VehicleSDF: A 3D generative model for constrained engineering design via surrogate modeling

TL;DR

VehicleSDF introduces a 3D generative model that explores vehicle design space, satisfying geometric constraints and estimating performance metrics like aerodynamic drag efficiently through surrogate modeling.

Contribution

The paper presents a novel deep learning framework combining generative modeling and surrogate models for constrained vehicle design exploration.

Findings

Generated diverse 3D vehicle models matching geometric specifications.

Enabled quick estimation of aerodynamic drag within the generative pipeline.

Demonstrated effective optimization of designs in the latent space.

Abstract

A main challenge in mechanical design is to efficiently explore the design space while satisfying engineering constraints. This work explores the use of 3D generative models to explore the design space in the context of vehicle development, while estimating and enforcing engineering constraints. Specifically, we generate diverse 3D models of cars that meet a given set of geometric specifications, while also obtaining quick estimates of performance parameters such as aerodynamic drag. For this, we employ a data-driven approach (using the ShapeNet dataset) to train VehicleSDF, a DeepSDF based model that represents potential designs in a latent space witch can be decoded into a 3D model. We then train surrogate models to estimate engineering parameters from this latent space representation, enabling us to efficiently optimize latent vectors to match specifications. Our experiments show that we can generate diverse 3D models while matching the specified geometric parameters. Finally, we demonstrate that other performance parameters such as aerodynamic drag can be estimated in a differentiable pipeline.