面向高保真与低保真系统的决策分析框架及其在制造过程中的应用
A Decision Analysis Framework for High-fidelity and Low-fidelity Systems with Applications in Manufacturing Processes
AI总结：
本文提出基于多保真高斯过程的决策分析框架，用于在制造过程中平衡高保真与低保真数据，以实现高效优化。
摘要：
优化复杂制造过程往往需要在数据准确性与获取成本之间进行权衡。高保真数据准确但有限，而低保真数据丰富但通常存在偏差。平衡这两种数据源对于高效制造优化至关重要。为解决这一挑战，我们基于Kennedy-O'Hagan（KOH）框架开发了一个基于多保真高斯过程（GP）建模的决策分析框架。我们提出了一种系统性的贝叶斯校准方法，利用多保真GP进行校准，明确量化模型偏差，并提出一种结合校准参数后验抽样与预测抽样的算法，以表征最优输入设置的分布及其相关不确定性。这些组件整合成一个五阶段的实用工作流程，用于制造过程的优化。通过一个示例和两个实际应用——复合材料固化周期优化和注塑成型过程控制——我们展示了该框架如何整合来自高保真和低保真数据源的信息，以支持参数不确定性下的决策制定。
英文摘要：
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.
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