In situ monitoring of selective laser melting using plume and spatter signatures by deep belief networks

doi:10.1016/j.isatra.2018.07.021

CORC > 合肥物质科学研究院 > 中国科学院合肥物质科学研究院 > 中科院合肥物质科学研究院先进制造技术研究所

	In situ monitoring of selective laser melting using plume and spatter signatures by deep belief networks
	Ye, Dongsen 1,2,3; Fuh, Jerry Ying Hsi 2; Zhang, Yingjie 2; Hong, Geok Soon 2; Zhu, Kunpeng 3
刊名	ISA TRANSACTIONS
	2018-10-01
卷号	81 页码:96-104
关键词	Deep belief network Selective laser melting Melted state recognition Plume and patter Process monitoring
ISSN号	0019-0578
DOI	10.1016/j.isatra.2018.07.021
通讯作者	Zhu, Kunpeng(zhukp@iamt.ac.cn)
英文摘要	Critical quality issues such as high porosity, cracks, and delamination are common in current selective laser melting (SLM) manufactured components. This study provides a flexible and integrated method for in situ process monitoring and melted state recognition during the SLM process, and it is useful for process optimization to decrease part quality issues. The part qualities are captured by images obtained from an off-axis setup with a near-infrared (NIR) camera. Plume and spatter signatures are closely related to the melted states and laser energy density, and they are employed for the SLM process monitoring in an adapted deep belief network (DBN) framework. The melted state recognition with the improved DBN and original NIR images requires little signal preprocessing, less parameter selection and feature extraction, obtaining the classification rate 83.40% for five melted states. Compared to the other methods of neural network (NN) and convolutional neural networks (CNN), the proposed DBN approach is identified to be accurate, convenient, and suitable for the SLM process monitoring and part quality recognition.
资助项目	China Scholarship Council ; Chinese Academy of Sciences 100 Talents Program ; National University of Singapore ; National Additive Manufacturing Innovation Cluster, Singapore
WOS关键词	POWDER-BED FUSION ; STAINLESS-STEEL ; FAULT-DIAGNOSIS ; NEURAL-NETWORK ; SPECTROSCOPY ; BEHAVIOR
WOS研究方向	Automation & Control Systems ; Engineering ; Instruments & Instrumentation
语种	英语
出版者	ELSEVIER SCIENCE INC
WOS记录号	WOS:000449897700010
资助机构	China Scholarship Council ; China Scholarship Council ; China Scholarship Council ; China Scholarship Council ; China Scholarship Council ; China Scholarship Council ; China Scholarship Council ; China Scholarship Council ; China Scholarship Council ; China Scholarship Council ; China Scholarship Council ; China Scholarship Council ; China Scholarship Council ; China Scholarship Council ; China Scholarship Council ; China Scholarship Council ; Chinese Academy of Sciences 100 Talents Program ; Chinese Academy of Sciences 100 Talents Program ; Chinese Academy of Sciences 100 Talents Program ; Chinese Academy of Sciences 100 Talents Program ; Chinese Academy of Sciences 100 Talents Program ; Chinese Academy of Sciences 100 Talents Program ; Chinese Academy of Sciences 100 Talents Program ; Chinese Academy of Sciences 100 Talents Program ; Chinese Academy of Sciences 100 Talents Program ; Chinese Academy of Sciences 100 Talents Program ; Chinese Academy of Sciences 100 Talents Program ; 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内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/34157]
专题	合肥物质科学研究院_中科院合肥物质科学研究院先进制造技术研究所
通讯作者	Zhu, Kunpeng
作者单位	1.Univ Sci & Technol China, Dept Automat, Hefei 230026, Anhui, Peoples R China 2.Natl Univ Singapore, Dept Mech Engn, Singapore 117575, Singapore 3.Chinese Acad Sci, Inst Adv Mfg Technol, Changzhou 213164, Peoples R China
推荐引用方式 GB/T 7714	Ye, Dongsen,Fuh, Jerry Ying Hsi,Zhang, Yingjie,et al. In situ monitoring of selective laser melting using plume and spatter signatures by deep belief networks[J]. ISA TRANSACTIONS,2018,81:96-104.
APA	Ye, Dongsen,Fuh, Jerry Ying Hsi,Zhang, Yingjie,Hong, Geok Soon,&Zhu, Kunpeng.(2018).In situ monitoring of selective laser melting using plume and spatter signatures by deep belief networks.ISA TRANSACTIONS,81,96-104.
MLA	Ye, Dongsen,et al."In situ monitoring of selective laser melting using plume and spatter signatures by deep belief networks".ISA TRANSACTIONS 81(2018):96-104.