Autonomously triggered model updates for self-learning thermal error compensation

Abstract The presented method significantly increases the self-optimization ability of thermal error compensation models by triggering on-machine measurements when unknown thermal conditions occur. These conditions, which are not represented by the training data of the compensation models, are identified by a novelty detection approach based on one-class support vector machines. The results show that the autonomously triggered on-machine measurements applied to a 5-axis machine tool overcome the trade-off between precision and productivity for thermal error compensation. The non-productive time to detect an exceedance of the predefined tolerances is reduced by 78% without significantly reducing the precision of the thermal error compensation.

[1]  Philip Blaser,et al.  An adaptive self-learning compensation approach for thermal errors on 5-axis machine tools handling an arbitrary set of sample rates , 2018 .

[2]  Zhiqiang Gao,et al.  Toward Intelligent Machine Tool , 2019, Engineering.

[3]  Philip Blaser,et al.  Adaptive learning control for thermal error compensation of 5-axis machine tools , 2017 .

[4]  Christian Brecher,et al.  Compensation of Thermo-elastic Machine Tool Deformation Based on Control internal Data , 2004 .

[5]  Christian Brecher,et al.  Thermal issues in machine tools , 2012 .

[6]  Otakar Horejš,et al.  Thermal error compensation of a 5-axis machine tool using indigenous temperature sensors and CNC integrated Python code validated with a machined test piece , 2020, Precision Engineering.

[7]  Bernhard Schölkopf,et al.  Support Vector Method for Novelty Detection , 1999, NIPS.

[8]  David A. Clifton,et al.  A review of novelty detection , 2014, Signal Process..

[9]  Hyun Joon Shin,et al.  One-class support vector machines - an application in machine fault detection and classification , 2005, Comput. Ind. Eng..

[10]  Nico Zimmermann,et al.  Adaptive input selection for thermal error compensation models , 2020 .

[11]  Josef Mayr,et al.  High precision grey-box model for compensation of thermal errors on five-axis machines , 2014 .

[12]  Hans-Christian Möhring,et al.  Self-optimizing machining systems , 2020, CIRP Annals.

[13]  Soichi Ibaraki,et al.  Adaptive thermal displacement compensation method based on deep learning , 2019, CIRP Journal of Manufacturing Science and Technology.

[14]  H. Akaike.  A new look at the statistical model identification , 1974 .