Research on Online Monitoring Technology of 3D Printing Faults in Fused Deposition Molding

Chun Li; Lianghua Zeng; Yubin Lin; Fengshou Gu

doi:10.1007/978-3-030-75793-9_10

Research on Online Monitoring Technology of 3D Printing Faults in Fused Deposition Molding

Chun Li, Lianghua Zeng, Yubin Lin, Fengshou Gu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

During the procedure of 3D printing in Fused Deposition Molding (FDM), building faults, such as Congealed-Material (CM), empty printing, substrate deformation, warped edges and fused wire drawing, often occur. However, most FDM machines lack online monitor systems which lead to huge waste of materials and time. This paper combines an acceleration sensor with vision sensors to monitor CM faults during 3D printing in FDM. An acceleration sensor is used to collect signals during a printing procedure and then abnormal signals are found when nozzle collides with CM. Subsequently, CM can be detected through the STFT algorithm. Furthermore, acceleration data can provide fault characteristics for on-line monitoring. Finally, a camera is used to obtain 3D print model pictures and then an image processing algorithm is studied. An image window is used to reduce calculation amount and extract useful model features. Then, a connected region area is calculated and the largest area region is selected after a threshold and a region are connected. Through image processing, online CM detection can be realized, which provides a foundation for further positioning and removal of CM.

Original language	English
Title of host publication	Proceedings of IncoME-V & CEPE Net-2020
Subtitle of host publication	Condition Monitoring, Plant Maintenance and Reliability
Editors	Dong Zhen, Dong Wang, Tianyang Wang, Hongjun Wang, Baoshan Huang, Jyoti K. Sinha, Andrew David Ball
Place of Publication	Cham
Publisher	Springer Nature Switzerland AG
Pages	91-99
Number of pages	9
Volume	105
Edition	1st
ISBN (Electronic)	9783030757939
ISBN (Print)	9783030757922
DOIs	https://doi.org/10.1007/978-3-030-75793-9_10
Publication status	Published - 16 May 2021
Event	5th International Conference on Maintenance Engineering and the 2020 Annual Conference of the Centre for Efficiency and Performance Engineering Network - Zhuhai, China Duration: 23 Oct 2020 → 25 Oct 2020 Conference number: 5 https://link.springer.com/book/10.1007/978-3-030-75793-9#about

Publication series

Name	Mechanisms and Machine Science
Volume	105
ISSN (Print)	2211-0984
ISSN (Electronic)	2211-0992

Conference

Conference	5th International Conference on Maintenance Engineering and the 2020 Annual Conference of the Centre for Efficiency and Performance Engineering Network
Abbreviated title	IncoME-V and CEPE Net-2020
Country/Territory	China
City	Zhuhai
Period	23/10/20 → 25/10/20
Internet address	https://link.springer.com/book/10.1007/978-3-030-75793-9#about

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Li, C., Zeng, L., Lin, Y., & Gu, F. (2021). Research on Online Monitoring Technology of 3D Printing Faults in Fused Deposition Molding. In D. Zhen, D. Wang, T. Wang, H. Wang, B. Huang, J. K. Sinha, & A. D. Ball (Eds.), Proceedings of IncoME-V & CEPE Net-2020: Condition Monitoring, Plant Maintenance and Reliability (1st ed., Vol. 105, pp. 91-99). (Mechanisms and Machine Science; Vol. 105). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-030-75793-9_10

Li, Chun ; Zeng, Lianghua ; Lin, Yubin et al. / Research on Online Monitoring Technology of 3D Printing Faults in Fused Deposition Molding. Proceedings of IncoME-V & CEPE Net-2020: Condition Monitoring, Plant Maintenance and Reliability. editor / Dong Zhen ; Dong Wang ; Tianyang Wang ; Hongjun Wang ; Baoshan Huang ; Jyoti K. Sinha ; Andrew David Ball. Vol. 105 1st. ed. Cham : Springer Nature Switzerland AG, 2021. pp. 91-99 (Mechanisms and Machine Science).

@inproceedings{73e85d16232641cdb9b4f8504bf5b57b,

title = "Research on Online Monitoring Technology of 3D Printing Faults in Fused Deposition Molding",

abstract = "During the procedure of 3D printing in Fused Deposition Molding (FDM), building faults, such as Congealed-Material (CM), empty printing, substrate deformation, warped edges and fused wire drawing, often occur. However, most FDM machines lack online monitor systems which lead to huge waste of materials and time. This paper combines an acceleration sensor with vision sensors to monitor CM faults during 3D printing in FDM. An acceleration sensor is used to collect signals during a printing procedure and then abnormal signals are found when nozzle collides with CM. Subsequently, CM can be detected through the STFT algorithm. Furthermore, acceleration data can provide fault characteristics for on-line monitoring. Finally, a camera is used to obtain 3D print model pictures and then an image processing algorithm is studied. An image window is used to reduce calculation amount and extract useful model features. Then, a connected region area is calculated and the largest area region is selected after a threshold and a region are connected. Through image processing, online CM detection can be realized, which provides a foundation for further positioning and removal of CM.",

keywords = "Fault monitor, FDM 3D printing, Image processing, Signal detection",

author = "Chun Li and Lianghua Zeng and Yubin Lin and Fengshou Gu",

note = "Funding Information: Acknowledgement. This work is partially supported by Guangdong Ordinary Universities Youth Innovative Talents Project (3D Printing Quality Real-time Detection and Quality Prediction Key Technology Research:2018KQNCX343); Zhuhai Mechanical Engineering Dominant Discipline. Publisher Copyright: {\textcopyright} 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 5th International Conference on Maintenance Engineering and the 2020 Annual Conference of the Centre for Efficiency and Performance Engineering Network, IncoME-V and CEPE Net-2020 ; Conference date: 23-10-2020 Through 25-10-2020",

