In situ process monitoring in selective laser sintering using optical coherence tomography

Gardner, Michael R.; Lewis, Adam; Park, Jongwan; McElroy, Austin; Estrada, Arnold D.; Fish, Scott; Beaman, Joseph J.; Milner, Thomas E.

doi:10.1117/1.oe.57.4.041407

Cited by 52 publications

(20 citation statements)

References 14 publications

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“…Another application field of on-axis image processing is optical coherence tomography (OCT). This optical on-axis imaging technique is based on low-coherence interferometry and allows an evaluation of the melting pool surface, as well as its depth and heat distribution (Gardner et al, 2018).…”

Section: Process-accompanying Quality Control By Optical Measurement Techniquesmentioning

confidence: 99%

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Methods and procedure of referenced in situ control of lateral contour displacements in additive manufacturing

Lerchen¹,

Hornung²,

Zou³

et al. 2021

J. Sens. Sens. Syst.

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Abstract. Additive manufacturing technologies are further developing from prototype to serial production. This trend requires rising challenges to the process-accompanying quality assurance. Optical in situ quality control approaches show great potential to generate accurate measurement data, which are essential for feedback control. If a reliable referencing concept for the layer-by-layer measured data is guaranteed, contour information can be used during the manufacturing to correct occurring geometrical deviations. Within this scientific study, two methods of optical, referenced in situ control of lateral displacements of additive manufactured contours are presented. In the first approach the 2-D contour of the melting pool is analysed in relation to a position-stable reference system implemented in the powder bed. The second approach uses the translucent contour of deeper layers covered with powder as a reference. Within the image evaluation several pre-processing steps like calibration, undistortion, rectification, illumination correction and low-pass filtering are essential for reliable and correct geometric measurements. The following adapted contour detection and position determination of the referenced melting pool contours are based on an extended edge detection algorithm according to Canny (1986). With the evaluation of further manufacturing layers of already lowered powder bed levels, it is possible to specify the influence of powder application on geometrical displacements separately. This is done by a comparison of the position of the detected powder-covered melting pool contours with the previously applied melted region. Consequently a better understanding of lateral contour displacements within the additive manufacturing process is the goal, which is important for a process-accompanying correction of geometrical deviations.

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Section: Process-accompanying Quality Control By Optical Measurement Techniquesmentioning

confidence: 99%

“…Additive manufacturing (AM) processes are being progressively established as new production technologies in industry (Gebhard et al, 2019). They are characterised by an incremental addition of material and differ in their binding mechanisms, manufacturing materials and corresponding processing methods (DIN EN ISO/ASTM-52900, 2015).…”

Section: Introductionmentioning

confidence: 99%

Methods and procedure of referenced in situ control of lateral contour displacements in additive manufacturing

Lerchen¹,

Hornung²,

Zou³

et al. 2021

J. Sens. Sens. Syst.

View full text Add to dashboard Cite

show abstract

“…The additional mentioned limitation of the OCT scanner applied to quality monitoring purposes is its inability of detection of overhangs or undercuts. Another application of the OCT to in situ process monitoring for selective laser sintering 3D printers has been recently proposed by Gardner et al [17].…”

Section: Overview Of Machine Vision In Monitoring Of 3d Printingmentioning

confidence: 99%

Improved quality assessment of colour surfaces for additive manufacturing based on image entropy

Okarma

Fastowicz

2020

Pattern Anal Applic

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A reliable automatic visual quality assessment of 3D-printed surfaces is one of the key issues related to computer and machine vision in the Industry 4.0 era. The colour-independent method based on image entropy proposed in the paper makes it possible to detect and identify some typical problems visible on the surfaces of objects obtained by additive manufacturing. Depending on the quality factor, some of such 3D printing failures may be corrected during the printing process or the operation can be aborted to save time and filament. Since the surface quality of 3D-printed objects may be related to some mechanical or physical properties of obtained objects, its fast and reliable evaluation may also be helpful during the quality monitoring procedures. The method presented in the paper utilizes the assumption of the increase of image entropy for irregularly distorted 3D-printed surfaces. Nevertheless, because of the local nature of distortions, the direct application of the global entropy does not lead to satisfactory results of automatic surface quality assessment. Therefore, the extended method, based on the combination of the local image entropy and its variance with additional colour adjustment, is proposed in the paper, leading to the proper classification of 78 samples used during the experimental verification of the proposed approach.

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“…There are yet other approaches providing spectral information about the process zone [24], but sacrificing temporal and spatial information. In optical coherence tomography (OCT), low-coherence interferometry allows to detect surface topologies during the process, which has been used to detect subsurface defects [6]. Fringe detection, on the other hand, has been shown to be suitable for detecting coating errors [27].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral imaging for prediction of surface roughness in laser powder bed fusion

Gerdes

Hoff

Hermsdorf

et al. 2021

Int J Adv Manuf Technol

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This article discusses the relevance of in situ quality assurance in metal additive manufacturing for cost-efficient product qualification. It presents an approach for monitoring the laser powder bed fusion (LPBF) process using an area-scan hyperspectral camera to predict the surface roughness Rz with the help of a convolutional neural network. These investigations were carried out during LPBF processing of the magnesium alloy WE43 that, due to its bioresorbability and compatibility, holds significant potential for biomedical implants. A data acquisition and processing methodology has been set up to enable efficient management of the hyperspectral data. The hyperspectral images obtained from the process were labeled with the surface roughness Rz as determined by a confocal microscope. The data was used to train a convolutional neural network whose hyperparameters were optimized in a hyperparameter tuning process. The resulting network was able to predict the surface roughness within a mean absolute error (MAE) of 4.1 μm over samples from three different parameter sets. Since this is significantly smaller than the spread of the actual roughness measured (MAE = 14.3 μm), it indicates that the network identified features in the hyperspectral data linking to the roughness. These results provide the basis for future research aiming to link hyperspectral process images to further part properties relevant for quality assurance.

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In situ process monitoring in selective laser sintering using optical coherence tomography

Cited by 52 publications

References 14 publications

Methods and procedure of referenced in situ control of lateral contour displacements in additive manufacturing

Methods and procedure of referenced in situ control of lateral contour displacements in additive manufacturing

Improved quality assessment of colour surfaces for additive manufacturing based on image entropy

Hyperspectral imaging for prediction of surface roughness in laser powder bed fusion

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