Machine learning-based high-precision and real-time focus detection for laser material processing systems

Can Polat; Gizem Nuran Yapici; Sepehr Elahi; Parviz Elahi

doi:10.1117/12.2624383

17 May 2022 Machine learning-based high-precision and real-time focus detection for laser material processing systems

Can Polat, Gizem Nuran Yapici, Sepehr Elahi, Parviz Elahi

Proceedings Volume 12138, Optics, Photonics and Digital Technologies for Imaging Applications VII; 1213803 (2022) https://doi.org/10.1117/12.2624383
Event: SPIE Photonics Europe, 2022, Strasbourg, France

Abstract

This work explores a real-time and high precision focus finding for the ultrafast laser material processing for a different types of materials. Focus detection is essential for laser machining because an unfocused beam cannot affect the material and, at worst, a destructive effect. Here, we compare CNN and non-CNN-based approaches to focus detection, ultimately proposing a robust CNN model that can achieve high performance when only trained on a portion of the dataset. We use an ordinary lens (11 mm focal length, 0.25 NA) and a CMOS camera. Our robust CNN model achieved a focus prediction accuracy of 95% when identifying focus distances in {-150, -140,...,0,...,150} µm, each step is about 7% of the Rayleigh length, and a high processing speed of 1000+ Hz on a CPU

Conference Presentation

Citation Download Citation

Can Polat, Gizem Nuran Yapici, Sepehr Elahi, and Parviz Elahi "Machine learning-based high-precision and real-time focus detection for laser material processing systems", Proc. SPIE 12138, Optics, Photonics and Digital Technologies for Imaging Applications VII, 1213803 (17 May 2022); https://doi.org/10.1117/12.2624383

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