The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Aug. 31, 2021
Filed:
Sep. 16, 2019
Institute of Automation, Chinese Academy of Sciences, Beijing, CN;
Beijing Ten Dimensions Technology Co., Ltd., Beijing, CN;
Zhen Shen, Beijing, CN;
Gang Xiong, Beijing, CN;
Yuqing Li, Beijing, CN;
Hang Gao, Beijing, CN;
Yi Xie, Beijing, CN;
Meihua Zhao, Beijing, CN;
Chao Guo, Beijing, CN;
Xiuqin Shang, Beijing, CN;
Xisong Dong, Beijing, CN;
Zhengpeng Wu, Beijing, CN;
Li Wan, Beijing, CN;
Feiyue Wang, Beijing, CN;
INSTITUTE OF AUTOMATION, CHINESE ACADEMY OF SCIENCES, Beijing, CN;
BEIJING TEN DIMENSIONS TECHNOLOGY CO., LTD., Beijing, CN;
Abstract
A neural network-based error compensation method for 3D printing includes: compensating an input model by a deformation network/inverse deformation network constructed and trained according to a 3D printing deformation function/inverse deformation function, and performing the 3D printing based on the compensated model. Training samples of the deformation network/inverse deformation network include to-be-printed model samples and printed model samples. The deformation network constructed according to the 3D printing deformation function is marked as a first network. During training of the first network, the to-be-printed model samples are used as real input models, and the printed model samples are used as real output models. The inverse deformation network constructed according to the 3D printing inverse deformation function is marked as a second network. During training of the second network, the printed model samples are used as real input models, and the to-be-printed model samples are used as real output models.