Uncertainty Estimation of the Optimal Decision with Application to Cure Process Optimization

TL;DR

This paper introduces a surrogate model framework to estimate and analyze the uncertainty of optimal manufacturing decisions, demonstrated through a composite cure process simulation.

Contribution

It presents a novel method for quantifying decision uncertainty in surrogate-based optimization, enhancing decision reliability in manufacturing processes.

Findings

Effective uncertainty estimation for optimal decisions.

Improved understanding of decision sensitivity.

Application to composite cure process simulation.

Abstract

Decision-making in manufacturing often involves optimizing key process parameters using data collected from simulation experiments. Gaussian processes are widely used to surrogate the underlying system and guide optimization. Uncertainty often inherent in the decisions given by the surrogate model due to limited data and model assumptions. This paper proposes a surrogate model-based framework for estimating the uncertainty of optimal decisions and analyzing its sensitivity with respect to the objective function. The proposed approach is applied to the composite cure process simulation in manufacturing.