... Gaussian process (GP) model, where the bottom tier relates the manipulating factors to the process conditions with control variation, and the top tier relates the process conditions to the outcome. It explicitly models the propagation of the control uncertainty to the outcome through the two modeling tiers...

... of a titanium alloy workpiece. The model used is a series of Gaussian process models, each established for a polishing stage at which surface data are gathered. The series of Gaussian process models appear capable of capturing surface changes and variation over the polishing process, resulting in a decision...

... multitask learning (EG-MTL) surface model by fusing surface cutting physics in engineering processes and the spatial data from a number of similar-but-not-identical processes. An iterative multitask Gaussian process learning algorithm is developed to learn the model parameters. Compared...

... process is constructed to obtain simulation data as the other kind of prior information. After implementing the Gaussian process modeling strategy, five uncertain parameters in the model are calibrated with uncertainty. Efficient model predictions with uncertainty quantification are also provided...

... such as lapping and polishing. Therefore, the MRR should be modeled and controlled to maintain the thickness uniformity. In this paper, a PDE-constrained Gaussian process model is developed based on the global Galerkin discretization of the governing partial differential equations (PDEs). Three features...