Continuous learning support vector machine to estimate stability lobe diagrams in milling

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dc.contributorInternational FAIM Conference 24th : 2014 : San Antonio, Texas
dc.contributorUniversity of Texas at San Antonio. Center for Advanced Manufacturing and Lean Systems
dc.contributor.authorFriedrich, Jens
dc.contributor.authorHartmann, Henning
dc.contributor.authorVerl, Alexander
dc.contributor.authorLechler, Armin
dc.date.accessioned2022-07-11T17:35:35Z
dc.date.available2022-07-11T17:35:35Z
dc.date.issued2014
dc.descriptionPaper presented at the Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing, held May 20-23, 2014 in San Antonio, Texas, and organized by the Center for Advanced Manufacturing and Lean Systems, University of Texas at San Antonio
dc.descriptionIncludes bibliographical references
dc.description.abstractThe productivity of milling processes is limited by the occurrence of chatter vibrations. The correlation of the maximum stable cutting depth and the spindle speed can be shown in a stability lobe diagram (SLD). Today it is a great effort to estimate the SLD. Lot's of experiments are necessary to measure the SLD or derive a detailed mathematical model to calculate the SLD. Moreover not only cutting depth, but also the cutting width should be taken into account. This paper presents an approach to learn the multidimensional stability lobe diagram (MSLD) during the production based on continuously measured signals using a support vector machine. The support vector machine is extended to make it capable for continuous learning and time-variant systems. The process conditions are classified as stable or unstable. The learned MSLDs are very similar to the analytically calculated MSLDs. Changes over time in the system dynamics can also be learned by the proposed algorithm.
dc.identifier.otherhttp://dx.doi.org/10.14809/faim.2014.0641
dc.identifier.urihttps://hdl.handle.net/20.500.12588/1000
dc.language.isoen_US
dc.publisherDEStech Publications, Inc.
dc.relation.ispartofseriesProceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing
dc.subjectMilling-machines--Vibration
dc.subjectMilling cutters
dc.subjectChattering control (Control systems)
dc.subjectSpindles (Machine-tools)
dc.titleContinuous learning support vector machine to estimate stability lobe diagrams in milling
dc.typeArticle

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