A Learning-Based Framework for Error Compensation in 3D Printing
文献类型:期刊论文
作者 | Shen, Zhen1,2; Shang, Xiuqin2; Zhao, Meihua2,3; Dong, Xisong2; Xiong, Gang2,4; Wang, Fei-Yue2 |
刊名 | IEEE TRANSACTIONS ON CYBERNETICS |
出版日期 | 2019-11-01 |
卷号 | 49期号:11页码:4042-4050 |
ISSN号 | 2168-2267 |
关键词 | 3D printing additive manufacturing cyber-physical system (CPS) deep learning error compensation |
DOI | 10.1109/TCYB.2019.2898553 |
通讯作者 | Xiong, Gang(gang.xiong@ia.ac.cn) |
英文摘要 | 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. |
WOS关键词 | SYSTEMS |
资助项目 | 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 |
WOS研究方向 | Automation & Control Systems ; Computer Science |
语种 | 英语 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
WOS记录号 | WOS:000476811000018 |
资助机构 | 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 |
源URL | [http://ir.ia.ac.cn/handle/173211/27812] |
专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队 |
通讯作者 | Xiong, Gang |
作者单位 | 1.Qingdao Acad Intelligent Ind, Intelligent Mfg Ctr, Qingdao 266109, Shandong, Peoples R China 2.Chinese Acad Sci, Beijing Engn Res Ctr Intelligent Syst & Technol, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, 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 |
推荐引用方式 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. |
入库方式: OAI收割
来源:自动化研究所
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