Kai Wang

TL;DR: A hybrid transfer learning framework is proposed to predict and compensate the in-plane shape deviations of new and untried freeform products based on a small number of previously fabricated products and demonstrates the effectiveness of this framework in predicting the shape deviation and improving the shape accuracy of new products with freeform shapes.

TL;DR: This article proposes integrating Multivariate Functional Principal Component Analysis with a three-level structured sparsity idea to develop a novel Hierarchical Sparse MFPCA (HSMFPCA), in which the stage-wise, profile-wise and element-wise sparsity are jointly investigated to clearly identify the informative stages and variables in each eigenvector.

TL;DR: A novel Bayesian process monitoring scheme via transfer learning is proposed to facilitate a cross-product data sharing and validate the superiority of the proposed scheme in terms of both the false alarm rate and detection capability.

TL;DR: An instance-based transfer learning approach which integrates a sufficient amount of auxiliary data from similar processes or products to aid the distribution inference in the authors' target task, and an efficient online algorithm with recursive formulas to update upon every incoming data point.

TL;DR: A monitoring scheme which caters to arbitrary geometric features on a surface which can be integrated with the 3-D laser scanner as a new production module for automatic quality control and decision making is proposed.