Detection and tracking of laser damage sites on fused silica components by digital image correlation

Hallo, Guillaume; Lacombe, Chloé; Néauport, Jérôme; Hild, François

doi:10.1016/j.optlaseng.2021.106674

Cited by 12 publications

(7 citation statements)

References 43 publications

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“…The effective and optimal use of these methods relies on the early detection of the growth of damage sites ranging from 50 µm to 750 µm in diameter. These damage diameters are close to the field of view of one pixel for observation systems of high energy laser facilities such as the Final Optics Damage Inspection (FODI) at NIF [18], the SG-III FODI [19] and the Chamber Center Diagnostic Module 25 (MDCC) on the LMJ facility [15,20]. In order to make damage sites visible, observed optics are illuminated by their edges, resulting in dark-field pictures on which damage sites appear as bright spots.…”

Section: Introductionmentioning

confidence: 68%

Estimating and monitoring laser-induced damage size on glass windows with a deep-learning-based pipeline

SOLTANE¹,

Hallo

Lacombe

et al. 2022

J. Opt. Soc. Am. A

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Laser-induced damage is a major issue in high power laser facilities such as the Laser MégaJoule (LMJ) and National Ignition Facility (NIF) since they lower the efficiency of optical components and may even require their replacement. This problem occurs mainly in the final stages of the laser beamlines and in particular in the glass windows through which laser beams enter the central vacuum chamber. Monitoring such damage sites in high energy laser facilities is, therefore, of major importance. However, the automatic monitoring of damage sites is challenging due to the small size of damage sites and to the low-resolution images provided by the onsite camera used to monitor their occurrence. A systematic approach based on a deep learning computer vision pipeline is introduced to estimate the dimensions of damage sites of the glass windows of the LMJ facility. The ability of the pipeline to specialize in the estimation of damage sites of a size less than the repair threshold is demonstrated by showing its higher efficiency than classical machine learning approaches in the specific case of damage site images. In addition, its performances on three datasets are evaluated to show both robustness and accuracy.

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Section: Introductionmentioning

confidence: 68%

Estimating and monitoring laser-induced damage size on glass windows with a deep-learning-based pipeline

SOLTANE¹,

Hallo

Lacombe

et al. 2022

J. Opt. Soc. Am. A

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“…Digital Image Correction was used to estimate the displacement field 𝒖 between reference image 𝐼 0 and subsequent image 𝐼 𝑛 . By decomposing the displacement field on a selected basis consisting of 4 fields (i.e., rigid body translations and rotation, as well as scale changes) [28], the cost function…”

Section: Discussionmentioning

confidence: 99%

“…Throughout the growth sequence, images were affected by disturbances such as displacements, spurious light and focus changes. In order to ensure that the images are comparable during the whole sequence, with differences between them only caused by damage growth, all of those changes had to be corrected [28]. Displacements between images were corrected using Digital…”

Section: Image Correctionsmentioning

confidence: 99%

Estimation of laser-induced damage depth from surface image features

et al. 2023

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In laser damage experiments, damage initiation and growth are typically monitored by imaging the surface of the tested fused silica sample, ignoring their bulk morphology. The depth of a damage site in fused silica optics is considered to be proportional to its equivalent diameter. However, some damage sites experience phases with no diameter changes but growth in the bulk independently from their surface. A proportionality relationship with the damage diameter does not accurately describe the growth of such sites. In the following, an accurate estimator for damage depth is proposed, which is based on the hypothesis that the light intensity scattered by a damage site is proportional to its volume. Such an estimator, using the pixel intensity, describes the change of damage depth through successive laser irradiations, including phases in which depth and diameter variations are uncorrelated.

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“…In global DIC/digital volume correlation (DVC) applications, the gray level residual (GLR) field is widely used to assess pixelwise matching quality [12,13]. As, it can present the unmatched area or new components, the GLR field is naturally used for defect detection [14][15][16] and crack location [17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Gray level residual field: an effective metric for pixelwise matching quality evaluation in local digital image correlation

Chi

Zhao

Pan

2023

Meas. Sci. Technol.

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Gray level residual (GLR), also known as correlation residual field or image difference, reflects the intensity differences between reference and deformed images. In finite element-based global digital image correlation (DIC), GLR field can be readily computed and has been successfully used for crack or damage detection. The computation of pixel-wise GLR field requires dense displacement results, which can be obtained using mesh interpolation in global DIC. To obtain the GLR field in subset-based local DIC, in which results are obtained at discrete calculation points, a simple, efficient and practical computation method for GLR field is presented, which accounts for the influence of illumination variations unavoidably occurred in real DIC tests. The proposed method can be easily implemented on both CPU and GPU to realize real-time visualization. Experiments showed that the GLR field can quantitatively and intuitively evaluate the pixelwise matching quality and provide clearer and finer locations for new components or textures (e.g., cracks) presented in deformed images. By fully leveraging the pixelwise matching quality information provided by the GLR field, the capability and robustness of local DIC is expected to be further enhanced.

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Detection and tracking of laser damage sites on fused silica components by digital image correlation

Cited by 12 publications

References 43 publications

Estimating and monitoring laser-induced damage size on glass windows with a deep-learning-based pipeline

Estimating and monitoring laser-induced damage size on glass windows with a deep-learning-based pipeline

Estimation of laser-induced damage depth from surface image features

Gray level residual field: an effective metric for pixelwise matching quality evaluation in local digital image correlation

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