A Learning-Based Framework for Error Compensation in 3-D Printing
Shen Z(沈震)1,2; Shang XQ(商秀芹)1; Zhao MH(赵美华)1; Xiong G(熊刚)1,3; Wang FY(王飞跃)1
Source PublicationIEEE Transactions on Cybernetics
2019-11
Volume49Issue:11Pages:4042-4050
Abstract
As a typical cyber-physical system, 3D printing has developed very fast in recent years. There is a strong demand for mass customization, such as printing dental crowns. However, the accuracy of the 3D printed objects is low compared with traditional methods. The main reason is that the model to be printed is arbitrary and usually the quantity is small. The deformation is affected by the shape of the object and there is a lack of a universal method for the error compensation. It is neither easy nor economical to perform the compensation manually. In this paper, we present a framework for the automatic error compensation. We obtain the shape by technologies such as 3D scanning. And we use the “3D deep learning” method to train a deep neural network. For a specifific task, such as dental crown printing, the network can learn the function of deformation when a large amount of data is used for training. To the best of our knowledge, this is the fifirst application of the deep neural network to the error compensation in 3D printing. And we propose the “inverse function network” to compensate for the error. We use four types of deformations of the dental crowns to verify the performance of the neural network: 1) translation; 2) scaling up; 3) scaling down; and 4) rotation. The convolutional AutoEncoder structure is employed for the end-to-end learning. The experiments show that the network can predict and compensate for the error well. By introducing the new method, we can improve the accuracy with little need for increasing the hardware cost.

 

Keyword3d Printing Additive Manufacturing Cyber Physical System (Cps) Deep Learning Error Compensation
Indexed BySCI
Funding ProjectNational Natural Science Foundation of China[61773382] ; National Natural Science Foundation of China[61773381] ; National Natural Science Foundation of China[61533019] ; China Guangdong's ST Project[2017B090912001]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/25815
Collection复杂系统管理与控制国家重点实验室_先进控制与自动化
Corresponding AuthorXiong G(熊刚)
Affiliation1.中国科学院自动化研究所
2.Qingdao Academy of Intelligent Industries
3.the Cloud Computing Center, Chinese Academy of Sciences
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Shen Z,Shang XQ,Zhao MH,et al. A Learning-Based Framework for Error Compensation in 3-D Printing[J]. IEEE Transactions on Cybernetics,2019,49(11):4042-4050.
APA Shen Z,Shang XQ,Zhao MH,Xiong G,&Wang FY.(2019).A Learning-Based Framework for Error Compensation in 3-D Printing.IEEE Transactions on Cybernetics,49(11),4042-4050.
MLA Shen Z,et al."A Learning-Based Framework for Error Compensation in 3-D Printing".IEEE Transactions on Cybernetics 49.11(2019):4042-4050.
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