Dimensional accuracy improvement of FDM square cross-section parts using artificial neural networks and an optimization algorithm

Noriega, Á.; Blanco, David; Álvarez, Braulio José Álvarez; García, A.

doi:10.1007/s00170-013-5196-2

Cited by 113 publications

(52 citation statements)

References 15 publications

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“…[5] Machine learning has been successfully applied in applications such as image processing, text classification, and speech recognition. [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] An example of the utility of machine learning in the established quality control method of visual inspection is demonstrated by the use of a neural network to identify flaws in laser powder bed fusion 3D printing. [8] Examples of their use in both monitoring/feedback applications and predictive models include predicting property outcomes based on parameter settings, predicting global parameter settings for specific outcomes, identifying failures during printing, predicting bead geometry, adjusting geometry to prevent failures, and assessing part manufacturability.…”

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confidence: 99%

“…[8] Examples of their use in both monitoring/feedback applications and predictive models include predicting property outcomes based on parameter settings, predicting global parameter settings for specific outcomes, identifying failures during printing, predicting bead geometry, adjusting geometry to prevent failures, and assessing part manufacturability. [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] An example of the utility of machine learning in the established quality control method of visual inspection is demonstrated by the use of a neural network to identify flaws in laser powder bed fusion 3D printing. [28,29] An Quality control and repeatability of 3D printing must be enhanced to fully unlock its utility beyond prototyping and noncritical applications.…”

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confidence: 99%

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Machines as Craftsmen: Localized Parameter Setting Optimization for Fused Filament Fabrication 3D Printing

Gardner

Hunt

Ebel

et al. 2019

Adv Materials Technologies

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FFF is known to have issues with part quality and consistency, which has limited its use to prototyping and noncritical applications where functional reliability does not affect safety. [2] The occurrence of issues such as poor surface finish, layer delamination, and poor dimensional stability depends on a number of parameter settings, including nozzle temperature, print speed, environmental conditions, geometry, and location (Figure 1a,b). [3] Experienced operators must set these parameters according to the material, part geometry, and 3D printer. It can be difficult for even an expert to select optimum parameter settings (Figure 1c,d). [4] An operator can specify settings for over 100 different printing options using a slicing software such as Cura. Attempting all combinations is not practical, so an operator must rely on their knowledge of 3D printing to adjust parameter settings. This leads to operator-dependent part-to-part variations and uncertainty in the performance of the final part. Additionally, an operator will typically select a set of global parameter settings that are used throughout the part, or at most make some layer-to-layer adjustments. A more objective solution for selecting parameter settings is needed to ensure optimal use of the printer/material performance space and consistency in 3D-printed parts, regardless of part geometry, material, system, or operator.Machine learning offers a potential approach to addressing this problem. Machine learning models are trained on thousands to millions of data points to recognize patterns that are too difficult to identify using deterministic algorithms. [5] Machine learning has been successfully applied in applications such as image processing, text classification, and speech recognition. [5][6][7] Potential uses for machine learning in 3D printing have also been studied in a limited capacity. [8] Examples of their use in both monitoring/feedback applications and predictive models include predicting property outcomes based on parameter settings, predicting global parameter settings for specific outcomes, identifying failures during printing, predicting bead geometry, adjusting geometry to prevent failures, and assessing part manufacturability. [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] An example of the utility of machine learning in the established quality control method of visual inspection is demonstrated by the use of a neural network to identify flaws in laser powder bed fusion 3D printing. [28,29] An Quality control and repeatability of 3D printing must be enhanced to fully unlock its utility beyond prototyping and noncritical applications. Machine learning is a potential solution to improving 3D printing performance and is explored for areas including flaw identification and property prediction. However, critical problems must be resolved before machine learning can truly enable 3D printing to reach its potential, including the very large data sets required for training and the inherently local nature of 3D print...

