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University of Wisconsin - Madison

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Polymer Engineering Center > Research > Computer-aided Process and Design Optimization Tools

 

Sophisticated commercial computer-aided engineering (CAE) tools for various polymer-processing methods have been available and are now widely used in industrial practices. As a result, the design and manufacturing of plastic parts have been literally transformed from a “black art” to an engineering discipline based on scientific principles. It is well recognized that computer simulation tools help the engineer gain process insight and pinpoint blind spots and the problems usually overlooked. Nevertheless, there remains a missing link in CAE, which lies in the ability to effectively identify the optimal design and processing parameters, as it is hampered by the sheer amount of computer-generated data and complex, non-linear interactions among the various design and processing parameters. This proposed research is to develop an integrated computer-aided engineering (CAE) optimization tool that couples commercial computer simulation program with optimization algorithm/tool to intelligently determine the optimal design and processing parameters, as shown in the below figure.

Diagram: Design --%gr; Optimization --%gr; Predictions --&gr; Simulation

Iterative procedures to determine the optimal design and processing parameters by coupling the computer process simulation tool with the optimization tool

The idea is to perform iterative Design of Experiments (DOE) "numerically" by using computer simulation program as a number crunching tool. The objective of this research is to develop a methodology that enables the engineer to represent the optimal design with an Objective Function (OF) and certain constraint conditions based on the first principle or in an ad hoc fashion. After that, the process simulation program is used to generate data corresponding to an initial set of input design and processing parameters. Given the objective function, the predicted output, and basic “built-in” rules and intelligence, the optimization tool recommends modifications to the input variables for a new round of simulation, akin to what an experienced engineer will typically do. With the updated results, the optimization tool analyzes the effect of changing the input parameters on the value of the objective function and then suggests a new set of input variables. This kind of iterations continues automatically until the best combination of the input parameters that either minimizes or maximizes the objective function is found.

Anticipated benefits:
  • Extended effectiveness of computer simulation and optimization tool and knowledge in codifying the optimal design and processing conditions.

  • Providing an integrated computer-aided engineering (CAE) optimization tool that intelligently identifies the optimal design and processing parameters.


Polymer Engineering Center

University of Wisconsin-Madison

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