Computational fluid dynamics modeling and in situ physics-based monitoring of aerosol jet printing toward functional assurance of additively-manufactured, flexible and hybrid electronics

<p>Aerosol jet printing (AJP)—a direct-write, additive manufacturing technique—has emerged as the process of choice particularly for the fabrication of flexible and hybrid electronics. AJP has paved the way for high-resolution device fabrication with high placement accuracy, edge definition, and adhesion. In addition, AJP accommodates a broad range of ink viscosity, and allows for printing on non-planer surfaces. Despite the unique advantages and host of strategic applications, AJP is a highly unstable and complex process, prone to gradual drifts in machine behavior and deposited material. Hence, real-time monitoring and control of AJP process is a burgeoning need. In pursuit of this goal, the objectives of the work are, as follows: (i) <em>In situ </em>image acquisition from the traces/lines of printed electronic devices right after deposition. To realize this objective, the AJP experimental setup was instrumented with a high-resolution charge-coupled device (CCD) camera, mounted on a variable-magnification lens (in addition to the standard imaging system, already installed on the AJ printer). (ii) <em>In situ </em>image processing and quantification of the trace morphology. In this regard, several customized image processing algorithms were devised to quantify/extract various aspects of the trace morphology from online images. In addition, based on the concept of shape-from-shading (SfS), several other algorithms were introduced, allowing for not only reconstruction of the 3D profile of the AJ-printed electronic traces, but also quantification of 3D morphology traits, such as thickness, cross-sectional area, and surface roughness, among others. (iii) Development of a supervised multiple-input, single-output (MISO) machine learning model—based on sparse representation for classification (SRC)—with the aim to estimate the device functional properties (e.g., resistance) in near real-time with an accuracy of ≥ 90%. (iv) Forwarding a computational fluid dynamics (CFD) model to explain the underlying aerodynamic phenomena behind aerosol transport and deposition in AJP process, observed experimentally.</p> <p>Overall, this doctoral dissertation paves the way for: (i) implementation of physics-based real-time monitoring and control of AJP process toward conformal material deposition and device fabrication; and (ii) optimal design of direct-write components, such as nozzles, deposition heads, virtual impactors, atomizers, etc.</p>

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Systems Science and Industrial Engineering

Year

2018

Author dc:creator

• Salary, Roozbeh (Ross)

Contributors dc:contributor

• