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      • Faculty of Engineering
      • Department of Electrical and Electronics Engineering
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      Noise robust focal distance detection in laser material processing using CNNs and Gaussian processes

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      Author(s)
      Elahi, Sepehr
      Polat, Can
      Safarzadeh, Omid
      Elahi, Parviz
      Date
      2022-05-17
      Source Title
      SPIE - International Society for Optical Engineering
      Print ISSN
      0277-786X
      Publisher
      S P I E - International Society for Optical Engineering
      Volume
      12138
      Pages
      1 - 7
      Language
      English
      Type
      Conference Paper
      Item Usage Stats
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      Abstract
      In this work, we investigate the effects of noise on real-time focal distance control for laser material processing by generating the images of a sample at different focal lengths using Fourier optics and then designing, training, and testing a deep learning model in order to detect the focal distances from the simulated images with varying standard deviations of added noise. We simulate both input noise, such as noise due to surface roughness, and output noise, such as detection camera noise, by adding zero-mean Gaussian noise to the source wave and the simulated image, respectively, for different focal distances. We then train a convolutional neural network combined with a Gaussian process classifier to predict focus distances of noisy images together with confidence ratings for the predictions.
      Keywords
      Focus detection
      Fourier optics
      Machine learning
      Surface roughness
      Deep learning
      Gaussian process
      Permalink
      http://hdl.handle.net/11693/111549
      Published Version (Please cite this version)
      https://doi.org/10.1117/12.2624337
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      • Department of Electrical and Electronics Engineering 4011
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