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Dynamic parameter identification in nonlinear machining systems
KTH, School of Industrial Engineering and Management (ITM), Production Engineering. (Maskin och processteknologi)
KTH, School of Industrial Engineering and Management (ITM), Production Engineering. (Maskin och processteknologi)ORCID iD: 0000-0001-9185-4607
2013 (English)In: Journal of Machine Engineering, ISSN ISSN 1895-7595X, Vol. 13, no 3, 91-116 p.Article in journal (Refereed) Published
Abstract [en]

The demand for enhanced performance of production systems in terms of quality, cost and reliability is ever increasing while, at the same time, there is a demand forshorter design cycles, longer operating life, minimisation of inspection and maintenance needs. Experimental testing and system identification in operational conditionsstill represent an important technique for monitoring, control andoptimization. The term identification refers in the present paper to the extraction of information from experimental data and is used to estimate operationaldynamic parameters for machining system. Such approach opens up the possibility of monitoring the dynamics of machining system during operational conditions, and to be used for control and/or predictive purposes. Machining system is considered non-linear and excited by random loads. Parametric and non-parametric techniques are developed for the identification of the non-linear machiningsystem and their application is demonstrated both by numerical simulations and in actual machining operations. Discrimination between forced and self-excitedvibration is also presented. The ability of the developed methods to estimate operational dynamic parameters ODPs is presented in practical machining operations.

Place, publisher, year, edition, pages
2013. Vol. 13, no 3, 91-116 p.
Keyword [en]
Machining systems, dynamic parameters, white noise excitation, self-excited vibrations
National Category
Engineering and Technology
Research subject
SRA - Production
URN: urn:nbn:se:kth:diva-136746OAI: diva2:677018
XPRES - Initiative for excellence in production research

QC 20131209

Available from: 2013-12-09 Created: 2013-12-09 Last updated: 2016-04-19Bibliographically approved

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