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X-ray computed tomography for predictive quality assessment, 3D visualisation of micro-injection mouldings and soft-tool deformation
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Gülçür, Mert, Wilson, Paul, Donnelly, Michael, Couling, Kevin, Goodship, Vannessa, Charmet, Jérôme, Williams, Mark A. and Gibbons, Gregory John (2023) X-ray computed tomography for predictive quality assessment, 3D visualisation of micro-injection mouldings and soft-tool deformation. Materials & Design, 227 . 111741. doi:10.1016/j.matdes.2023.111741 ISSN 0264-1275.
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Official URL: http://doi.org/10.1016/j.matdes.2023.111741
Abstract
This work presents X-ray computed tomography (XCT) as a dimensional quality assurance technique for micro-injection moulded polymeric test objects for the establishment of predictive quality models and quantifying soft-tool deformation. The results are compared against an industry standard laser-scanning-confocal microscope (LSCM) for the evaluation of XCT’s capability. The work demonstrates; (i) the exploitation of a XCT equipment for dimensional characterisation of micro-injection moulded products made out of polymers with adequate acquisition times, (ii) that acquired XCT data from the 3D visualisation of the micromouldings perform on par with a laser-scanning-confocal microscope in a quality prediction model, (iii) that the deformation occurring in an additively manufactured soft-tool can be quantified using XCT. The technique was particularly superior in volumetric data acquisition compared to LSCM in the filling prediction of the micromouldings. Better accuracy and repeatability in predicting the quality of the mouldings up to 92% achieved with XCT, in conjunction with an in-line collected soft-tool surface temperature data as an indirect quality assurance method. Given the capability of the XCT for the 3D data acquisition of polymeric miniature components, the approach described here has great potential in high-value micro-manufacturing process quality modelling for in-line quality assessment of miniature and added value products in data-rich contexts.
Item Type: | Journal Article | |||||||||
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Subjects: | R Medicine > RC Internal medicine T Technology > TJ Mechanical engineering and machinery T Technology > TS Manufactures |
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Divisions: | Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) |
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Library of Congress Subject Headings (LCSH): | Three-dimensional imaging, Additive manufacturing, Tomography, Image reconstruction -- Mathematical models, Nanotechnology -- Research, Nanomanufacturing | |||||||||
Journal or Publication Title: | Materials & Design | |||||||||
Publisher: | Elsevier Ltd | |||||||||
ISSN: | 0264-1275 | |||||||||
Official Date: | March 2023 | |||||||||
Dates: |
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Volume: | 227 | |||||||||
Article Number: | 111741 | |||||||||
DOI: | 10.1016/j.matdes.2023.111741 | |||||||||
Status: | Peer Reviewed | |||||||||
Publication Status: | Published | |||||||||
Date of first compliant deposit: | 14 March 2023 | |||||||||
Date of first compliant Open Access: | 14 March 2023 | |||||||||
RIOXX Funder/Project Grant: |
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