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Research Papers

Computational Fluid Dynamics Modeling and Online Monitoring of Aerosol Jet Printing Process

[+] Author and Article Information
Roozbeh (Ross) Salary, Jack P. Lombardi, M. Samie Tootooni, Ryan Donovan, Peter Borgesen, Mark D. Poliks

Department of System Science and
Industrial Engineering (SSIE),
Binghamton University,
State University of New York,
Binghamton, NY 13902

Prahalad K. Rao

Department of Mechanical and
Materials Engineering (MME),
University of Nebraska-Lincoln,
Lincoln, NE 68588-0526
e-mails: rao@unl.edu; prao@binghamton.edu

1Corresponding author.

Manuscript received April 21, 2016; final manuscript received August 10, 2016; published online October 3, 2016. Assoc. Editor: Donggang Yao.

J. Manuf. Sci. Eng 139(2), 021015 (Oct 03, 2016) (21 pages) Paper No: MANU-16-1239; doi: 10.1115/1.4034591 History: Received April 21, 2016; Revised August 10, 2016

The objectives of this paper in the context of aerosol jet printing (AJP)—an additive manufacturing (AM) process—are to: (1) realize in situ online monitoring of print quality in terms of line/electronic trace morphology; and (2) explain the causal aerodynamic interactions that govern line morphology based on a two-dimensional computational fluid dynamics (2D-CFD) model. To realize these objectives, an Optomec AJ-300 aerosol jet printer was instrumented with a charge coupled device (CCD) camera mounted coaxial to the nozzle (perpendicular to the platen). Experiments were conducted by varying two process parameters, namely, sheath gas flow rate (ShGFR) and carrier gas flow rate (CGFR). The morphology of the deposited lines was captured from the online CCD images. Subsequently, using a novel digital image processing method proposed in this study, six line morphology attributes were quantified. The quantified line morphology attributes are: (1) line width, (2) line density, (3) line edge quality/smoothness, (4) overspray (OS), (5) line discontinuity, and (6) internal connectivity. The experimentally observed line morphology trends as a function of ShGFR and CGFR were verified with computational fluid dynamics (CFD) simulations. The image-based line morphology quantifiers proposed in this work can be used for online detection of incipient process drifts, while the CFD model is valuable to ascertain the appropriate corrective action to bring the process back in control in case of a drift.

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References

Figures

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Fig. 1

(a)–(c) Offline images of AJP-printed electronic traces captured by an optical microscope (Carl Zeiss M1M); (d)–(f) online images captured by a high-resolution CCD color camera installed on our experimental setup

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Fig. 2

(a)–(c) Offline 3D profilometry images showing the line thickness profile taken with an optical profilometer (Wyko NT-1100)

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Fig. 3

Electronic devices and structures AJP-printed at the authors' facilities in Binghamton University. (a) An antenna printed on a flexible glass substrate; (b) reduced graphene oxide (rGO) supercapacitors (SCs) and silver interdigitated electrodes (IDEs) printed on a slim glass substrate; (c) silver test lines printed on a polymer substrate; and (d) silver interdigitated electrodes (IDEs) printed on polyimide.

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Fig. 4

A list of material, machine, and process factors influencing the morphology and functional integrity of an AJP-printed line

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Fig. 5

Different situations where line morphology deviates (drifts) from the target, stemming from complex materials, process, and machine interactions. (The images have been inverted and thresholded to binary equivalents.)

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Fig. 6

(a) and (b) Pictures and (c) a schematic diagram of the experiential setup showing the imaging components installed on the Optomec AJ-300 system

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Fig. 7

(a) The main components of the deposition head. (b) The deposition head assembly. A cross-sectional view of the deposition head and (c) obtained using X-ray computed tomography.

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Fig. 8

The Reynolds number as a function of ShGFR showing that the internal flow remains laminar both in the combination chamber and at the nozzle exit. A laminar viscous model was chosen as a result.

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Fig. 9

A free body diagram showing the forces acting on a particle in a shear flow. u is the carrier flow velocity vector, v is the particle velocity vector, and ωd is the particle rotation vector.

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Fig. 10

The four types of boundary defined for each zone of the problem modeled in the ansys-fluent environment

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Fig. 11

(a) The zones of background (BG), overspray (OS), and line width (LW) detected based on two spatial threshold parameters (obtained using a threshold estimator, proposed in this study). (b) The one-dimensionalized intensity profile of the image shown in part (a). (c) The first derivative of the intensity profile indicating the approximate range of each zone based on which the threshold parameters (as reference intensities) as well as the coordinates of the threshold lines (shown in Fig. 12) are determined.

