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.
Type:
Grant
Filed:
September 16, 2019
Date of Patent:
August 31, 2021
Assignees:
INSTITUTE OF AUTOMATION, CHINESE ACADEMY OF SCIENCES, BEIJING TEN DIMENSIONS TECHNOLOGY CO., LTD.
Inventors:
Zhen Shen, Gang Xiong, Yuqing Li, Hang Gao, Yi Xie, Meihua Zhao, Chao Guo, Xiuqin Shang, Xisong Dong, Zhengpeng Wu, Li Wan, Feiyue Wang
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.
Type:
Application
Filed:
September 16, 2019
Publication date:
August 12, 2021
Applicants:
INSTITUTE OF AUTOMATION, CHINESE ACADEMY OF SCIENCES, BEIJING TEN DIMENSIONS TECHNOLOGY CO., LTD.
Inventors:
Zhen SHEN, Gang XIONG, Yuqing LI, Hang GAO, Yi XIE, Meihua ZHAO, Chao GUO, Xiuqin SHANG, Xisong DONG, Zhengpeng WU, Li WAN, Feiyue WANG