Acousto-optic material differentiation during water jet-guided laser cutting by applying a neural network

By applying a combination of statistical feature analysis and machine learning we demonstrate the feasibility of detecting different materials in a multi-material layered substrate based on optical and acoustic signatures during water jet-guided laser cutting.

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Bibliographic Details
Published in2024 Conference on Lasers and Electro-Optics Pacific Rim (CLEO-PR) pp. 1 - 2
Main Authors Richter, Roland Axel, Disalvo, Luca, Ivas, Toni, Pandiyan, Vigneashwara, Zryd, Amedee, Hoffmann, Patrik, Shevchik, Sergey
Format Conference Proceeding
LanguageEnglish
Published IEEE 04.08.2024
Subjects
Feature extraction
Lasers and electrooptics
Machine learning
Neural networks
Optical computing
Optical materials
Water jet cutting
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Summary:By applying a combination of statistical feature analysis and machine learning we demonstrate the feasibility of detecting different materials in a multi-material layered substrate based on optical and acoustic signatures during water jet-guided laser cutting.
ISSN:2997-7037
DOI:10.1109/CLEO-PR60912.2024.10676839