Advanced Manufacturing Configuration by Sample-Efficient Batch Bayesian Optimization
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Date
2022-10
Publication Type
Journal Article
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yes
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Abstract
We propose a framework for the configuration and operation of expensive-to-evaluate advanced manufacturing methods, based on Bayesian optimization. The framework unifies a tailored acquisition function, a parallel acquisition procedure, and the integration of process information providing context to the optimization procedure. The novel acquisition function is demonstrated, analyzed and compared on state-of-the-art bench-marking problems. We apply the optimization approach to atmospheric plasma spraying and fused deposition modeling. Our results demonstrate that the proposed framework can efficiently find input parameters that produce the desired outcome and minimize the process cost.
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Publication status
published
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Journal / series
Volume
7 (4)
Pages / Article No.
11886 - 11893
Publisher
IEEE
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Edition / version
Methods
Software
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Date collected
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Subject
Bayesian optimization; intelligent and flexible manufacturing; machine learning for control; probability and statistical methods; process control
Organisational unit
03751 - Lygeros, John / Lygeros, John
Notes
Funding
180545 - NCCR Automation (phase I) (SNF)