Browsing by Subject "Design Optimization"

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    Design Optimization of Injection Molds with Conformal Cooling for Additive Manufacturing
    (Office of the Vice Chancellor for Research, 2015-04-17) Wu, Tong; Jahan, Suchana A.; Kumaar, Praveen; Tovar, Andres; El-Mounayri, Hazim; Zhang, Yi; Zhang, Jing; Acheson, Doug; Nalim, M. Razi
    Abstract This is a framework for optimizing additive manufacturing of plastic injection molds. The proposed system consists of three modules, namely process and material modeling, multi-scale topology optimization, and experimental testing, calibration and validation. Advanced numerical simulation is implemented for a typical die with conformal cooling channels to predict cycle time, part quality and tooling life. A thermo-mechanical topology optimization algorithm is being developed to minimize the die weight and enhance its thermal performance. The technique is implemented for simple shapes for validation before it is applied to dies with conformal cooling in future work. A method for designing a die with porous material which can be produced in additive manufacturing is developed. Also a comprehensive set of systemic design rules are developed and to be integrated with CAD modeling to automate the process of obtaining viable plastic injection dies with conformal cooling channels. Finally, material modeling using simulation as well as design of experiments is underway for obtaining the material properties and their variations.
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    Material design using surrogate optimization algorithm
    (2015-02-28) Khadke, Kunal R.; Tovar, Andrés; Zhu, Likun; El-Mounayri, Hazim
    Nanocomposite ceramics have been widely studied in order to tailor desired properties at high temperatures. Methodologies for development of material design are still under effect. While finite element modeling (FEM) provides significant insight on material behavior, few design researchers have addressed the design paradox that accompanies this rapid design space expansion. A surrogate optimization model management framework has been proposed to make this design process tractable. In the surrogate optimization material design tool, the analysis cost is reduced by performing simulations on the surrogate model instead of high fidelity finite element model. The methodology is incorporated to and the optimal number of silicon carbide (SiC) particles, in a silicon-nitride(Si3N4) composite with maximum fracture energy [2]. Along with a deterministic optimization algorithm, model uncertainties have also been considered with the use of robust design optimization (RDO) method ensuring a design of minimum sensitivity to changes in the parameters. These methodologies applied to nanocomposites design have a significant impact on cost and design cycle time reduced.