2017
A Statistical Transfer Learning Perspective for Modeling Shape Deviations in Additive Manufacturing
RA-L 2017
Quality control of additive manufacturing applications is required to improve the shape fidelity of the products, which relies on increasing the predictive performance of statistical deviation models for any new shape. Building a single comprehensive model for a wide range of shapes is a very challe