| A Learning-Based Framework for Error Compensation in 3D Printing | |
Shen, Zhen1,2 ; Shang, Xiuqin1; Zhao, Meihua1,3; Dong, Xisong1; Xiong, Gang1,4; Wang, Fei-Yue1
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| Source Publication | IEEE TRANSACTIONS ON CYBERNETICS
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| ISSN | 2168-2267 |
| 2019-11-01 | |
| Volume | 49Issue:11Pages:4042-4050 |
| Corresponding Author | Xiong, Gang(gang.xiong@ia.ac.cn) |
| 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 specific 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 first 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. |
| Keyword | 3D printing additive manufacturing cyber-physical system (CPS) deep learning error compensation |
| DOI | 10.1109/TCYB.2019.2898553 |
| WOS Keyword | SYSTEMS |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Natural Science Foundation of China[61773382] ; National Natural Science Foundation of China[61773381] ; National Natural Science Foundation of China[61872365] ; National Natural Science Foundation of China[61533019] ; National Natural Science Foundation of China[61702519] ; Chinese Guangdong's ST Project[2017B090912001] ; Chinese Guangdong's ST Project[2016B090910001] ; Beijing Natural Science Foundation[4182065] ; Chinese Hunan's ST Project[20181040] ; Beijing Ten Dimensions Technology Company Ltd. ; Institute of Automation, Chinese Academy of Sciences ; Dongguan's Innovation Talents Project ; National Natural Science Foundation of China[61773382] ; National Natural Science Foundation of China[61773381] ; National Natural Science Foundation of China[61872365] ; National Natural Science Foundation of China[61533019] ; National Natural Science Foundation of China[61702519] ; Chinese Guangdong's ST Project[2017B090912001] ; Chinese Guangdong's ST Project[2016B090910001] ; Beijing Natural Science Foundation[4182065] ; Chinese Hunan's ST Project[20181040] ; Beijing Ten Dimensions Technology Company Ltd. ; Institute of Automation, Chinese Academy of Sciences ; Dongguan's Innovation Talents Project |
| Funding Organization | National Natural Science Foundation of China ; Chinese Guangdong's ST Project ; Beijing Natural Science Foundation ; Chinese Hunan's ST Project ; Beijing Ten Dimensions Technology Company Ltd. ; Institute of Automation, Chinese Academy of Sciences ; Dongguan's Innovation Talents Project |
| WOS Research Area | Automation & Control Systems ; Computer Science |
| WOS Subject | Automation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics |
| WOS ID | WOS:000476811000018 |
| Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| Sub direction classification | 人工智能+制造 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/27813 |
| 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, Gang |
| Affiliation | 1.Chinese Acad Sci, Beijing Engn Res Ctr Intelligent Syst & Technol, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Qingdao Acad Intelligent Ind, Intelligent Mfg Ctr, Qingdao 266109, Shandong, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China 4.Chinese Acad Sci, Cloud Comp Ctr, Dongguan 523808, Peoples R China |
| Recommended Citation GB/T 7714 | Shen, Zhen,Shang, Xiuqin,Zhao, Meihua,et al. A Learning-Based Framework for Error Compensation in 3D Printing[J]. IEEE TRANSACTIONS ON CYBERNETICS,2019,49(11):4042-4050. |
| APA | Shen, Zhen,Shang, Xiuqin,Zhao, Meihua,Dong, Xisong,Xiong, Gang,&Wang, Fei-Yue.(2019).A Learning-Based Framework for Error Compensation in 3D Printing.IEEE TRANSACTIONS ON CYBERNETICS,49(11),4042-4050. |
| MLA | Shen, Zhen,et al."A Learning-Based Framework for Error Compensation in 3D Printing".IEEE TRANSACTIONS ON CYBERNETICS 49.11(2019):4042-4050. |
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