Design of automotive joints: Using neural networks and optimization to translate performance requirements to physical design parameters

• Published in: Computers & structures Vol. 60; no. 6; pp. 989 - 1001
• Main Authors: Nikolaidis, E., Zhu, M.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 24.07.1996
• ISSN: 0045-7949; 1879-2243
• DOI: 10.1016/0045-7949(96)00013-2

In the preliminary design stage of a car, targets are first set for the performance characteristics of the overall body and its components using optimization and engineering judgment. Then designers try to design components that meet these targets using empirical, trial-and-error procedures. This process usually yields poor results because it is difficult to find a feasible design that satisfies the targets by trial-and-error (a feasible design is one that satisfies packaging and manufacturing constraints). To improve this process, we need tools to link the performance targets with the physical design parameters that define the geometry of the components of a car body. A methodology is presented for developing two tools for design guidance of joints in car bodies. These tools translate the design parameters that define the geometry of a joint into performance characteristics of that joint and vice versa. The first tool, called translator A, rapidly predicts the performance characteristics of a given joint (at a fraction of a second). The second tool, called translator B, finds a joint design that meets or exceeds given performance targets and satisfies packaging and manufacturing constraints. The methodology is demonstrated on a joint of an actual car.