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

2026-03-02

AI Summary

This study addresses the challenge in manufacturing process optimization where high-fidelity data are scarce and expensive, while abundant low-fidelity data suffer from systematic bias. To tackle this, the authors propose a systematic multi-fidelity Bayesian calibration framework grounded in the Kennedy–O’Hagan model and multi-fidelity Gaussian processes. The approach explicitly models the discrepancy between high- and low-fidelity data and integrates Bayesian calibration with predictive sampling to quantify uncertainty in the optimal input parameters. A five-stage optimization workflow is developed to support robust decision-making under uncertainty. Validation on two real-world applications—composite curing and injection molding—demonstrates that the framework effectively fuses heterogeneous data sources, significantly enhancing the reliability and practicality of the optimization outcomes.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

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.

Problem

• multi-fidelity
• manufacturing optimization
• data fidelity
• decision analysis
• parameter uncertainty

Innovation

• multi-fidelity Gaussian process
• Bayesian calibration
• model discrepancy
• manufacturing optimization
• decision analysis