Design of automotive joints: using optimization to translate performance criteria to physical design parameters
In the preliminary design stage of a car body, targets are first set on the performance characteristics of the overall body and its components using optimization and engineering judgment. Then designers try to design the components to meet the determined performance targets and keep the weight low u...
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| Format: | Others |
| Language: | en |
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Virginia Tech
2014
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| Online Access: | http://hdl.handle.net/10919/38327 http://scholar.lib.vt.edu/theses/available/etd-06062008-165515/ |
| Summary: | In the preliminary design stage of a car body, targets are first set on the performance characteristics of the overall body and its components using optimization and engineering judgment. Then designers try to design the components to meet the determined performance targets and keep the weight low using empirical, trial-and-error procedures. This process usually yields poor results because it is difficult to find a good design that satisfies the targets using trial-and-error and there might even be no feasible design that meets the targets. To improve the current design process, we need tools to link the performance targets and the physical design parameters.
A methodology is presented for developing two such tools for design guidance of joints in car bodies. The first tool predicts the performance characteristics of a given joint fast (at a fraction of a second). The second finds a joint design that meets given performance targets and satisfies packaging and manufacturing constraints. These tools can be viewed as translators that translate the design parameters defining the geometry of a joint into performance characteristics of that joint and vice-versa.
The methodology for developing the first translator involves parameterization of a joint, identification of packaging, manufacturing and styling constraints, and establishment of a neural network and a response surface polynomial to predict the performance of a given joint fast (at a fraction of a second). The neural network is trained using results from finite element analysis of several joint designs. The second translator is an optimizer that finds the joint with the smallest mass that meets given performance targets and satisfies packaging, manufacturing and styling constraints.
The methodology is demonstrated on a joint of an actual car. === Ph. D. |
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