Adaptive input selection for thermal error compensation models
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Author / Producer
Date
2020
Publication Type
Journal Article
ETH Bibliography
yes
Citations
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Abstract
The presented method selects optimal inputs for compensation models based on the Thermal Adaptive Learning Control methodology. The number of inputs and the individual inputs for each considered thermal error are automatically adapted. The intelligent combination of k-means clustering and Time Series Cluster Kernel enables the approach to handle time series of thermal error measurements with missing data due to operational reasons. The results show that the adaptive sensor selection approach, tested on a 5-axis-machine tool, significantly increases the robustness of the used compensation model. The productivity loss due to on-machine measurements is reduced by approximately 40 percent. © 2020 CIRP. Published by Elsevier Ltd. All rights reserved.
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Publication status
published
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Editor
Book title
Journal / series
Volume
69 (1)
Pages / Article No.
485 - 488
Publisher
Elsevier
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Adaptive manufacturing; Thermal error; Sensor selection
Organisational unit
03641 - Wegener, Konrad (emeritus) / Wegener, Konrad (emeritus)