A high-resolution and large field-of-view scanner for in-line characterization of powder bed defects during additive manufacturing

Le, Tan-Phuc; Seita, Matteo

doi:10.1016/j.matdes.2018.107562

Cited by 69 publications

(20 citation statements)

References 24 publications

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“…This effect could be better understood through the graphs in Figure 1, where comparisons between 8-bit greyscale file sizes and scanning times of a 110 mm × 110 mm area at different resolutions are provided. Sensors 2020, 20 As can be observed in Figure 1b, scanning time abruptly increases for resolutions over 2400 dpi, lasting 5 min approximately for 4800 dpi resolution scan. A similar trend can be observed for correspondent files size.…”

Section: Materials and Equipmentmentioning

confidence: 76%

“…Examples of the use of flatbed scanners can be found in the field of biomedical imaging [17], materials science [18], astronomy [19] or agriculture [15]. Also in the field of AM, recent works had explored the possibilities of characterizing power defects in power bed fusion processes analyzing digital images captured with a flatbed scanner [20]. In the aforementioned research, attention was paid to the presence of unevenness on the powder surface revealed by out-of-focus regions in the acquired image.…”

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confidence: 99%

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Layer Contour Verification in Additive Manufacturing by Means of Commercial Flatbed Scanners

Blanco

Fernández

Noriega

et al. 2019

Sensors

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Industrial adoption of additive manufacturing (AM) processes demands improvement in the geometrical accuracy of manufactured parts. One key achievement would be to ensure that manufactured layer contours match the correspondent theoretical profiles, which would require integration of on-machine measurement devices capable of digitizing individual layers. Flatbed scanners should be considered as serious candidates, since they can achieve high scanning speeds at low prices. Nevertheless, image deformation phenomena reduce their suitability as two-dimensional verification devices. In this work, the possibilities of using flatbed scanners for AM contour verification are investigated. Image distortion errors are characterized and discussed and special attention is paid to the plication effect caused by contact imaging sensor (CIS) scanners. To compensate this phenomena, a new local distortion adjustment (LDA) method is proposed and its distortion correction capabilities are evaluated upon actual layer contours manufactured on a fused filament fabrication (FFF) machine. This proposed method is also compared to conventional global distortion adjustment (GDA). Results reveal quasi-systematic deformations of the images which could be minimized by means of distortion correction. Nevertheless, the irregular nature of such a distortion and the superposition of different errors penalize the use of GDA, to the point that it should not be used with CIS scanners. Conclusions indicate that LDA-based correction would enable the use of flatbed scanners in AM for on-machine verification tasks.Sensors 2020, 20, 1 2 of 24 following parts. Although this is the usual approach for quality assurance in AM it does not permit adoption of close-loop in-process improvements, and implies that optimization could not be done without previously manufacturing test specimens.Nevertheless, regarding the dimensional quality of manufactured parts, one key achievement would be to build layer contours that accurately follow theoretical profiles. This possibility would require on-machine integration of measurement devices to evaluate in-layer quality. The use of sensors capable of verifying each individual layer and checking the accuracy in contour tracing would allow for correction of the next layers. They would also make unnecessary previous testing of each particular geometry. Therefore, real-time, in-line metrology is commonly reported to be among the main challenges for AM development [9]. Contour verification could be carried out by means of different technologies, like structured light [7], conoscopic holography [10], coordinate measuring machine (CMM) optical probes [11] or ad hoc Charge-Couple Device (CCD) based instrumentation [12,13]. Nevertheless, computer vision based on flatbed scanner images should be considered as a serious candidate, since it can meet high scanning speeds at low prices.Flatbed scanners are optical devices commonly used to capture digital images of flat elements, such as sheets of paper or photographs. Nevertheless, image...

