Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/104258
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dc.contributorDepartment of Industrial and Systems Engineeringen_US
dc.creatorLu, XZen_US
dc.creatorChan, LCen_US
dc.date.accessioned2024-02-05T08:47:36Z-
dc.date.available2024-02-05T08:47:36Z-
dc.identifier.issn0924-0136en_US
dc.identifier.urihttp://hdl.handle.net/10397/104258-
dc.language.isoenen_US
dc.publisherElsevier BVen_US
dc.rights© 2018 Elsevier B.V. All rights reserved.en_US
dc.rights© 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.rightsThe following publication Lu, X. Z., & Chan, L. C. (2018). Micro-voids quantification for damage prediction in warm forging of biocompatible alloys using 3D X-ray CT and RVE approach. Journal of Materials Processing Technology, 258, 116–127 is available at https://doi.org/10.1016/j.jmatprotec.2018.03.020.en_US
dc.subjectBiocompatible alloysen_US
dc.subjectDamage predictionen_US
dc.subjectMicro-voidsen_US
dc.subjectRepresentative volume elementen_US
dc.subjectWarm forgingen_US
dc.subjectX-ray computed tomographyen_US
dc.titleMicro-voids quantification for damage prediction in warm forging of biocompatible alloys using 3D X-ray CT and RVE approachen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage116en_US
dc.identifier.epage127en_US
dc.identifier.volume258en_US
dc.identifier.doi10.1016/j.jmatprotec.2018.03.020en_US
dcterms.abstractThis study aims to quantify the three-dimensional (3D) micro-voids for damage prediction in warm forging through non-destructive X-ray computed tomography (CT) and RVE approach. Typical biocompatible alloys, i.e., stainless steel 316 L (SS316L) and titanium alloy Ti-6Al-4V, were used as specimen materials in warm-forging a medical implant, i.e., a basal thumb implant. X-ray CT scanning was performed for both the preforms and forged components. Volumetric CT images were then reconstructed and the 3D micro-void distribution and evolution inside the materials were detected and analysed quantitatively. Furthermore, three typical local strain regions, i.e., the small tensile strain region (STSR), small compressive strain region (SCSR) and large compressive strain region (LCSR), were established as the 3D representative volume element (RVE) models for both SS316L and Ti-6Al-4V preforms. The spatial location, size and volume of each micro-void were obtained from defect analysis of the 3D CT images and considered explicitly for subsequent damage prediction. An improved thermo-mechanical coupled micromechanics-based damage (micro-damage) model, which considered the variation of volume fraction of micro-voids (VFMVs), was implemented into finite element (FE) package ABAQUS for localized damage prediction of the RVE models. The damage distributions of the RVE models at different strain levels were visualized and identified. Finally, the localized damage evolutions at both compressive and tensile deformations were predicted and found to match quite well with the findings acquired from CT scanning. Thus, the application of non-destructive X-ray CT measurement of micro-voids, incorporating the RVE approach, was able to play a significant role leading to a more reliable damage prediction in the warm forging process.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationJournal of materials science & technology, Aug. 2018, v. 258, p. 116-127en_US
dcterms.isPartOfJournal of materials processing technologyen_US
dcterms.issued2018-08-
dc.identifier.scopus2-s2.0-85044983070-
dc.identifier.eissn1873-4774en_US
dc.description.validate202402 bcchen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberISE-0616-
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextResearch Committee of The Hong Kong Polytechnic University (RTBN); Research Grants Council of the Hong Kong Special Administrative Region (PolyU 511511)en_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS6832805-
dc.description.oaCategoryGreen (AAM)en_US
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