Abstract: The fierce competition within the automotive industry requires manufacturers to shorten their development time for a new body, and the CAE-based optimization techniques are arousing wide attention. Compared with traditional size optimization, shape optimization in engineering optimization has greater potential. As a result, mesh morphing technology is first introduced into shape optimization, and a metamodel-based multi-objective shape optimization methodology is presented. Mesh morphing technology is employed to define the shape variables which are then screened through sensitivity analysis. An optimal Latin hypercube sampling is utilized to generate uniformly distributed sample points for fitting the Kriging models with high accuracies. A multi-objective particle swarm algorithm is adopted to perform the optimization where the mass and bending stiffness are defined as the objective functions while maintaining other performance indicators. The conclusion can be drawn that the proposed methodology is used to perform the multi-objective optimization for body-in-white successfully, and engineers can handle the trade-off between the objectives for guiding the decision-making.

Key words: Kriging model, Mesh morphing, Multi-objective particle swarm algorithm, Shape optimization