Design Optimization for Robustness Using Quadrature Factorial Models

This paper describes a robust optimization methodology for designs involving either complex simulations or actual experiments. The methodology adopts a new objective function which consists of an Expected Performance (EP) and a weighted Quality Index (QI). This definition enhances the measure of optimality and robustness. This paper introduces the Quadrature Factorial Experiment to estimate the expected performance and standard deviation. This technique greatly reduces the number of experiments and provides superior results for performance with significant interaction effects and nonlinear variations. The case study applies the proposed methodology to the design of helical gears with minimum peak-to-peak transmission error (PPTE) using the profile modification technique. The robust optimum shows a significant reduction of the expected PPTE compared with previous studies, while maintaining the insensitivity to profile errors, shaft misalignment, and load variation.