A statistical transfer learning perspective for modeling shape deviations in additive manufacturing

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 challenging problem, since the error generating mechanism of additive manufacturing applications is usually of high complexity, the amount of training data is usually limited, and the connection among different shapes is unknown. In this study, a novel shape deviation modeling scheme is proposed. In this scheme, the dimensional error of the product is modeled in a parameter-based transfer learning approach. In particular, the shape deviation is decomposed into two components: The shape-independent error and the shape-specific error. The shape-independent error is described by a statistical model that incorporates the engineering knowledge. Guidelines to investigate modeling of the shape-specific error are also given.