Defect detection in selective laser melting technology by acoustic signals with deep belief networks | |
Ye, Dongsen1; Hong, Geok Soon2; Zhang, Yingjie2; Zhu, Kunpeng3; Fuh, Jerry Ying Hsi2 | |
刊名 | INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
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2018-05-01 | |
卷号 | 96期号:5-8页码:2791-2801 |
关键词 | Additive manufacturing Deep belief networks Fast Fourier transform Defect detection |
ISSN号 | 0268-3768 |
DOI | 10.1007/s00170-018-1728-0 |
英文摘要 | Selective laser melting (SLM) is one of the most important and successfully additive manufacturing processes in 3D metal printing technologies. Critical quality issues such as porosity, surface roughness, crack, and delamination continue to present challenges within SLM-manufactured parts. Monitoring and in-process defect diagnosis are the key to improving the final part quality. Currently, it greatly hinders the adaptability and the development within the defect detection system since the setup restricts the vision and photo diode applications in the SLM process monitoring. Additionally, defect detection with traditional classification approaches makes the system rather complex due to introducing a series of steps. To meet these needs, this study proposes a novel method for the defect detection within the SLM parts. The setup was flexibly conducted using a microphone, and the defect detection was obtained by the framework of deep belief network (DBN). It is implemented by a simplified classification structure without signal preprocessing and feature extraction. The experimental results showed that the utilization of acoustic signals was workable for quality monitoring, and the DBN approach could reach high defect detection rate among five melted states without signal preprocessing. |
WOS关键词 | BEHAVIOR ; MACHINE ; POWDER ; SYSTEM |
WOS研究方向 | Automation & Control Systems ; Engineering |
语种 | 英语 |
出版者 | SPRINGER LONDON LTD |
WOS记录号 | WOS:000430539100103 |
内容类型 | 期刊论文 |
源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/35349] ![]() |
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 | Fuh, Jerry Ying Hsi |
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,Hong, Geok Soon,Zhang, Yingjie,et al. Defect detection in selective laser melting technology by acoustic signals with deep belief networks[J]. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY,2018,96(5-8):2791-2801. |
APA | Ye, Dongsen,Hong, Geok Soon,Zhang, Yingjie,Zhu, Kunpeng,&Fuh, Jerry Ying Hsi.(2018).Defect detection in selective laser melting technology by acoustic signals with deep belief networks.INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY,96(5-8),2791-2801. |
MLA | Ye, Dongsen,et al."Defect detection in selective laser melting technology by acoustic signals with deep belief networks".INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 96.5-8(2018):2791-2801. |
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