A Digital Twin Framework of In-Line Process Optimisation for Material Extrusion-Based Additive Manufacturing

Kexin Yin; Shan Lou; Yuchu Qin; Yongjia Xu; Paul Scott; Jane Jiang

doi:10.1007/978-3-031-49421-5_63

A Digital Twin Framework of In-Line Process Optimisation for Material Extrusion-Based Additive Manufacturing

Kexin Yin, Shan Lou, Yuchu Qin, Yongjia Xu, Paul Scott, Jane Jiang

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

Abstract

In this paper, a two-layered digital twin framework for in-situ parameter optimisation in material extrusion-based additive manufacturing is presented. The digital twin framework leverages several advanced data-driven technologies, such as surrogate modelling and machine learning, to enhance process efficiency and product quality. A case study was conducted using a fused deposition modelling printer, where a multi-source sensor net, comprising a 2D camera, a 2.5D laser scanner, and a 3D surface reconstruction system, provided valuable data insights. The results showcased successful implementation and highlighted the potential of the digital twin model in additive manufacturing. The efficiency trade-off of the multi-source sensor net was discussed, proving that applying the framework will not affect the fast-printing system. Future work will focus on closed-loop control and generalised modelling for further optimising the digital twin framework and enhancing printing product quality.

Original language	English
Title of host publication	Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 2
Editors	Andrew D. Ball, Huajiang Ouyang, Jyoti K. Sinha, Zuolu Wang
Publisher	Springer, Cham
Pages	777-786
Number of pages	10
Volume	152
ISBN (Electronic)	9783031494215
ISBN (Print)	9783031494208, 9783031494239
DOIs	https://doi.org/10.1007/978-3-031-49421-5_63
Publication status	Published - 29 May 2024
Event	The UNIfied Conference of DAMAS, InCoME and TEPEN Conferences - Huddersfield, United Kingdom, Huddersfield, United Kingdom Duration: 29 Aug 2023 → 1 Sep 2023 https://unified2023.org/

Publication series

Name	Mechanisms and Machine Science
Publisher	Springer
Volume	152 MMS
ISSN (Print)	2211-0984
ISSN (Electronic)	2211-0992

Conference

Conference	The UNIfied Conference of DAMAS, InCoME and TEPEN Conferences
Abbreviated title	UNIfied 2023
Country/Territory	United Kingdom
City	Huddersfield
Period	29/08/23 → 1/09/23
Internet address	https://unified2023.org/

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10.1007/978-3-031-49421-5_63Licence: Unspecified

Cite this

Yin, K., Lou, S., Qin, Y., Xu, Y., Scott, P., & Jiang, J. (2024). A Digital Twin Framework of In-Line Process Optimisation for Material Extrusion-Based Additive Manufacturing. In A. D. Ball, H. Ouyang, J. K. Sinha, & Z. Wang (Eds.), Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 2 (Vol. 152, pp. 777-786). (Mechanisms and Machine Science; Vol. 152 MMS). Springer, Cham. https://doi.org/10.1007/978-3-031-49421-5_63

Yin, Kexin ; Lou, Shan ; Qin, Yuchu et al. / A Digital Twin Framework of In-Line Process Optimisation for Material Extrusion-Based Additive Manufacturing. Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 2. editor / Andrew D. Ball ; Huajiang Ouyang ; Jyoti K. Sinha ; Zuolu Wang. Vol. 152 Springer, Cham, 2024. pp. 777-786 (Mechanisms and Machine Science).

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title = "A Digital Twin Framework of In-Line Process Optimisation for Material Extrusion-Based Additive Manufacturing",

abstract = "In this paper, a two-layered digital twin framework for in-situ parameter optimisation in material extrusion-based additive manufacturing is presented. The digital twin framework leverages several advanced data-driven technologies, such as surrogate modelling and machine learning, to enhance process efficiency and product quality. A case study was conducted using a fused deposition modelling printer, where a multi-source sensor net, comprising a 2D camera, a 2.5D laser scanner, and a 3D surface reconstruction system, provided valuable data insights. The results showcased successful implementation and highlighted the potential of the digital twin model in additive manufacturing. The efficiency trade-off of the multi-source sensor net was discussed, proving that applying the framework will not affect the fast-printing system. Future work will focus on closed-loop control and generalised modelling for further optimising the digital twin framework and enhancing printing product quality.",

keywords = "In-situ detection, Machine learning, Material extrusion-based additive manufacturing, Multi-objective optimisation",

author = "Kexin Yin and Shan Lou and Yuchu Qin and Yongjia Xu and Paul Scott and Jane Jiang",

note = "Funding Information: The authors gratefully acknowledge the support of EPSRC funding (EP/ P006930/1, EP/S000453/1) and the help from dear colleagues Darragh Broadbent, Farhan Farouq, and Weidong Liu. Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; The UNIfied Conference of DAMAS, InCoME and TEPEN Conferences, UNIfied 2023 ; Conference date: 29-08-2023 Through 01-09-2023",

