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  • 标题:Continuous identification for mechanistic force models in milling
  • 本地全文:下载
  • 作者:M. Schwenzer ; S. Stemmler ; M. Ay
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2019
  • 卷号:52
  • 期号:13
  • 页码:1791-1796
  • DOI:10.1016/j.ifacol.2019.11.461
  • 语种:English
  • 出版社:Elsevier
  • 摘要:Force determines the product quality, the productivity and the safety of a milling process. Mechanistic force models are the key to understand, optimize or control the cutting process. They combine the undeformed chip parameter with empiric tuning coefficients in a gray-box model. Identifying those coefficients is costly in both, time and number of experiments. This paper introduces two recursive identification methods for force model identification: recursive least squares and ensemble Kalman filters. The model is nonlinear. The ensemble Kalman filter shows an extraordinary robustness against measurement noise and a fast convergence time -depending on the selection of the ensemble size and the measurement noise. The recursive least squares fit serves as a benchmark but is highly sensitive to measurement noise. It is the first time that a continuous identification is examined for mechanistic force models in milling.
  • 关键词:KeywordsEnsemble Kalman filterRecursive least squaresManufacturingMillingParameter identificationIdentificationnonlinear model identification
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