Model Uncertainty Estimation for Thermal Error Compensation in Machine Tools
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Date
2024-03-14
Publication Type
Conference Paper
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yes
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Abstract
This paper introduces a method to compensate for thermal errors in machine tools (MT) using LSTM neural networks, with a focus
on addressing prediction uncertainties. It presents the application of Monte Carlo Dropout (MC-Dropout) to estimate the uncertainty
of LSTM predictions using data generated in a simulated MT environment. MC-Dropout offers a practical, computationally efficient
method to allow for effective thermal error compensation without repeated on-machine measurements. Incorporating uncertainty
estimates can enhance decision-making, thus allowing more autonomous machine operations and improve the selection of training
data for machine learning models, leading to greater overall prediction accuracy.
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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
Monte Carlo Dropout; Uncertainty estimation; LSTM; Thermal error prediction; Simulation of machine tools
Organisational unit
09706 - Bambach, Markus / Bambach, Markus