nazmi; Davis, Michael; Kapadia, Phiroze

doi:10.1088/0022-3727/20/1/006

Cited by 103 publications

(62 citation statements)

References 4 publications

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“…This gives the weld zone an approximately rotational symmetry. [2] As the welding speed increases there is less time for heat to conduct and thus there will be less melting in front of, and to the sides of, the keyhole. This results in a generally smaller melt volume and an asymmetrical keyhole/weldpool geometry with an inclined front.…”

Section: Introductionmentioning

confidence: 99%

Melt behavior on the keyhole front during high speed laser welding

Eriksson

Powell

Kaplan

2013

Optics and Lasers in Engineering

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The flow of molten metal on the front wall of a laser generated welding keyhole has been observed by high speed photography, optically measured by mapping the flow of ripples on the liquid surface and theoretically calculated. A clear downward flow can be observed and measured by a Particle Image Velocimetry algorithm. A theoretical calculation of the melt thickness on the keyhole front is also presented. Results indicate that the thickness of the liquid on the keyhole front is similar to that of the resolidified layer found in micrographs of the front if the laser is suddenly turned off. The measured surface ripple flow speeds are between two and four times as high as the theoretical average fluid flow rate.

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Section: Introductionmentioning

confidence: 99%

Melt behavior on the keyhole front during high speed laser welding

Eriksson

Powell

Kaplan

2013

Optics and Lasers in Engineering

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“…Therefore, some simplifications have been frequently made to make the computational task tractable. Dowden et al [13][14][15] assumed a circular keyhole cross section of variable radius and modeled the liquid flow as horizontal flow around the keyhole and as axial motion between two coaxial cylinders. Klemens [16] considered the pressure-driven liquid metal flow resulting from vapor flow while ignoring the Marangoni convection.…”

Section: Introductionmentioning

confidence: 99%

A Convective Heat-Transfer Model for Partial and Full Penetration Keyhole Mode Laser Welding of a Structural Steel

Naresh

Kelly

Martukanitz

et al. 2007

Metall Mater Trans A

101

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In the keyhole mode laser welding of many important engineering alloys such as structural steels, convective heat transport in the weld pool significantly affects temperature fields, cooling rates, and solidification characteristics of welds. Here we present a comprehensive model for understanding these important weld parameters by combining an efficient keyhole model with convective three-dimensional (3-D) heat-transfer calculations in the weld pool for both partial and full penetration laser welds. A modified turbulence model based on PrandtlÕs mixing length hypotheses is included to account for the enhanced heat and mass transfer due to turbulence in the weld pool by calculating spatially variable effective values of viscosity and thermal conductivity. The model has been applied to understand experimental results of both partial and full penetration welds of A131 structural steel for a wide range of welding speeds and input laser powers. The experimentally determined shapes of the partial and full penetration keyhole mode laser welds, the temperature profiles, and the solidification profiles are examined using computed results from the model. Convective heat transfer was the main mechanism of heat transfer in the weld pool and affected the weld pool geometry for A131 steel. Calculation of solidification parameters at the trailing edge of the weld pool showed nonplanar solidification with a tendency to become more dendritic with increase in laser power. Free surface calculation showed formation of a hump at the bottom surface of the full penetration weld. The weld microstructure becomes coarser as the heat input per unit length is increased, by either increasing laser power or decreasing welding velocity.

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“…In this work, theoretical investigations have been performed in order to predict temperature distribution evolution in a particular device model: The nanokeyhole model [19][20][21][22][23][24]. The use of the Colombian approximation 7], Newtonian laws along with the already established Boubaker polynomials expansion scheme BPES [8][9][10][11][12][13][14][15][16][17][18], allowed monitoring temperature dynamical profiles.…”

Section: Resultsmentioning

confidence: 99%