Making Mixtures Robust to Noise and Mixing Measurement Errors

Abstract

Mixture experiments involve the mixing or blending of two or more ingredients to form an end product. Typically, the quality of the end product is a function of the relative proportions of the ingredients and other extraneous process factors such as heat or time. When some of the process variables are either uncontrollable or difficult to control (i.e., noise variables) the goal of a mixture experiment should be to find the mixture amounts and process settings that lead to a product of high quality that is also robust to the noise. Due to the nature of mixture experiments this leads to a constrained optimization problem. This article discusses setting up an appropriate objective function and provides techniques for determining the robust mixture proportions. It is also shown that under certain conditions mixing measurement errors can be handled in the same way.

• Loss Functions
• Robust Design
• Taguchi Methods

Additional information

Notes on contributors

Stefan H. Steiner

Dr. Steiner is an Assistant Professor in the Department of Statistics and Actuarial Sciences. He is a Member of ASQ.

Michael Hamada

Dr. Hamada is a Visiting Associate Professor in the Department of Statistics.