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Traditional robust parameter design methods mainly focus on the optimization of quantitative quality characteristics. However, computer experiments involving qualitative and quantitative mixed input and output occur frequently in the manufacturing industry, which prompted the authors to develop an effective meta-modeling and optimization technique for such experiments. This article combines the latent variable Gaussian process (LVGP) model and fuzzy set theory to create a mixed multi-response LVGP (MMR-LVGP) model involving qualitative and quantitative mixed input and output. Then, the optimization scheme is established by comprehensively weighing the location and dispersion effects of each quality characteristic to find the joint optimal solution of qualitative and quantitative factors. Numerical and industrial cases are used to illustrate the validity of the proposed method in the modeling and optimization of experimental data with qualitative and quantitative mixed input and output or spatio-temporal structure. The comparison results indicate that the proposed method is preferred over existing methods. [ABSTRACT FROM AUTHOR]

To address the problem of robust parameter design for high-dimensional experimental data, the parallel partial Gaussian process (PPGP) model is adopted to construct the response surfaces between the test factors and the multivariate quality characteristics under the modeling framework of Gaussian process (GP). On this basis, the multivariate quality loss function is used as an optimization index to obtain the optimal parameter design values of the controllable factors. A classic simulation examples and two actual cases are used to verify the effectiveness and advantages of the proposed method. The research results show that compared with the independent modeling of the univariate GP model or the Kriging model, the proposed method can not only deal with the modeling and parameter optimization problems of high-dimensional experimental data effectively, but also obtain more robust optimization results and higher operational efficiency. [ABSTRACT FROM AUTHOR]

In this work, the phase composition and microstructure evolution of vacuum plasma-sprayed MoSi2 coating between room temperature and 1200 °C in air was evaluated and characterized. The results showed that hexagonal MoSi2 (h-MoSi2) became the main phase in the deposited coating, which remained even after 50 h oxidation at 500 °C, exhibiting excellent thermal stability. MoO3 bundles and SiO2 clusters were generated by consuming tetragonal MoSi2 (t-MoSi2) after 1 h, and white powders formed on the coating’s surface after 10-h exposure to air at 500 °C. Most h-MoSi2 transformed to t-MoSi2 at 800 °C; moreover, a protective silica layer formed on the coating surface. Similar phenomenon was observed for the coating exposed to 1000 °C where grain growth also occurred. Vacuum heat treatment at 900 °C effectively improved the thermal stability of the MoSi2 coating. The formation of silica layer alleviated negative effects of structural defects and helped the MoSi2 coating serve as a protective coating for varied substrates. [ABSTRACT FROM AUTHOR]