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Fig. 12

Visual representation of the morphology quantifiers proposed in this study. Line width (LW): the average distance between the upper and lower edges (shown as two solid lines); line density (Lρ): the average intensity of all pixels constituting the line; edge quality (LEQ): the inverse, average distance between each edge and its corresponding threshold line (shown as two dashed lines); overspray (LOS): The weighted, average distance between each overspray pixel and its corresponding line edge multiplied by the intensity; line discontinuity (LDisc): the average number of failures in the edge detection. The image is read from top to bottom.

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Fig. 13

The effect of the sheath gas flow rate (ShGFR) on line morphology. The carrier gas flow rate (CGFR) and print speed (Ps) were fixed at 30 sccm and 1 mm/s, respectively.

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Fig. 14

The line morphology features captured using the six quantifiers proposed in this study. The error bars are (±1 σ/n) long where n equals the number of replications (10). The secondary abscissa tracks the corresponding sheath gas flow pressure (ShGFP).

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Fig. 15

A comparison between the online and offline experimental results versus ShGFR, signifying the consistency between the two methods. The error bars are (±1 σ/n) long where n equals the number of replications (10). Capturing the same trend for each morphology attribute, the online and offline quantifiers were plotted on two axes to offset the difference arising from different image properties.

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Fig. 16

The changes in line morphology as a function of ShGFR, employing the pneumatic atomizer. The lines are of Paru PG-007 Ag ink printed on a Ube UPILEX-75 S polyimide film.

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Fig. 17

A comparison between the ultrasonic and pneumatic experimental results of line morphology versus ShGFR, corroborating the consistency of trends between the two atomization techniques. The error bars are (±1 σ/n) long where n (=10) equals the number of replications.

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Fig. 18

(a) The influence of ShGFR on line width (left axis) and on the aerosol jet width (right axis); (b) the influence of ShGFR on the carrier flow deposition spread (right axis) and on the internal connectivity of the lines, represented by Fiedler number (left axis). (c) Plot of the Reynolds number versus ShGFR indicating that the aerosol flow in the combination chamber becomes hydrodynamically instable when the ShGFR ≥ 100 sccm (i.e., Re ∼ 950) [60]. The error bars are (±1 σ/n) long where n equals the number of replications (10). An empirical geometry factor of 6 was used to scale the 2D-CFD simulation to the 3D experimental data.

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Fig. 19

The influence of increasing ShGFR on the flow velocity profile and on the particle trajectory (a) and (b): in the combination chamber and (c) and (d): during the deposition

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Fig. 20

The influence of ShGFR on the flow pressure profile as well as on the trajectory of particles in the combination chamber. The ShGFR of 80 sccm seems to be the onset of pressure buildup in the chamber. Hence, the maximum pressure limit can be considered approximately 622 Pa. In this simulation, the carrier gas flow rate (CGFR) was set at 30 sccm.

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Fig. 21

(a)–(f) The effect of the carrier gas flow rate (CGFR) on line morphology, both the sheath gas flow rate (ShGFR) and print speed (PS) were fixed at 60 sccm and 1 mm/s, respectively

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Fig. 22

The main features of the line morphology as a function of the CGFR captured using the quantifiers developed in this study. The error bars are (±1 σ/n) long where n equals the number of replications (10). The secondary abscissa tracks the corresponding carrier gas flow pressure (CGFP, Pa).

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Fig. 23

A comparison between the online and offline experimental results versus the CGFR, signifying the consistency between the two methods. The error bars are (±1 σ/n) long where n equals the number of replications (10). Capturing the same trend for each morphology attribute, the online and offline quantifiers were plotted on two axes to offset the difference arising from different image properties.

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Fig. 24

The influence of the CGFR on the line width (left axis) and on the aerosol jet width (right axis). The error bars are (±1 σ/n) long where n equals the number of replications (10). The objective was to show the model could capture the same trend as the experiment; a geometry factor of 6 was used for scaling the CFD simulation results to the experimental observations.

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Fig. 25

The influence of increasing CGFR on the flow velocity profile and on the particle trajectory (a) and (b): in the combination chamber and (c) and (d) during the deposition process. In this simulation, the sheath gas flow rate (ShGFR) was set at 60 sccm.

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Fig. 26

The proposed stereomicroscopy-based approach, which will be used to quantify line thickness in our future work. (a) A picture of the experimental setup equipped with a stereomicroscope; (b) and (c) the left and right views of a printed line, respectively. The line thickness is quantified based on the two perspective views.

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Fig. 27

The influence of print speed (PS) on line morphology. The edge quality and line width are more significantly affected than the other morphology features (developed in Sec. 4).

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Fig. 28

The line morphology features captured using the quantifiers developed in this study. The error bars are (±1 σ/n) long where n equals the number of replications (n = 10).

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