year = "2021",

month = may,

day = "16",

doi = "10.1007/978-3-030-75793-9_10",

language = "English",

isbn = "9783030757922",

volume = "105",

series = "Mechanisms and Machine Science",

publisher = "Springer Nature Switzerland AG",

pages = "91--99",

editor = "Dong Zhen and Dong Wang and Tianyang Wang and Hongjun Wang and Baoshan Huang and Sinha, {Jyoti K.} and Ball, {Andrew David}",

booktitle = "Proceedings of IncoME-V & CEPE Net-2020",

address = "Switzerland",

edition = "1st",

url = "https://link.springer.com/book/10.1007/978-3-030-75793-9#about",

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Li, C, Zeng, L, Lin, Y & Gu, F 2021, Research on Online Monitoring Technology of 3D Printing Faults in Fused Deposition Molding. in D Zhen, D Wang, T Wang, H Wang, B Huang, JK Sinha & AD Ball (eds), Proceedings of IncoME-V & CEPE Net-2020: Condition Monitoring, Plant Maintenance and Reliability. 1st edn, vol. 105, Mechanisms and Machine Science, vol. 105, Springer Nature Switzerland AG, Cham, pp. 91-99, 5th International Conference on Maintenance Engineering and the 2020 Annual Conference of the Centre for Efficiency and Performance Engineering Network, Zhuhai, China, 23/10/20. https://doi.org/10.1007/978-3-030-75793-9_10

Research on Online Monitoring Technology of 3D Printing Faults in Fused Deposition Molding. / Li, Chun; Zeng, Lianghua; Lin, Yubin et al.

Proceedings of IncoME-V & CEPE Net-2020: Condition Monitoring, Plant Maintenance and Reliability. ed. / Dong Zhen; Dong Wang; Tianyang Wang; Hongjun Wang; Baoshan Huang; Jyoti K. Sinha; Andrew David Ball. Vol. 105 1st. ed. Cham : Springer Nature Switzerland AG, 2021. p. 91-99 (Mechanisms and Machine Science; Vol. 105).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Research on Online Monitoring Technology of 3D Printing Faults in Fused Deposition Molding

AU - Li, Chun

AU - Zeng, Lianghua

AU - Lin, Yubin

AU - Gu, Fengshou

N1 - Conference code: 5

PY - 2021/5/16

Y1 - 2021/5/16

N2 - During the procedure of 3D printing in Fused Deposition Molding (FDM), building faults, such as Congealed-Material (CM), empty printing, substrate deformation, warped edges and fused wire drawing, often occur. However, most FDM machines lack online monitor systems which lead to huge waste of materials and time. This paper combines an acceleration sensor with vision sensors to monitor CM faults during 3D printing in FDM. An acceleration sensor is used to collect signals during a printing procedure and then abnormal signals are found when nozzle collides with CM. Subsequently, CM can be detected through the STFT algorithm. Furthermore, acceleration data can provide fault characteristics for on-line monitoring. Finally, a camera is used to obtain 3D print model pictures and then an image processing algorithm is studied. An image window is used to reduce calculation amount and extract useful model features. Then, a connected region area is calculated and the largest area region is selected after a threshold and a region are connected. Through image processing, online CM detection can be realized, which provides a foundation for further positioning and removal of CM.

AB - During the procedure of 3D printing in Fused Deposition Molding (FDM), building faults, such as Congealed-Material (CM), empty printing, substrate deformation, warped edges and fused wire drawing, often occur. However, most FDM machines lack online monitor systems which lead to huge waste of materials and time. This paper combines an acceleration sensor with vision sensors to monitor CM faults during 3D printing in FDM. An acceleration sensor is used to collect signals during a printing procedure and then abnormal signals are found when nozzle collides with CM. Subsequently, CM can be detected through the STFT algorithm. Furthermore, acceleration data can provide fault characteristics for on-line monitoring. Finally, a camera is used to obtain 3D print model pictures and then an image processing algorithm is studied. An image window is used to reduce calculation amount and extract useful model features. Then, a connected region area is calculated and the largest area region is selected after a threshold and a region are connected. Through image processing, online CM detection can be realized, which provides a foundation for further positioning and removal of CM.

KW - Fault monitor

KW - FDM 3D printing

KW - Image processing

KW - Signal detection

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T3 - Mechanisms and Machine Science

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EP - 99

BT - Proceedings of IncoME-V & CEPE Net-2020

A2 - Zhen, Dong

A2 - Wang, Dong

A2 - Wang, Tianyang

A2 - Wang, Hongjun

A2 - Huang, Baoshan

A2 - Sinha, Jyoti K.

A2 - Ball, Andrew David

PB - Springer Nature Switzerland AG

CY - Cham

T2 - 5th International Conference on Maintenance Engineering and the 2020 Annual Conference of the Centre for Efficiency and Performance Engineering Network

Y2 - 23 October 2020 through 25 October 2020

ER -

Li C, Zeng L, Lin Y, Gu F. Research on Online Monitoring Technology of 3D Printing Faults in Fused Deposition Molding. In Zhen D, Wang D, Wang T, Wang H, Huang B, Sinha JK, Ball AD, editors, Proceedings of IncoME-V & CEPE Net-2020: Condition Monitoring, Plant Maintenance and Reliability. 1st ed. Vol. 105. Cham: Springer Nature Switzerland AG. 2021. p. 91-99. (Mechanisms and Machine Science). Epub 2021 May 16. doi: 10.1007/978-3-030-75793-9_10

Research on Online Monitoring Technology of 3D Printing Faults in Fused Deposition Molding

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