Multivariate yield maximization using CAD/CAE models: Efficient approximations based on mean and variance

A product contributes to yield if all of its performance functions fall within their upper and/or lower limits. For example, a piston connecting rod may be required to provide rigidity along several axes. The actual connecting rod deflection will vary, depending on variations in the materials and forging conditions, but the deflection must remain less than an upper limit. Designing for maximum yield for multivariate performance limits is a difficult task. Direct optimization may require excessive computing resources. We discuss two efficient methods for yield improvement; 'performance centering' and a method based on Taguchi's 'parameter design' philosophy. Both are shown to be motivated by the Chebychev inequality. It is important to remember that these are approximate methods. An example shows that they may produce sub-optimal yield, even when the random components of the performance functions are independent and identically distributed.