Turning: Autonomous process set-up through Bayesian optimization and Gaussian process models
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Date
2020
Publication Type
Conference Paper
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yes
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Abstract
In turning, process parameters such as feed rate and cutting speed are normally selected by an operator and adapted depending on the process requirements. In this study, autonomous process set-up of the turning process is investigated based on Bayesian optimization and Gaussian process models. Unconstrained Bayesian optimization is compared to an implementation with added constraint with simulated machining parameters. It is shown that constraint Bayesian optimization has a better performance in the turning process set-up. The method is suitable for measurements with uncertainty and typically results in reduced number of experiments.
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Publication status
published
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Book title
Journal / series
Volume
88
Pages / Article No.
306 - 311
Publisher
Elsevier
Event
13th CIRP Conference on Intelligent Computation in Manufacturing Engineering (CIRP ICME ˈ19)
Edition / version
Methods
Software
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Date collected
Date created
Subject
Bayesian optimization; Process set-up; Turning; Gaussian process models
Organisational unit
03641 - Wegener, Konrad (emeritus) / Wegener, Konrad (emeritus)