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Research Papers

A Correlation Analysis of Cooling-Induced Temperature Changes

[+] Author and Article Information
Tiebao Yang, Xiang Chen

Department of Mechanical, Automotive & Materials Engineering, University of Windsor, Windsor, ON, N9B 3P4, Canada

Henry Hu

Department of Mechanical, Automotive & Materials Engineering, University of Windsor, Windsor, ON, N9B 3P4, Canadahuh@uwindsor.ca

Yeou-li Chu, Patrick Cheng

Department of Research and Development, Ryobi Die Casting (USA) Inc., Shelbyville, IN 46176-9720

J. Manuf. Sci. Eng 131(1), 011003 (Dec 12, 2008) (7 pages) doi:10.1115/1.3006315 History: Received January 15, 2007; Revised July 30, 2008; Published December 12, 2008

Proper temperature control is essential for producing superior quality components and yielding high production rates in high temperature manufacturing processes. Due to the closure of machine tools such as casting dies during production, it is usually very difficult to monitor their local surface temperatures without destructively inserting thermal sensors into them. However, the measurement of cooling water temperature at the coolant outlet is relatively unproblematic. This work demonstrates that there is a correlation between these two temperatures. As an example, the effect of the local surface temperature of a casting die on the cooling water outlet temperature is analyzed from a laboratory die casting process simulator. Based on the system identification theory, a control-oriented linear time-invariant model has been developed, which correlates the local die temperature to the cooling water outlet temperature. The model enables the prediction of the local die temperature with the measurements of the cooling water temperature. Thus, it provides an alternative approach other than the destructive sensing method to acquire the local die temperatures, and the model could be applied to design a real-time temperature control system for die cooling process.

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Copyright © 2009 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

System with disturbance

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Figure 2

Schematic of a laboratory die casting process simulator

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Figure 3

Structure of a die insert

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Figure 4

Schematic of a DACS

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Figure 5

Schematic of the hardware structure

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Software subroutines

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Die insert temperatures

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Water outlet temperatures

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Water flow rates

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Figure 10

Denoised water flow rates

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Figure 11

Comparison between measured data and predicted data

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Comparison between measured data and predicted data for model validation

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Correlation functions: (a) correlation function of residuals from output y, (b) cross-correlation function between input u1 and residuals from output y, and (c) cross-correlation function between input u2 and residuals from output y.

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Figure 14

3D plot of temperature relations

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