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Material evaluation and selection processes to enable design for manufacture

In order to optimize product designs it is necessary to quickly evaluate many candidate materials in terms of performance and processing costs. Evaluation using physical prototypes yields concrete results but is time intensive and costly when dealing with multiple optimization objectives. As an alternative, computer aided simulation is a reliable means of material evaluation and selection, is increasingly available to smaller companies due to the shrinking cost of computation, and is essential for handling the dual optimization objectives of manufacturability and performance in a timely and cost effective manner. To support this thesis, the author first examines iRobot Corporation's current process of experimental trial and error for evaluating and selecting a polymer material for use in the wheels of its robotic military vehicles. The author then demonstrates that the experimental derived performance results can be reasonably predicted using the viscoelastic properties of polymers, as captured in such models as the standard linear solid model, and that this predictability can be used to quickly simulate wheel performance with computer aided engineering (CAE) tools. Finally, the author performs a cost analysis of the current material evaluation/selection process versus the CAE approach to show the best path forward for incorporating CAE tools into the design process of smaller corporations like iRobot. Thesis Supervisor: Stephen C. Graves Title: Abraham J. Siegel Professor of Management Thesis Supervisor: Randolph E. Kirchain Jr. Title: Assistant Professor, Materials Science and Engineering & Engineering Systems Division