Noise robust focal distance detection in laser material processing using CNNs and Gaussian processes

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buir.contributor.authorElahi, Sepehr
buir.contributor.orcidElahi, Sepehr|0000-0001-5494-6465
dc.citation.epage7en_US
dc.citation.spage1en_US
dc.citation.volumeNumber12138en_US
dc.contributor.authorElahi, Sepehr
dc.contributor.authorPolat, Can
dc.contributor.authorSafarzadeh, Omid
dc.contributor.authorElahi, Parviz
dc.coverage.spatialStrasbourg, Franceen_US
dc.date.accessioned2023-02-20T12:10:37Z
dc.date.available2023-02-20T12:10:37Z
dc.date.issued2022-05-17
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractIn 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.en_US
dc.identifier.doi10.1117/12.2624337en_US
dc.identifier.issn0277-786X
dc.identifier.urihttp://hdl.handle.net/11693/111549
dc.language.isoEnglishen_US
dc.publisherS P I E - International Society for Optical Engineeringen_US
dc.relation.isversionofhttps://doi.org/10.1117/12.2624337en_US
dc.source.titleSPIE - International Society for Optical Engineeringen_US
dc.subjectFocus detectionen_US
dc.subjectFourier opticsen_US
dc.subjectMachine learningen_US
dc.subjectSurface roughnessen_US
dc.subjectDeep learningen_US
dc.subjectGaussian processen_US
dc.titleNoise robust focal distance detection in laser material processing using CNNs and Gaussian processesen_US
dc.typeConference Paperen_US
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