A hybrid transfer learning framework for in-plane freeform shape accuracy control in additive manufacturing
A hybrid transfer learning framework for in-plane freeform shape accuracy control in additive manufacturing
复制标题
用于增材制造中面内自由形状精度控制的混合迁移学习框架
DOI：
10.1080/24725854.2020.1741741
复制
发表时间：
2020
影响因子：
2.6
通讯作者：
F. Tsung
中科院分区：
文献类型：
--
作者：
Longwei Cheng;Kai Wang;F. Tsung
摘要形状精度控制是增材制造（AM）中最受关注的质量问题之一。通过修改由数字设计模型定义的输入形状来补偿AM系统的制造误差是提高制造产品的形状精度的有效方法。与大规模生产相比，AM工艺通常制造具有极低体积和巨大形状变化的定制产品，这使得AM中的形状精度控制成为一个具有挑战性的问题。在这篇文章中，我们提出了一个混合迁移学习框架来预测和补偿基于少量先前制造的产品的新的和未经尝试的自由形状产品的平面形状偏差。在此框架内，形状偏差被分解为形状无关的错误和形状特定的错误。一个基于参数的迁移学习方法是用来促进共享的参数建模的形状无关的错误，而基于特征的迁移学习方法被用来促进学习的一个共同的表示的局部形状特征建模的形状特定的错误。熔丝制造工艺的实验研究表明，我们提出的框架在预测形状偏差和提高新产品的形状精度与自由形状的有效性。
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.