Design of Experiments Reduces Time Required to Optimize Complex Design

The traditional approach to optimizing a product or process using computer simulation is to evaluate the effects of one design parameter at a time. Then after it has been optimized the analyst moves to the next variable. The problem with this approach is that interactions between design factors and second order effects mean that this approach is likely to lead down a blind alley. It will result in a locally optimized design that will provide far less performance than the global optimum. Another problem is that many types of simulation take a considerable amount of time, even days, to evaluate a single design iteration. So there is only time to evaluate a small subset of the design space. For these reasons, a number of analysts have begun using design of experiments (DOE) via Response Surface Methods (RSM) to drive the design process. DOE/RSM can be used to develop experiments that examine first order, second order, and multiple factor effects simultaneously with relatively few simulation runs. The result is that the analyst can iterate to a globally optimized design with a far higher level of certainty and in much less time than the traditional approach. This article will show how Dan Cler, Senior Mechanical Engineer for Benét Laboratories, Watervliet, New York, is using DOE/RSM to design a new generation of muzzle brakes.