A Learning-Based Framework for Error Compensation in 3D Printing

doi:10.1109/TCYB.2019.2898553

	A Learning-Based Framework for Error Compensation in 3D Printing
	Shen, Zhen 1,2; Shang, Xiuqin 2; Zhao, Meihua 2,3; Dong, Xisong 2; Xiong, Gang2,4 ; Wang, Fei-Yue 2
刊名	IEEE TRANSACTIONS ON CYBERNETICS
	2019-11-01
卷号	49 期号:11 页码:4042-4050
关键词	3D printing additive manufacturing cyber-physical system (CPS) deep learning error compensation
ISSN号	2168-2267
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.
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关键词	SYSTEMS
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
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/27812]
专题	中国科学院自动化研究所
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.