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Development of a numerical optimization methodology for the aluminum alloy wheel casting process

University of British Columbia

Aluminum alloy wheel manufacturers face on-going challenges to produce high quality wheels and increase production rates. Improvements are generally realized by modifying the wheel and die designs and continually improving the manufacturing processes. Conventionally, these improvements have been realized by trial-and-error, building on past practice or experience. This approach typically results in long design lead times, high scrap rates and less than optimal production rates. The work presented in this study seeks to reduce the reliance on trial-and-error techniques by developing a new methodology to optimize the wheel casting process through the combination of a casting process model and open-source numerical optimization algorithms. The casting process model utilized in this method was developed in the commercial finite element package Abaqus™ and was validated through plant trials. An open source optimization module Python Scipy.optimize has been employed to perform the optimization. The work focuses on optimizing the cooling conditions in a low-pressure die-casting (LPDC) process used to produce automotive wheels. Specifically cooling channel timing was selected because of the critical role heat extraction plays on casting quality, both in terms of dendrite cell size and the formation and growth of porosities. The methodology was first developed with a series of test problems ending with an L-shaped geometry that employed the major features of the wheel casting process. The most suitable approach, based on the test problems, was then applied to the optimization of a 2-D axisymmetric prototype wheel die structure. The outcome revealed that numerical optimization coupled with a state-of-the-art process model has the potential to dramatically improve the method of determining cooling channel timings while also improving the product quality and process performance. The utility of the optimization methodology was found to depend on the accuracy of the casting process model. Significant challenges remain before widespread implementation of this methodology can occur in industry. Possible directions for further developments have been identified. In summary, this study represents one of the initial applications of a numerical optimization methodology to wheel casting, and that with further development; it will become an effective tool for process and die design optimization.

Text

2016-04-20

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/57699

Materials Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/