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Bayesian closed-loop robust process design considering model uncertainty and data quality

Abstract Response-surface-based design optimization has been commonly used in Robust Process Design (RPD) to seek optimal process settings for minimizing the output variability around a target value. Recently, the online RPD strategy has attracted increasing research attention, as it is expected to provide a better performance than offline RPD by utilizing online process feedback to continuously adjust process settings during process operation. However, the lack of knowledge about process model parameter uncertainty and data quality in the online RPD decisions means that this superiority cannot be guaranteed. This motivates this article to present a Bayesian approach for online RPD, which can provide systematic decisions of when and how to update the process model parameters for online process design optimization by considering data quality. The effectiveness of the proposed approach is illustrated with both simulation studies and a case study on a micro-milling process. The comparison results demonstrate that the proposed approach can achieve a better process performance than two conventional design approaches that do not consider the data quality and model parameter uncertainty.