Chinese Academy of Sciences

Beijing Ten Dimensions Technology Co., Ltd.

Meihua Zhao

Yuqing Li

Chao Guo

Zhen Shen

Yi Xie

Li Wan

Feiyue Wang

Gang Xiong

Hang Gao

Xiuqin Shang

Xisong Dong

Zhengpeng Wu

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.

Neural network-based error compensation method, system and device for 3D printing

Growing community of inventors

Beijing, China

Meihua Zhao