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

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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 languageEnglish
Title of host publicationProceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 2
EditorsAndrew D. Ball, Huajiang Ouyang, Jyoti K. Sinha, Zuolu Wang
PublisherSpringer, Cham
Number of pages10
ISBN (Electronic)9783031494215
ISBN (Print)9783031494208, 9783031494239
Publication statusPublished - 29 May 2024
EventThe UNIfied Conference of DAMAS, InCoME and TEPEN Conferences - Huddersfield, United Kingdom, Huddersfield, United Kingdom
Duration: 29 Aug 20231 Sep 2023

Publication series

NameMechanisms and Machine Science
Volume152 MMS
ISSN (Print)2211-0984
ISSN (Electronic)2211-0992


ConferenceThe UNIfied Conference of DAMAS, InCoME and TEPEN Conferences
Abbreviated titleUNIfied 2023
Country/TerritoryUnited Kingdom
Internet address

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