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Defect minimizing control of low pressure die casting

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Title: Defect minimizing control of low pressure die casting
Author: Shi, XinMei
Degree Doctor of Philosophy - PhD
Program Electrical and Computer Engineering
Copyright Date: 2012
Publicly Available in cIRcle 2012-07-16
Abstract: Controlling and eliminating defects, such as macro-porosity, in die casting processes is an on-going challenge for manufacturers. Current strategies for eliminating macro-porosity focus on the execution of pre-set casting cycles, die structure design or the combination of both. To respond to process variability and mitigate its negative effects, advanced process control methodology has been developed to dynamically drive the process towards optimal dynamic or static operational conditions, hence minimizing macro-porosity in the casting. In this thesis, a Finite Element heat transfer model has been developed to predict the evolution of temperatures and the volume of encapsulated liquid in a casting with a high propensity to form macro-porosity. The model was validated by comparison to plant trial data. A virtual process has then been developed based on the model to simulate the continuous operation of a real process, for use as a platform to evaluate a controller’s performance. Since macro-porosity cannot be measured during casting, die temperature has been used as an indirect indicator of this defect. A model-based methodology has been developed to analyze the correlation between die temperature and encapsulated liquid volume, a precursor to the formation of macro-porosity. This methodology is employed to assess the suitability of different in-cycle die temperatures for use as indicators of macro-porosity formation. The optimal locations have then been determined to monitor die temperatures for the purpose of minimizing macro-porosity. A nonlinear state-space model, based on data from the virtual process, has been developed to provide a reliable representation of this virtual process. The control variable-driven portion exhibits linear dynamic behavior with nonlinear static gain. The resulting MIMO state-space model facilitated the design of a controller for this process. Finally, the performance of the nonlinear model-based predictive controller was evaluated using the virtual process. Independent of the initial state of the process - i.e. steady state or startup, the controller exhibited the capability to automatically adjust the process toward the dynamic or static optimal operational condition during disturbances examined. The advanced control methodology developed for LPDC provides a novel solution to improve the operational conditions in die casting process.
URI: http://hdl.handle.net/2429/42720
Scholarly Level: Graduate

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