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Finite element modelling and characterisation of chip curl in nose turning process
University West, Department of Engineering Science, Research Enviroment Production Technology West. R&D Turning, Sandvik Coromant, Sandviken. (PTW)ORCID iD: 0000-0003-3877-9067
University West, Department of Engineering Science, Division of Subtractive and Additive Manufacturing. (PTW)ORCID iD: 0000-0003-0976-9820
R&D Turning, Sandvik Coromant, Sandviken.
2017 (English)In: International Journal of Machining and Machinability of Materials, E-ISSN 1748-572X, Vol. 19, no 3, p. 277-295Article in journal (Refereed) Published
Abstract [en]

Finite element (FE) modelling of machining provide valuable insights into its deformation mechanics. Evaluating an FE model predicted chip morphology requires characterisation of chip shape, chip curl and chip flow angles. In this study, a chip morphology characterisation methodology is developed using computed tomography (CT), high-speed imaging and Kharkevich model equations enabling evaluation of FE model’s chip morphology prediction accuracy. Chip formation process in nose turning of AISI 1045 steel is simulated using a 3D FE model for varying feed rate and depth of cut and evaluated against experimental investigations using the employed methodology. The study shows that the methodology is able to characterise chip morphology in nose turning process accurately and enables evaluation of FE model’s chip morphology prediction accuracy. This can enable the finite element model to be deployed in cutting tool design for chip breaker geometry design.

Place, publisher, year, edition, pages
InderScience Publishers, 2017. Vol. 19, no 3, p. 277-295
Keywords [en]
machining, chip-up-curl, chip-side-curl, chip shape, computed tomography, CT, finite element model, chip side flow angle, Johnson cook model, coulomb friction model, advantedge, high speed videography
National Category
Manufacturing, Surface and Joining Technology
Research subject
ENGINEERING, Manufacturing and materials engineering; Production Technology
Identifiers
URN: urn:nbn:se:hv:diva-11575DOI: 10.1504/IJMMM.2017.084009Scopus ID: 2-s2.0-85019137109OAI: oai:DiVA.org:hv-11575DiVA, id: diva2:1142648
Available from: 2017-09-19 Created: 2017-09-19 Last updated: 2019-11-18Bibliographically approved

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Devotta, Ashwin MorisBeno, Tomas

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