Optimal Designs for Thermal Spraying
研究如何设计额外实验来更新热喷涂过程中日间效应的统计模型，提出鲁棒D最优设计以高效估计参数，并通过实际数据比较了参考设计与贝叶斯D最优设计的分析效果。
Summary We consider the problem of designing additional experiments to update statistical models for latent day specific effects. The problem appears in thermal spraying, where particles are sprayed on surfaces to obtain a coating. The relationships between in-flight properties of the particles and the controllable variables are modelled by generalized linear models. However, there are also non-controllable variables, which may vary from day to day and are modelled by day-specific additive effects. Existing generalized linear models for properties of the particles in flight must be updated on a limited number of additional experiments on a different day. We develop robust D-optimal designs to collect additional data for an update of the day effects, which are efficient for the estimation of the parameters in all models under consideration. The results are applied to the thermal spraying process and a comparison of the statistical analysis based on a reference design as well as on a selected Bayesian D-optimal design is performed.