Research Papers

Identification of Die Thermal Dynamics Using Neural Networks

[+] Author and Article Information
Jaho Seo, Amir Khajepour

Jan P. Huissoon

Department of Mechanical and Mechatronics Engineering,  University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canadajph@mecheng1.uwaterloo.ca

J. Dyn. Sys., Meas., Control 133(6), 061008 (Nov 11, 2011) (9 pages) doi:10.1115/1.4004045 History: Received November 02, 2009; Revised March 16, 2011; Published November 11, 2011; Online November 11, 2011

The objective of this research is to identify a dynamic model that describes the temperature distribution in a die with uncertain dynamics using a neural network (NN) approach. By using data sets obtained from a finite element analysis (FEA) of the thermal dynamics of a die and applying NN off-line and on-line learning algorithms, the die model is identified. This identification approach has been conducted assuming fully measurable and partially measurable states. For the latter, a NN based adaptive observer is employed to estimate unmeasurable states. It is shown that the complex behavior of the die system with cooling channels can be accurately identified in both cases of fully and partially measurable states.

Copyright © 2011 by American Society of Mechanical Engineers
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Figure 1

Shot block (a) and casting die (b)

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Figure 2

Conformal cooling channel of shot block

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Figure 3

Procedure for system identification using NNs

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Figure 4

Monitored points for FEA

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Figure 5

Operating input conditions for FEA

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Figure 6

Temperature changes at node 1,2,3, and 4

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Figure 7

NN structure for off-line training with LM algorithm

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Figure 8

NN based adaptive observer model

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Figure 9

Off-line training results using LM algorithm

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Figure 10

On-line training results using on-line learning algorithm with optimized learning rate

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Figure 11

Verification of performance of NN with adaptive observer using testing data set.



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