A Decision Analysis Framework for High-fidelity and Low-fidelity Systems with Applications in Manufacturing Processes

TL;DR

This paper introduces a Bayesian decision analysis framework using multi-fidelity Gaussian processes to optimize manufacturing processes by effectively combining high- and low-fidelity data sources, improving decision-making under uncertainty.

Contribution

It presents a novel systematic Bayesian calibration method with multi-fidelity GPs and an integrated workflow for manufacturing optimization.

Findings

Framework effectively combines data sources for better optimization.

Demonstrated success in composite cure cycle and injection molding.

Supports decision-making under parameter uncertainty.

Abstract

Optimizing complex manufacturing processes often involves a trade-off between data accuracy and acquisition cost. High-fidelity data are accurate but limited, while low-fidelity data are abundant but often biased. Balancing these two sources is critical for efficient manufacturing optimization. To address this challenge, we develop a decision analysis framework based on multi-fidelity Gaussian process (GP) modeling based on the Kennedy-O'Hagan (KOH) framework. We propose a systematic Bayesian calibration approach using multi-fidelity GPs that explicitly quantifies the model discrepancy, and an algorithm that combines posterior sampling of calibration parameters with predictive sampling to characterize the distribution of optimal input settings and their associated uncertainty. These components are integrated into a five-stage practical workflow for the optimization of manufacturing processes. Through an illustrative example and two real-world applications in composite cure cycle optimization and injection molding process control, we demonstrate how the framework integrates information from both high-fidelity and low-fidelity data sources to support decision-making under parameter uncertainty.