Design Exploration and Prediction of Automotive Hood Designs based on Non-Uniform Feature Parameters

Abstract

The design of automotive structures is a multi-objective problem that includes, light weighting, manufacturability and overall performance. Light weighting, while not compromising crash worthiness, requires careful placement of features in automotive body components. The current design process of automotive hoods involve the use of designs from previous generations as benchmark upon which new designs are evolved. The design obtained from this evolution process must meet the required performance targets. However, in this process of design by evolution, it becomes impossible to generate new designs or adapt design ideas from other models. In addition, the lack of information on performance for a set of mix and match of features from other designs, make it difficult to adapt features by cross designing. There is a gap needed to be filled to assist designers with cross designing of hood models so that the features from one hood can be used on another. The uniqueness of each design and presence of non-uniform parameters makes it difficult to compare two or more designs and extract useful feature information. It is necessary to use unconventional methods to compare the performance and pick the best suitable design. This paper aims to fill this gap by introducing an innovative approach to use a non-uniform parametric study for design exploration in order to make valuable suggestions to the designer. The proposed method uses data sets produced from finite element analysis (FEA), for a given set of loads. Based on designer preference, the response data generated from this FEA can be processed in three ways: 1) analyze for a specific hood model 2) analyze for a larger set that includes features from multiple hoods at the same time 3) analyze based on specific hood attributes (area, curvature, etc.) instead of individual feature parameters. The final predictions will provide the designer with parameterized surface models with potentially new designs adapted from a range of models. This method can be extended to other components and domains that use feature-based parametric designs.