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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ndltd-VTETD-oai-vtechworks.lib.vt.edu-10919-383272021-12-23T05:49:31Z Design of automotive joints: using optimization to translate performance criteria to physical design parameters Zhu, Min Aerospace and Ocean Engineering LD5655.V856 1994.Z58 Automobiles -- Bodies -- Design and construction Joints (Engineering) -- Design and construction 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. 2014-03-14T21:13:56Z 2014-03-14T21:13:56Z 1994 2008-06-06 2008-06-06 2008-06-06 Dissertation Text etd-06062008-165515 http://hdl.handle.net/10919/38327 http://scholar.lib.vt.edu/theses/available/etd-06062008-165515/ en OCLC# 32772064 LD5655.V856_1994.Z58.pdf In Copyright http://rightsstatements.org/vocab/InC/1.0/ xiii, 162 leaves BTD application/pdf application/pdf Virginia Tech |
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LD5655.V856 1994.Z58 Automobiles -- Bodies -- Design and construction Joints (Engineering) -- Design and construction |
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LD5655.V856 1994.Z58 Automobiles -- Bodies -- Design and construction Joints (Engineering) -- Design and construction Zhu, Min Design of automotive joints: using optimization to translate performance criteria to physical design parameters |
description |
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. |
author2 |
Aerospace and Ocean Engineering |
author_facet |
Aerospace and Ocean Engineering Zhu, Min |
author |
Zhu, Min |
author_sort |
Zhu, Min |
title |
Design of automotive joints: using optimization to translate performance criteria to physical design parameters |
title_short |
Design of automotive joints: using optimization to translate performance criteria to physical design parameters |
title_full |
Design of automotive joints: using optimization to translate performance criteria to physical design parameters |
title_fullStr |
Design of automotive joints: using optimization to translate performance criteria to physical design parameters |
title_full_unstemmed |
Design of automotive joints: using optimization to translate performance criteria to physical design parameters |
title_sort |
design of automotive joints: using optimization to translate performance criteria to physical design parameters |
publisher |
Virginia Tech |
publishDate |
2014 |
url |
http://hdl.handle.net/10919/38327 http://scholar.lib.vt.edu/theses/available/etd-06062008-165515/ |
work_keys_str_mv |
AT zhumin designofautomotivejointsusingoptimizationtotranslateperformancecriteriatophysicaldesignparameters |
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1723965660124413952 |