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In-process identification of modal parameters using dimensionless relationships in milling chatter
University West, Department of Engineering Science, Division of Subtractive and Additive Manufacturing. (PTW)ORCID iD: 0000-0001-9331-7354
2019 (English)In: International journal of machine tools & manufacture, ISSN 0890-6955, E-ISSN 1879-2170, Vol. 143, p. 49-62Article in journal (Refereed) Published
Abstract [en]

Machining parameters needed for stable, high-performance high-speed machining could be found using mathematical models that need accurate measurements of modal parameters of the machining system. In-process modal parameters, however, can slightly differ from those measured offline and this can limit the applicability of simple measurement methods such as impact hammer tests. To study and extract the in-process modal parameters, mathematical models are used to define two key dimensionless parameters and establish their relationships with each other and the modal parameters. Based on these relationships, the modal parameters are extracted using two analytical methods, the two-point method (TPM), and the regression method (RM). As shown with experimental studies, the RM extracts the modal parameters successfully and while being much faster than the existing iteration-based methods, it provides stability lobe predictions that match well the experimental results. Furthermore, it is noted that the natural frequency parameter is estimated with much better relative precision compared to the damping ratio and the modal stiffness parameters. © 2019 Elsevier Ltd

Place, publisher, year, edition, pages
2019. Vol. 143, p. 49-62
Keywords [en]
Composite beams and girders, Identification (control systems), Iterative methods, Milling (machining), Modal analysis, Regression analysis, Accurate measurement, Chatter, Dimensionless parameters, Frequency parameters, High speed machining, In-process, Machining dynamics, Stability lobe diagrams, Parameter estimation
National Category
Manufacturing, Surface and Joining Technology
Research subject
ENGINEERING, Manufacturing and materials engineering; Production Technology
Identifiers
URN: urn:nbn:se:hv:diva-13988DOI: 10.1016/j.ijmachtools.2019.04.003ISI: 000471196800005Scopus ID: 2-s2.0-85066478846OAI: oai:DiVA.org:hv-13988DiVA, id: diva2:1327995
Funder
Knowledge FoundationAvailable from: 2019-06-20 Created: 2019-06-20 Last updated: 2020-02-03Bibliographically approved

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Eynian, Mahdi

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