Training efficient and compensating fast: Data augmentation for thermal error compensation models of machine tools
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Date
2024-03-14
Publication Type
Conference Paper
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yes
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Abstract
The increasing importance of sustainability in manufacturing has created a trilemma situation between resource efficiency, productivity and precision. As the relevance of thermal errors increases with the shift to less cooling-reliant manufacturing approaches, compensation models for thermal errors have been proposed as a possible solution to the described trilemma. Since those data-driven compensation models are heavily reliant on both data amount and data quality, this paper aims at investigating different approaches of preprocessing and potentially augmenting the input data for compensation models. This allows for a higher sampling frequency than that of the employed measurement cycle and makes more, as well as additional synthetic, data points available for training. The employed ARX model can reduce the volumetric error by around two thirds. The use of data augmentation represents an increase in volumetric modelling accuracy from 48.2% to 65.8 % without requiring any additional measurement effort.
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Publication status
published
Editor
Book title
SIG : Thermal Issues - Proceedings
Journal / series
Volume
Pages / Article No.
Publisher
European Society for Precision Engineering and Nanotechnology
Event
euspen Special Interest Group Meeting: Thermal Issues
Edition / version
Methods
Software
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Date collected
Date created
Subject
Data augmentation; Thermal error compensation; Machine tools; Upsampling
Organisational unit
09706 - Bambach, Markus / Bambach, Markus