A hybrid transfer learning framework for in-plane freeform shape accuracy control in additive manufacturing

Abstract

Shape accuracy control is one of the quality issues of greatest concern in Additive Manufacturing (AM). An efficient approach to improving the shape accuracy of a fabricated product is to compensate the fabrication errors of AM systems by modifying the input shape defined by a digital design model. In contrast with mass production, AM processes typically fabricate customized products with extremely low volume and huge shape varieties, which makes shape accuracy control in AM a challenging problem. In this article, we propose a hybrid transfer learning framework to predict and compensate the in-plane shape deviations of new and untried freeform products based on a small number of previously fabricated products. Within this framework, the shape deviation is decomposed into a shape-independent error and a shape-specific error. A parameter-based transfer learning approach is used to facilitate a sharing of parameters for modeling the shape-independent error, whereas a feature-based transfer learning approach is taken to promote the learning of a common representation of local shape features for modeling the shape-specific error. Experimental studies of a fused filament fabrication process demonstrate the effectiveness of our proposed framework in predicting the shape deviation and improving the shape accuracy of new products with freeform shapes.