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confidence: 99%

mentioning

confidence: 99%

Machines as Craftsmen: Localized Parameter Setting Optimization for Fused Filament Fabrication 3D Printing

Gardner

Hunt

Ebel

et al. 2019

Adv Materials Technologies

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“…Stratasys was one of the first companies to develop the FDM process and FDM machine. The following is a list of commonly used FDM machines: Fortus FDM 400 mc machine (Stratasys, Eden Prairie, MN, USA) [10,21,22], Fortus 360 mc machine (Stratasys, Eden Prairie, MN, USA) [23], FDM 200 mc machine (Stratasys, Eden Prairie, MN, USA) [24], FDM 1650 machine (Stratasys, Eden Prairie, MN, USA) [25][26][27][28], FDM Vantage machine (Stratasys, Eden Prairie, MN, USA) [29,30], Ultimaker 2 (Ultimaker, Amsterdam, Netherlands) [23,31,32], MakerBot Replicator 2 (MakerBot, New York, NY, USA) [33,34], MEM-300 machine (Beijing Yinhua Laser Rapid Prototypes Making and Mould Technology Co., Ltd, Beijing, China) [35], Uprint SE 3D printer (Stratasys, Eden Prairie, MN, USA) [5], The FDM 3000 3D printer (Stratasys, Eden Prairie, MN, USA) [36,37], Raise3D N2plus machine (Raise3D, Irvine, CA, USA) [38], Makerbot Replicator 2X (MakerBot, New York, NY, USA) [1,3,20], Julia 3D printer (Fracktal Works, Bangalore, India) [7], FDM Maxum Machine (Stratasys, Eden Prairie, MN, USA) [39], Prodigy Plus (Stratasys, Eden Prairie, MN, USA) [6,40], WitBox desktop 3D printer (BQ, Madrid, Spain) [11], and HP Designjet 3D CQ656A (HP, Palo Alto, CA, USA) [41].…”

Section: Fdm Equipmentmentioning

confidence: 99%

A Systematic Survey of FDM Process Parameter Optimization and Their Influence on Part Characteristics

Dey

Yodo

2019

JMMP

335

257

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Fused deposition modeling (FDM) is an additive manufacturing (AM) process that is often used to fabricate geometrically complex shaped prototypes and parts. It is gaining popularity as it reduces cycle time for product development without the need for expensive tools. However, the commercialization of FDM technology in various industrial applications is currently limited due to several shortcomings, such as insufficient mechanical properties, poor surface quality, and low dimensional accuracy. The qualities of FDM-produced products are affected by various process parameters, for example, layer thickness, build orientation, raster width, or print speed. The setting of process parameters and their range depends on the section of FDM machines. Filament materials, nozzle dimensions, and the type of machine determine the range of various parameters. The optimum setting of parameters is deemed to improve the qualities of three-dimensional (3D) printed parts and may reduce post-production work. This paper intensively reviews state-of-the-art literature on the influence of parameters on part qualities and the existing work on process parameter optimization. Additionally, the shortcomings of existing works are identified, challenges and opportunities to work in this field are evaluated, and directions for future research in this field are suggested.

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“…Generalized industrial adoption of AM has to face several challenges, like broadening the range of available materials, increasing production batch size or improving part quality [3]. Although part quality is a broad concept that encompasses aspects such as physical properties or durability, dimensional and geometrical accuracy of AM parts have always stood out among researchers' main concerns during the last decade [4][5][6][7][8]. These works analyzed the lack of dimensional or geometric quality in three-dimensional features in final parts, which enabled optimization of process configuration, or even for the modification of part design, so that an improvement in quality could be achieved in the…”

Section: Introductionmentioning

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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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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Dimensional accuracy improvement of FDM square cross-section parts using artificial neural networks and an optimization algorithm

Cited by 113 publications

References 15 publications

Machines as Craftsmen: Localized Parameter Setting Optimization for Fused Filament Fabrication 3D Printing

Machines as Craftsmen: Localized Parameter Setting Optimization for Fused Filament Fabrication 3D Printing

A Systematic Survey of FDM Process Parameter Optimization and Their Influence on Part Characteristics

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

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