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Research Papers

Simulations of Die Casting With Uncertainty Quantification

[+] Author and Article Information
Shantanu Shahane

Department of Mechanical Science and Engineering,
University of Illinois at Urbana-Champaign,
Urbana, IL 61801
e-mail: sshahan2@illinois.edu

Soham Mujumdar

Department of Mechanical Science and Engineering,
University of Illinois at Urbana-Champaign,
Urbana, IL 61801
e-mail: sohammujumdar@iitb.ac.in

Namjung Kim

Department of Mechanical Science and Engineering,
University of Illinois at Urbana-Champaign,
Urbana, IL 61801
e-mail: kim847@illinois.edu

Pikee Priya

Department of Mechanical Science and Engineering,
University of Illinois at Urbana-Champaign,
Urbana, IL 61801
e-mail: ppriya@illinois.edu

Narayana R. Aluru

Department of Mechanical Science and Engineering,
University of Illinois at Urbana-Champaign,
Urbana, IL 61801
e-mail: aluru@illinois.edu

Placid Ferreira

Department of Mechanical Science and Engineering,
University of Illinois at Urbana-Champaign,
Urbana, IL 61801
e-mail: pferreir@uiuc.edu

Shiv G. Kapoor

Department of Mechanical Science and Engineering,
University of Illinois at Urbana-Champaign,
Urbana, IL 61801
e-mail: sgkapoor@illinois.edu

Surya Vanka

Department of Mechanical Science and Engineering,
University of Illinois at Urbana-Champaign,
Urbana, IL 61801
e-mail: spvanka@illinois.edu

1Corresponding author.

Manuscript received June 21, 2018; final manuscript received December 21, 2018; published online February 27, 2019. Assoc. Editor: Y. Lawrence Yao.

J. Manuf. Sci. Eng 141(4), 041003 (Feb 27, 2019) (8 pages) Paper No: MANU-18-1469; doi: 10.1115/1.4042583 History: Received June 21, 2018; Accepted December 23, 2018

Die casting is a type of metal casting in which a liquid metal is solidified in a reusable die. In such a complex process, measuring and controlling the process parameters are difficult. Conventional deterministic simulations are insufficient to completely estimate the effect of stochastic variation in the process parameters on product quality. In this research, a framework to simulate the effect of stochastic variation together with verification, validation, and uncertainty quantification (UQ) is proposed. This framework includes high-speed numerical simulations of solidification, microstructure, and mechanical properties prediction models along with experimental inputs for calibration and validation. Both experimental data and stochastic variation in process parameters with numerical modeling are employed, thus enhancing the utility of traditional numerical simulations used in die casting to have a better prediction of product quality. Although the framework is being developed and applied to die casting, it can be generalized to any manufacturing process or other engineering problems as well.

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Figures

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Fig. 1

Virtual certification framework

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Fig. 2

Temperature profiles (Ra = 105)

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Fig. 3

Velocity profiles (Ra = 105)

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Fig. 4

Velocity profiles (Ra = 106)

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Fig. 6

Solid fraction isosurfaces: (a) 0.1166 s and (b) 0.2332 s

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Fig. 7

Solidification simulation output X = 0.013 m: (a) SDAS (m) and (b) yield strength (MPa)

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Fig. 8

Response surface: solidification time (s)

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Fig. 9

Response surface: maximum of SDAS (m)

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Fig. 10

Response surface: minimum of yield strength (MPa)

Tables

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