Knowledge-based bidirectional thermal variable modelling for directed energy deposition additive manufacturing

Simple item page
dc.contributor.authorQin, Jian
dc.contributor.authorTaraphdar, Pradeeptta
dc.contributor.authorSun, Yongle
dc.contributor.authorWainwright, James
dc.contributor.authorLai, Wai Jun
dc.contributor.authorFeng, Shuo
dc.contributor.authorDing, Jialuo
dc.contributor.authorWilliams, Stewart
dc.date.accessioned2024-09-19T09:15:27Z
dc.date.available2024-09-19T09:15:27Z
dc.date.freetoread2024-09-19
dc.date.issued2024-09-05
dc.date.pubOnline2024-09-05
dc.description.abstractDirected energy deposition additive manufacturing (DED-AM) has gained significant interest in producing large-scale metallic structural components. In this paper, a knowledge-based machine learning (ML) approach, combining both physics-based simulation and data-driven modelling, is proposed for a study on thermal variables of DED-AM. This approach enables both forward and backward predictions, which breaks down the barriers between the basic process parameters and key process attributes. Process knowledge plays a critical role to enable the prediction and enhance the accuracy in both prediction directions. The proposed ML approach successfully predicted the thermal variables of wire arc based DED-AM for forward modelling and the process parameters for backward modelling, typically within 7% errors. This approach can be further generalised as a powerful modelling tool for design, control, and evaluation of DED-AM processes regarding build geometry and properties, as well as an essential constituent element in a digital twin of a DED-AM system.
dc.description.journalNameVirtual and Physical Prototyping
dc.description.sponsorshipEngineering and Physical Sciences Research Council
dc.description.sponsorshipThe authors would like to express their gratitude to Engineering and Physical Sciences Research Council (EPSRC) (EP/ R027218/1, New Wire Additive Manufacturing) for supporting aspects of this research.
dc.identifier.citationQin J, Taraphdar P, Sun Y, et al., (2024) Knowledge-based bidirectional thermal variable modelling for directed energy deposition additive manufacturing. Virtual and Physical Prototyping, Volume 19, September 2024, Article number e2397008-.
dc.identifier.eissn1745-2767
dc.identifier.elementsID552920
dc.identifier.issn1745-2759
dc.identifier.issueNo1
dc.identifier.urihttps://doi.org/10.1080/17452759.2024.2397008
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/22936
dc.identifier.volumeNo19
dc.languageEnglish
dc.language.isoen
dc.publisherInforma UK Limited
dc.publisher.urihttps://www.tandfonline.com/doi/full/10.1080/17452759.2024.2397008
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject4014 Manufacturing Engineering
dc.subject40 Engineering
dc.subjectMachine Learning and Artificial Intelligence
dc.subject4016 Materials engineering
dc.subject4017 Mechanical engineering
dc.titleKnowledge-based bidirectional thermal variable modelling for directed energy deposition additive manufacturing
dc.typeArticle
dcterms.dateAccepted2024-08-21

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Knowledge-based_bidirectional_thermal_variable_modelling-2024.pdf
Size:
1.94 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Plain Text
Description:
Download

Collections

Staff publications (SATM)