Analytical techniques of quality and cost : robust design, design of experiments, and the prediction of mean shift

Abstract

The quality of a product to a large extent determines the success of that product in competitive markets. Measuring and improving quality is thus a primary objective of the designer. The aim of the following work is to provide an introduction to the methods of quality optimization and to illustrate these techniques through examples. Quality is first defined and quantified. The robust design method, which is a technique that focuses on improving quality without adding cost, is then described. Particular attention is paid to experiment design, which is a major factor in the effectiveness and efficiency of the robust design process. The effect of product variability on the mean performance of a product is also explained along with the various ways that can be used to predict a shift in the mean value of the performance. Two examples are then developed. The first focuses on the application of the robust design method to illustrate the steps of the process. The second example primarily focuses on creating a comparison of the Monte Carlo, Latin Hypercube, and star pattern sampling methods on predicting mean shift. The benefits of the star pattern sampling method are apparent through the example. The error in the prediction of mean shift of the star pattern is less than 1%, and the execution time was less than one fifth the times of the Monte Carlo and Latin Hypercube methods.