Development of an error compensation case study for 3D printers

Majarena, Ana Cristina; Aguilar, Juan José; Santolaria, Jorge

doi:10.1016/j.promfg.2017.09.145

Cited by 9 publications

(13 citation statements)

References 1 publication

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“…Image distortion could be evaluated using calibrated grid distortion targets [22,29]. These targets usually consist of equally spaced dots grid, although some target designs could consist of concentric squares or even crossed lines.…”

Section: Materials and Equipmentmentioning

confidence: 99%

“…This method has been used to measure dimensional features of a flat part with good results. A variation of this method has also been used to compensate flat 3D printed part contours in order to improve the quality of parts manufactured thereafter [29]. Although this method contemplates linear deformations, other authors provided calibration models based on higher order polynomials [30].A literature review shows that, although commercial flatbed scanners could be a suitable option for two-dimensional verification, they provide digitized images with significant distortion issues.…”

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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: 99%

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 use of a coordinate measurement machine is proposed to describe the accuracy of medical models in [18,19]) and the optical scanning is mentioned in [6]). A metrology feedback procedure is used in [20,21] to improve the geometrical accuracy by errors compensation using a 3D scanning on sacrificial printed objects.…”

Section: Introductionmentioning

confidence: 99%

A Study on the Errors of 2D Circular Trajectories Generated on a 3D Printer

et al. 2021

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This paper presents a study on the movement precision and accuracy of an extruder system related to the print bed on a 3D printer evaluated using the features of 2D circular trajectories generated by simultaneous displacement on x and y-axes. A computer-assisted experimental setup allows the sampling of displacement evolutions, measured with two non-contact optical sensors. Some processing procedures of the displacement signals are proposed in order to evaluate and to describe the circular trajectories errors (e.g., open and closed curves fitting, the detection of recurrent periodical patterns in x and y-motions, low pass numerical filtering, etc.). The description of these errors is suitable to certify that the 3D printer works correctly (keeping the characteristics declared by the manufacturer) for maintenance purpose sand, especially, for computer-aided correction of accuracy (e.g., by error compensation).

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“…The works by De Vicente [16] and Majarena [17] have provided a method for adjusting global deformation errors in FBS. Their approach improved the reliability of digital image-based analysis of part contours to a point that even manufacturing errors can be characterised and partially compensated.…”

Section: Introductionmentioning

confidence: 99%

“…This vector reflects the magnitude of the gradient at a given point G(x,y) and its angle with respect to the coordinate system of reference [10,11,16,18], both filtering and edge detection stages as well as their correspondent configurations are directly given, and no discussion about their possible influence on the results is provided. Even more, some works rely on manual measurement of the characteristics in the image, so they did not really use image processing or automatic contour detection algorithms [12][13][14][15]17]. Nevertheless, edge detection is a relevant part of every automated measurement strategy, and the effect of image processing configuration should not be neglected.…”

Section: Introductionmentioning

confidence: 99%

The Influence of Image Processing and Layer-to-Background Contrast on the Reliability of Flatbed Scanner-Based Characterisation of Additively Manufactured Layer Contours

Fernández¹,

Álvarez²,

Suárez³

et al. 2020

Applied Sciences

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Flatbed scanners (FBSs) provide non-contact scanning capabilities that could be used for the on-machine verification of layer contours in additive manufacturing (AM) processes. Layer-wise contour deviation assessment could be critical for dimensional and geometrical quality improvement of AM parts, because it would allow for close-loop error compensation strategies. Nevertheless, contour characterisation feasibility faces many challenges, such as image distortion compensation or edge detection quality. The present work evaluates the influence of image processing and layer-to-background contrast characteristics upon contour reconstruction quality, under a metrological perspective. Considered factors include noise filtering, edge detection algorithms, and threshold levels, whereas the distance between the target layer and the background is used to generate different contrast scenarios. Completeness of contour reconstruction is evaluated by means of a coverage factor, whereas its accuracy is determined by comparison with a reference contour digitised in a coordinate measuring machine. Results show that a reliable contour characterisation can be achieved by means of a precise adjustment of image processing parameters under low layer-to-background contrast variability. Conversely, under anisotropic contrast conditions, the quality of contour reconstruction severely drops, and the compromise between coverage and accuracy becomes unbalanced. These findings indicate that FBS-based characterisation of AM layers will demand developing strategies that minimise the influence of anisotropy in layer-to-background contrast.

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Development of an error compensation case study for 3D printers

Cited by 9 publications

References 1 publication

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

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

A Study on the Errors of 2D Circular Trajectories Generated on a 3D Printer

The Influence of Image Processing and Layer-to-Background Contrast on the Reliability of Flatbed Scanner-Based Characterisation of Additively Manufactured Layer Contours

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