Metadata only
Datum
2020Typ
- Journal Article
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. Mehr anzeigen
Publikationsstatus
publishedExterne Links
Zeitschrift / Serie
CIRP AnnalsBand
Seiten / Artikelnummer
Verlag
ElsevierThema
Adaptive manufacturing; Thermal error; Sensor selectionOrganisationseinheit
03641 - Wegener, Konrad / Wegener, Konrad