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Multi-response online parameter design based on Bayesian vector autoregression model
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DOI：10.1016/j.cie.2020.106775.png)
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Abstract
En 中文
With the rapid development of the Internet of Things and sensor technology, some noise factors can be measured or estimated during operation and production. This paper develops a new multi-response optimization method that facilitates online parameter design by using the extra information available about observable noise factors. Bayesian multivariate regression model and Bayesian vector autoregressive model are used to consider the uncertainty of both the response model and the noise model. The Monte Carlo procedure is employed to obtain the predictions of multiple correlated noise factors from their posterior predictive distribution. The proposed method provides a convenient way to continuously update process settings during the production, which helps to further reduce the influence of the variability in the noise factor on product or process quality. Two examples are used to illustrate the effectiveness of the proposed method. The results show that the proposed method outperformance the offline parameter design and another online parameter design that does not consider model parameter uncertainty.
Keywords:
Multiple responses
Bayesian inference
Model uncertainty
Noise factor variability
Robust parameter design
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