.png){kind=icon}
.png){kind=icon}
Return
Functional multi-response quality design based on Bayesian semiparametric model
.png){kind=icon}
PRE
AI
DOI：10.1080/16843703.2026.2662008.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In the case of complex products, a large number of observations can be collected for multiple responses. These observations often display diverse curve patterns depending on time or location. In the quality design of functional responses, if the complex relationship between response observations and time or location is not effectively handled, and the non-normality of the responses is not considered, it will affect the predictive accuracy of the model, which in turn will impact the reliability and accuracy of the optimal solution. This paper proposes a new method to tackle the aforementioned quality design problem. Specifically, a semi-parametric mixed-effects modeling strategy is adopted. In the first stage, B-splines are used to fit the relationship between the observations and time or location. In the second stage, the coefficients obtained from the first stage are used as responses to establish a model for their relationship with the input variables. A weighted multivariate quality loss function is then constructed to resolve the optimal parameter settings. Simulation examples and 3D printing examples verify the effectiveness of the proposed method in modeling and optimization.
Keywords:
Quality design
functional multi-response
non-normal distribution
bayesian inference
semiparametric mixed-effects modeling
Journal
Q
IF:
3
Papers:
26
Citations:
0