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

doi:10.1109/TCYB.2019.2898553

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 平行智能技术与系统团队

	A Learning-Based Framework for Error Compensation in 3D Printing
	Shen, Zhen1,2 ; Shang, Xiuqin1 ; Zhao, Meihua 1,3; Dong, Xisong1 ; Xiong, Gang1,4 ; Wang, Fei-Yue1
发表期刊	IEEE TRANSACTIONS ON CYBERNETICS
ISSN	2168-2267
	2019-11-01
卷号	49 期号:11 页码:4042-4050
通讯作者	Xiong, Gang(gang.xiong@ia.ac.cn)
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.
关键词	3D printing additive manufacturing cyber-physical system (CPS) deep learning error compensation
DOI	10.1109/TCYB.2019.2898553
关键词[WOS]	SYSTEMS
收录类别	SCI
语种	英语
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 ; 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
项目资助者	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研究方向	Automation & Control Systems ; Computer Science
WOS类目	Automation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS记录号	WOS:000476811000018
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
七大方向——子方向分类	人工智能+制造
引用统计	被引频次：37[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/27813
专题	多模态人工智能系统全国重点实验室_平行智能技术与系统团队
通讯作者	Xiong, Gang
作者单位	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
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.