DESIGN OF AUTOMOTIVE JOINTS - USING NEURAL NETWORKS AND OPTIMIZATION TO TRANSLATE PERFORMANCE REQUIREMENTS TO PHYSICAL DESIGN PARAMETERS

In the preliminary design stage of a car, targets are first set for th e performance characteristics of the overall body and its components u sing optimization and engineering judgment. Then designers try to desi gn components that meet these targets using empirical, trial-and-error procedures. This process usually yields poor results because it is di fficult to find a feasible design that satisfies the targets by trial- and-error (a feasible design is one that satisfies packaging and manuf acturing constraints). To improve this process, we need tools to link the performance targets with the physical design parameters that defin e the geometry of the components of a car body. A methodology is prese nted for developing two tools for design guidance of joints in car bod ies. These tools translate the design parameters that define the geome try of a joint into performance characteristics of that joint and vice versa. The first tool, called translator A, rapidly predicts the perf ormance 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 manuf acturing constraints. The methodology is demonstrated on a joint of an actual car. Copyright (C) 1996 Elsevier Science Ltd