Construction and validation of distribution‐based regression simulation metamodels

Metamodels are used as analysis tools for solving optimization problems. A metamodel is a simplification of the simulation model, representing the system's input–output relationship through a mathematical function with customized parameters. The proposed approach uses confidence intervals as an acceptance procedure, and as a predictive validation procedure when new points are employed. To improve the knowledge about the system, the response is depicted by modelling both the mean and variance functions of a normal distribution along the experimental region. Such metamodels are specially useful when the variance of the output varies significantly. These metamodels may be used for minimizing product quality loss and improving production robustness. The development of such metamodels is illustrated with two examples.