Continuous identification for mechanistic force models in milling. Issue 13 (2019)

• Record Type: Journal Article
• Authors: Schwenzer, M.; Stemmler, S.; Ay, M.; Bergs, T.; Abel, D.
• Is Part Of: IFAC-PapersOnLine. Volume 52:Issue 13(2019)
• Page Start: 1791
• Page End: 1796
• Publication Date: 2019
• Subjects: Ensemble Kalman filter -- Recursive least squares -- Manufacturing -- Milling -- Parameter identification -- Identification -- nonlinear model identification; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2019.11.461
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: 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.