Search Results

Auto components with large manufacturing variation may cause vehicle quality problems after they are assembled. The impact of this variation depends on the assembly process used. If the assembly process is sensitive to the component variation, the impact may be more significant. In this case, an assembly process with lower sensitivity to component variation will solve the problem. This paper presents an example where the component variation largely impacted the quality of the car, and a more robust assembly process solved the problem.

Robust optimization usually requires numerous functional evaluations, which is not feasible when the functional evaluation is time-consuming. Examples in automobile industry include crash worthiness/safety and fatigue life simulations. In practice, a response surface model (RSM) is often used as a surrogate to the CAE model, so that robust optimization can be carried out. However, if the error in the RSM is significant, the solution based on the RSM can be invalid. This paper proposes a method of finding multiple candidate solutions, all of which have similar predicted performances. This approach is effective in finding the close-to-optimum solutions when the model has error, and providing design alternatives. Examples are provided to illustrate the method.