Space-Filling Designs for Robustness Experiments

TL;DR

This paper introduces space-filling designs tailored for robustness experiments in computer experiments, addressing the challenge of non-uniform noise factor distributions and improving the estimation of control-by-noise interactions.

Contribution

It proposes optimal, computationally efficient space-filling design methods that account for non-uniform noise distributions, enhancing robustness analysis accuracy.

Findings

Proposed designs outperform traditional methods in simulations.

Real industry example demonstrates practical effectiveness.

Improved estimation of control-noise interactions.

Abstract

To identify the robust settings of the control factors, it is very important to understand how they interact with the noise factors. In this article, we propose space-filling designs for computer experiments that are more capable of accurately estimating the control-by-noise interactions. Moreover, the existing space-filling designs focus on uniformly distributing the points in the design space, which are not suitable for noise factors because they usually follow non-uniform distributions such as normal distribution. This would suggest placing more points in the regions with high probability mass. However, noise factors also tend to have a smooth relationship with the response and therefore, placing more points towards the tails of the distribution is also useful for accurately estimating the relationship. These two opposing effects make the experimental design methodology a challenging problem. We propose optimal and computationally efficient solutions to this problem and demonstrate their advantages using simulated examples and a real industry example involving a manufacturing packing line.