The impact of self-learning thermal error compensation models on the accuracy of 4-axis thermal test pieces
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Date
2022-11-30
Publication Type
Conference Paper
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yes
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Abstract
Self-learning thermal error compensation models are a cost-effective and robust approach to reduce thermal errors of machine tools. This paper evaluates a self-learning thermal error compensation strategy under cutting conditions with and without metal working fluid using thermal test pieces mapping the most relevant thermal errors of a rotary table. The experimental results indicate that the self-learning thermal error compensation models reduce the deviations of the thermal test pieces for both cutting conditions between 63% and 73%. Consequently, the machining results on the thermal test pieces verify the ability of the self-learning thermal compensation models to reduce robustly thermal errors of machine tools.
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published
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Publisher
ETH Zurich
Event
19th International Conference on Precision Engineering (ICPE 2022)
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Software
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Date created
Subject
Machine tool; Thermal error; Thermal test piece; self-learning machine tools
Organisational unit
03641 - Wegener, Konrad (emeritus) / Wegener, Konrad (emeritus)
03641 - Wegener, Konrad (emeritus) / Wegener, Konrad (emeritus)
09706 - Bambach, Markus / Bambach, Markus
Notes
Conference lecture held on November 30, 2022.