| 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
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| Source Publication | IEEE Transactions on Cybernetics
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| 2019-11 | |
| Volume | 49Issue: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.
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| Keyword | 3d Printing Additive Manufacturing Cyber Physical System (Cps) Deep Learning Error Compensation |
| Indexed By | SCI |
| Funding Project | China Guangdong's ST Project[2017B090912001] ; National Natural Science Foundation of China[61533019] ; National Natural Science Foundation of China[61773381] ; National Natural Science Foundation of China[61773382] ; National 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 |
| Identifier | http://ir.ia.ac.cn/handle/173211/25815 |
| Collection | 复杂系统管理与控制国家重点实验室_平行智能技术与系统团队 |
| Corresponding Author | Xiong G(熊刚) |
| Affiliation | 1.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 Affilication | Institute of Automation, Chinese Academy of Sciences |
| Corresponding Author Affilication | Institute 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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| 正式出版-沈老师-A Learning-（3664KB） | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | View Download | |
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