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Learning theory applications to product design modeling

Integrated product development increasingly relies upon simulations to predict design characteristics. However, several problems arise in integrated modeling. First, the computational requirements of many sub-system models can prohibit system level optimization. Further, when several models are chained in a distributed environment, overall simulation robustness hinges upon the reliability of each of the individual simulations and the communications infrastructure. A solution to these problems might lie in the application fast surrogate models, which can emulate slow systems or mirror unreliable simulations. Also, in many cases sub-system behavior is described through empirical data only. In such cases, surrogate modeling techniques may be applicable as well. This thesis explores the use of Artificial Neural Network (ANN) algorithms in integrated design simulation. In summary, the thesis focuses on generating a quick to setup, easy to use, and reliable neural network tool for the use within DOME (Distributed Object Modeling Environment). The work was divided in four areas. Understanding the system to be emulated, generating a good exemplar set from the system, designing the neural network, and finally demonstrating the usefulness of the application through case studies. The ANN object is validated on a variety of test problems: from a surrogate LCA, to aiding Dynamic Monte Carlo Simulation. Finally, the ANN is applied to a design application within Ford Motor Company door model where the neural object trained an ANN to emulate an FEA for a car door-seal sub-system. Thesis Supervisor: David Wallace Title: Esther and Harold Edgerton Associate Professor of Mechanical Engineering