Autonomously triggered model updates for self-learning thermal error compensation
Autor: | Nico Zimmermann, Mario Breu, Konrad Wegener, Josef Mayr |
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Rok vydání: | 2021 |
Předmět: |
0209 industrial biotechnology
Training set business.product_category Computer science Mechanical Engineering 02 engineering and technology Novelty detection Industrial and Manufacturing Engineering Machine tool Compensation (engineering) Support vector machine 020303 mechanical engineering & transports 020901 industrial engineering & automation 0203 mechanical engineering Control theory Thermal business Productivity |
Zdroj: | CIRP Annals. 70:431-434 |
ISSN: | 0007-8506 |
DOI: | 10.1016/j.cirp.2021.04.029 |
Popis: | 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. |
Databáze: | OpenAIRE |
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