A hybrid transfer learning framework for in-plane freeform shape accuracy control in additive manufacturing. (8th December 2020)

• Record Type: Journal Article
• Authors: Cheng, Longwei; Wang, Kai; Tsung, Fugee
• Is Part Of: IISE transactions. Volume 53:Number 3(2021)
• Page Start: 298
• Page End: 312
• Publication Date: 2020-12-08
• Subjects: Additive manufacturing -- feature transfer -- freeform shape -- functional linear model -- parameter transfer -- shape accuracy control; Industrial engineering -- Periodicals; Systems engineering -- Periodicals; Industrial engineering; Systems engineering; Electronic journals; Periodicals; 670.285
• DOI: 10.1080/24725854.2020.1741741
• Languages: English
• ISSNs: 2472-5854

Abstract

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.