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The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

Feedback control of Layerwise Laser Melting using optical sensors

Open access
Craeghs, Tom, Bechmann, Florian, Berumen, Sebastian, Kruth, Jean-Pierre·2010·Physics Procedia

Abstract

Layerwise Laser Melting (LLM) is a layerwise production technique enabling the production of complex metallic parts. Thin powder layers are molten according to a predefined scan pattern by means of a laser source. Nowadays constant process parameters are used throughout the build, leading for some geometries to an overly thick feature size or overheating at downfacing surfaces. In this paper a monitoring and control system is presented which enables monitoring the melt pool continously at high speed throughout the building process. The signals from the sensors can be incorporated in a real-time control loop, in this way enabling feedback control of the process parameters. In this paper the experimental set-up will be first shown. Next the dynamic relation between the melt pool and the process parameters is identified. Finally the proof of concept for feedback control is demonstrated with experimental results.

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Crossref · 10.1016/j.phpro.2010.08.078

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Metadata

Authors
Craeghs, T., Bechmann, F., Berumen, S., & Kruth, J.-P.
Venue
Physics Procedia
Publisher
Elsevier BV
Published
2010
Volume
5
Pages
505-514
ISSN
1875-3892 (ISSN-L)
OpenAlex
W2066240202
Cited by
288
References
7
FWCI
6.69
Type
journal article
Language
en
Open access
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CoChat. (2026). Citation check: Feedback control of Layerwise Laser Melting using optical sensors. https://app.cochat.ai/papers/10.1016/j.phpro.2010.08.078
The paper
Craeghs, T., Bechmann, F., Berumen, S., & Kruth, J.-P. (2010). Feedback control of Layerwise Laser Melting using optical sensors. Physics Procedia. https://doi.org/10.1016/j.phpro.2010.08.078

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.