2019
DOI: 10.1016/j.optlaseng.2019.03.014
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Statistical evaluation method to determine the laser welding depth by optical coherence tomography

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Cited by 41 publications
(18 citation statements)
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“…One approach for an inline depth control of the laser welding process is optical coherence tomography (OCT), which is applied for many applications in macro laser welding already [1][2][3]. For micro laser welding, only few investigations have been carried out.…”
Section: Introductionmentioning
confidence: 99%
“…One approach for an inline depth control of the laser welding process is optical coherence tomography (OCT), which is applied for many applications in macro laser welding already [1][2][3]. For micro laser welding, only few investigations have been carried out.…”
Section: Introductionmentioning
confidence: 99%
“…Interferometric techniques (including RRI) offer illumination and detection from an instrument mounted at a single location and are hence more easily integrated into mounting structures. Optical coherence tomography (OCT), an interferometric technique conceptually similar to RRI, has been widely applied to welding research [14][15][16][17], although in these implementations the ability of OCT to image structure within turbid materials [10] such as human skin is not utilised as the light cannot penetrate the metal surface. OCT can in principle achieve 0.01 mm but typically has a working range of only a few millimetres (compared to up to 10 s of cm for RRI).…”
Section: Introductionmentioning
confidence: 99%
“…One solution to observe the weld penetration depth inline is optical coherence tomography (OCT). OCT is an interferometric measurement technology that enables the measurement of weld penetration depth coaxially to the processing laser in a fixed optic [5][6][7][8][9][10][11][12] or scanning optic [13][14][15].…”
Section: Introductionmentioning
confidence: 99%
“…The process noise can be caused by changes in the optical path by the vapor plume in the welding process and thermal noise [16]. To date, this issue was treated with data analysis methods such as histogram analysis [9,17], different filtering (e.g., percentile filter [9,10], Kalman filtering [14]), or kernel density estimation (KDE) [1,7]. The simplest method is the evaluation of histograms at every measurement point [17] and the extraction of the local maximum [9].…”
Section: Introductionmentioning
confidence: 99%