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Yin, K, Lou, S , Qin, Y , Xu, Y , Scott, P & Jiang, J 2024, A Digital Twin Framework of In-Line Process Optimisation for Material Extrusion-Based Additive Manufacturing. in AD Ball, H Ouyang, JK Sinha & Z Wang (eds), Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 2. vol. 152, Mechanisms and Machine Science, vol. 152 MMS, Springer, Cham, pp. 777-786, The UNIfied Conference of DAMAS, InCoME and TEPEN Conferences, Huddersfield, United Kingdom, 29/08/23. https://doi.org/10.1007/978-3-031-49421-5_63

A Digital Twin Framework of In-Line Process Optimisation for Material Extrusion-Based Additive Manufacturing. / Yin, Kexin; Lou, Shan ; Qin, Yuchu et al.

Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 2. ed. / Andrew D. Ball; Huajiang Ouyang; Jyoti K. Sinha; Zuolu Wang. Vol. 152 Springer, Cham, 2024. p. 777-786 (Mechanisms and Machine Science; Vol. 152 MMS).

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

TY - GEN

T1 - A Digital Twin Framework of In-Line Process Optimisation for Material Extrusion-Based Additive Manufacturing

AU - Yin, Kexin

AU - Lou, Shan

AU - Qin, Yuchu

AU - Xu, Yongjia

AU - Scott, Paul

AU - Jiang, Jane

N1 - Funding Information: The authors gratefully acknowledge the support of EPSRC funding (EP/ P006930/1, EP/S000453/1) and the help from dear colleagues Darragh Broadbent, Farhan Farouq, and Weidong Liu. Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024/5/29

Y1 - 2024/5/29

N2 - In this paper, a two-layered digital twin framework for in-situ parameter optimisation in material extrusion-based additive manufacturing is presented. The digital twin framework leverages several advanced data-driven technologies, such as surrogate modelling and machine learning, to enhance process efficiency and product quality. A case study was conducted using a fused deposition modelling printer, where a multi-source sensor net, comprising a 2D camera, a 2.5D laser scanner, and a 3D surface reconstruction system, provided valuable data insights. The results showcased successful implementation and highlighted the potential of the digital twin model in additive manufacturing. The efficiency trade-off of the multi-source sensor net was discussed, proving that applying the framework will not affect the fast-printing system. Future work will focus on closed-loop control and generalised modelling for further optimising the digital twin framework and enhancing printing product quality.

AB - In this paper, a two-layered digital twin framework for in-situ parameter optimisation in material extrusion-based additive manufacturing is presented. The digital twin framework leverages several advanced data-driven technologies, such as surrogate modelling and machine learning, to enhance process efficiency and product quality. A case study was conducted using a fused deposition modelling printer, where a multi-source sensor net, comprising a 2D camera, a 2.5D laser scanner, and a 3D surface reconstruction system, provided valuable data insights. The results showcased successful implementation and highlighted the potential of the digital twin model in additive manufacturing. The efficiency trade-off of the multi-source sensor net was discussed, proving that applying the framework will not affect the fast-printing system. Future work will focus on closed-loop control and generalised modelling for further optimising the digital twin framework and enhancing printing product quality.

KW - In-situ detection

KW - Machine learning

KW - Material extrusion-based additive manufacturing

KW - Multi-objective optimisation

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DO - 10.1007/978-3-031-49421-5_63

M3 - Conference contribution

AN - SCOPUS:85195601236

SN - 9783031494208

SN - 9783031494239

VL - 152

T3 - Mechanisms and Machine Science

SP - 777

EP - 786

BT - Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 2

A2 - Ball, Andrew D.

A2 - Ouyang, Huajiang

A2 - Sinha, Jyoti K.

A2 - Wang, Zuolu

PB - Springer, Cham

T2 - The UNIfied Conference of DAMAS, InCoME and TEPEN Conferences

Y2 - 29 August 2023 through 1 September 2023

ER -

Yin K, Lou S , Qin Y , Xu Y , Scott P , Jiang J. A Digital Twin Framework of In-Line Process Optimisation for Material Extrusion-Based Additive Manufacturing. In Ball AD, Ouyang H, Sinha JK, Wang Z, editors, Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 2. Vol. 152. Springer, Cham. 2024. p. 777-786. (Mechanisms and Machine Science). Epub 2024 May 28. doi: 10.1007/978-3-031-49421-5_63

A Digital Twin Framework of In-Line Process Optimisation for Material Extrusion-Based Additive Manufacturing

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