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Section: Materials and Equipmentmentioning

confidence: 76%

mentioning

confidence: 99%

Layer Contour Verification in Additive Manufacturing by Means of Commercial Flatbed Scanners

Blanco

Fernández

Noriega

et al. 2019

Sensors

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“…The choice of the Wi-Fi microscope was justified from the possibility to investigate the powder bed in situ and without presumably altering the bed itself. The so obtained images were then processed via an image analysis software (ImageJ) following an approach similar to that of Than Puc et al [11] the out area out of focus (and hence out of the intended deposition plane) was identified using an edge detection algorithm and quantified. Fig.…”

Section: La Lay Yer Morphology Er Morphologymentioning

confidence: 99%

Spreading of Powders in Powder Bed Additive Manufacturing: an Experimental Approach

Lampitella¹,

Astarita²,

D’Avino³

2021

Esaform 2021

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Powder bed additive manufacturing allows for the production of fully customizable parts and is of great interest for industrial applications. However, the repeatability of the parts and the uniformity of the mechanical properties are still an issue. More specifically, the physical mechanism of the spreading process of the powders, which significantly affects the characteristics of the final part, is not completely understood. In powder bed fusion technologies, the spreading is performed by a device, typically a roller or a blade, that collects the powders from the feedstock and successively deposits them in a layer of several dozens of microns that is then processed with a laser beam. In this work, an experimental approach is developed and employed to study the powder spreading process and analyze in detail the motion of the powders from the accumulation zone to the deposition stage. The presented experiments are carried out on a home-made device that reproduces the spreading process and enables the measurement of the characteristics of the powder bed. Furthermore, the correlation with the process parameters, e.g., the speed of the spreading device, is also investigated. These results can be used to obtain useful insights on the optimal window for the process parameters.

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“…These ML-based faultdetection techniques, in general, involve a data collection step, data mapping, feature extraction, fault labeling, and finally model creation/evaluation. Use of image filters, either to extract features or clean the data [7][8][9][10][11] is also a common practice in the realm of image processing [12]. Many works, including the present paper, use CT scans to examine the ex-situ quality of a part [7,8,13].…”

Section: Introductionmentioning

confidence: 99%

“…As can be seen, several groups have approached the problem of applying machine learning and image processing to additive manufacturing processes [7][8][9][10][11]. The present work is novel in that it examines the powder bed surface, specifically after sintering but before a new layer was deposited.…”

Section: Introductionmentioning

confidence: 99%

Active Monitoring of Selective Laser Melting Process by Training an Artificial Neural Net Classifier on Layer-By-Layer Surface Laser Profilometry Data

Terry

Baucher

Chaudhary

et al. 2021

Preprint

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This paper reports some recent results related to active monitoring of Selective Laser Melting (SLM) processes through analysis of layer-by-layer surface profile data. Estimation of fault probability was carried out experimentally in a Renishaw AM250 machine, by collecting Fe3Si powder bed height data, in-situ, during the metal additive manufacturing of a Heat Exchanger section, comprised of a series of conformal channels. Specifically, high-resolution powder bed surface height data from a laser profilometer was linked to post-print ground-truth labels (faulty or nominal) for each site from CT scans, by training a shallow artificial neural net (ANN). The ANN demonstrated interesting capabilities for discovering correlations between surface roughness characteristics and the presence and size of faults. Strong performance was achieved with respect to several standard metrics for classifying faulty and nominal sites. These developments can potentially enable active monitoring processes to become a future component of a layer-by-layer feedback system for better control of SLM processes.

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A high-resolution and large field-of-view scanner for in-line characterization of powder bed defects during additive manufacturing

Cited by 69 publications

References 24 publications

Layer Contour Verification in Additive Manufacturing by Means of Commercial Flatbed Scanners

Layer Contour Verification in Additive Manufacturing by Means of Commercial Flatbed Scanners

Spreading of Powders in Powder Bed Additive Manufacturing: an Experimental Approach

Active Monitoring of Selective Laser Melting Process by Training an Artificial Neural Net Classifier on Layer-By-Layer Surface Laser Profilometry Data

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