Research by Associate Prof. Linhan Ouyang that proposes a closed-loop robust design method to provide systematic decisions of when and how to update the process model parameters was featured in IISE Transactions on August 2019. As the flagship journal of the Institute of Industrial and Systems Engineers, IISE Transactions publishes original high-quality papers on a wide range of topics of interest to industrial engineers who want to remain current with the state-of-the-art technologies. The refereed journal aims to foster the engineering community by publishing papers with a strong methodological focus motivated by real problems that impact engineering practice and research. “Our online closed-loop approach considering the effect of data quality can achieve significant improvement in the output performance over the existing two approaches because it directly reduces the Bayesian posterior variation leading to making corrective adjustments to the design variables”, the authors noted. Abstract is copied below.
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 the target value. Recently, online RPD strategy has attracted increasing research interests 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 ignorance of process model parameter uncertainty and data quality in the online RPD decisions cannot guarantee this superiority. This article is motivated 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 in 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.

Fig.1. The main idea of the online optimization strategy

If you are interested in the research, please read the paper
Linhan Ouyang, Jianxiong Chen, Yizhong Ma, Chanseok Park, Jionghua (Judy) Jin. Bayesian closed-loop robust process design considering model uncertainty and data quality[J]. IISE Transactions, 2019, https://doi.org/10.1080/24725854.2019.1636428.

A full version of this article could be viewed at:
https://www.tandfonline.com/doi/abs/10.1080/24725854.2019.1636428?journalCode=uiie21