Polat, CanYapici, Gizem NuranElahi, SepehrElahi, Parviz2024-03-272024-03-272023-05-180143-8166https://hdl.handle.net/11693/115124This work presents a precise positioning detection based on a convolutional neural network (CNN) to control the laser focus in laser material processing systems. The images of the diffraction patterns measured at different positions of the laser focus concerning the workpiece are classified in the range of the Rayleigh length of the focusing lens with an increment of about 7% of it. The experiment was carried out on different materials with different levels of surface roughness, such as copper, silicon, and steel, and over 99% accuracy in the positioning detection was achieved. Considering surface roughness and camera noise, a theoretical model is established, and the effects of these parameters on the accuracy of focus detection are also presented. The proposed method exhibits a noise-robust focus detection system and the potential for many precise positioning detection systems in industry and biology. © 2023 Elsevier Ltd.enCC BY 4.0 DEED (Attribution 4.0 International)https://creativecommons.org/licenses/by/4.0/Deep learningFocus detectionFourier opticsMachine learningUltra-fast laser micromachiningArticle10.1016/j.optlaseng.2023.1076461873-0302