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Research Papers

Modeling of the Vacuum Arc Remelting Process for Estimation and Control of the Liquid Pool Profile

[+] Author and Article Information
Joseph J. Beaman

Professor
Department of Mechanical Engineering,
University of Texas at Austin,
Austin, TX 78712
e-mail: jbeaman@austin.utexas.edu

Luis Felipe Lopez

Graduate Research Assistant
Department of Mechanical Engineering,
University of Texas at Austin,
Austin, TX 78712
e-mail: felipelopez@utexas.edu

Rodney L. Williamson

Advanced Manufacturing Center,
University of Texas at Austin,
Austin, TX 78712
e-mail: rlwilliamson@mail.utexas.edu

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received September 24, 2012; final manuscript received December 11, 2013; published online February 19, 2014. Assoc. Editor: Shankar Coimbatore Subramanian.

J. Dyn. Sys., Meas., Control 136(3), 031007 (Feb 19, 2014) (11 pages) Paper No: DS-12-1315; doi: 10.1115/1.4026319 History: Received September 24, 2012; Revised December 11, 2013

Vacuum arc remelting (VAR) is an industrial metallurgical process widely used throughout the specialty metals industry to cast large alloy ingots. The final ingot grain structure is strongly influenced by the molten metal pool profile, which in turn depends on the temperature distribution in the ingot. A reduced-order model of the solidifying ingot was developed specifically for dynamic control and estimation of the depth of molten liquid pool atop the ingot in a VAR process. This model accounts only for the thermal aspects of the system ignoring other physical domains such as fluid flow and electromagnetic effects. Spectral methods were used to obtain a set of nonlinear dynamic equations which capture the transient characteristics of liquid pool profile variations around a quasi-steady operating condition. These nonlinear equations are then linearized and further simplified by suppressing fast modes. The resulting system was used to construct a linear-quadratic-gaussian (LQG) controller which was tested in a laboratory-scale furnace showing a good performance. A high-fidelity physics-based model is used in real-time to provide information about the solidifying ingot and potential solidification defects.

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References

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Figures

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Fig. 1

Schematic diagram of VAR process (courtesy ATI Allvac)

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Fig. 2

Schematic of the solidifying ingot

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Fig. 3

Analysis of the interface

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Fig. 4

Power balance at the interface

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Fig. 5

Comparison between the high-fidelity model (BAR) and the reduced-order one for τfast=40s

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Fig. 6

Comparison of predicted pool profiles

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Fig. 7

Pool depth control of VAR

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Fig. 8

Experimental results

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Fig. 9

Comparisons between liquid pool depths predicted with BAR and those measured experimentally

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Fig. 10

Thermal gradient in experiment

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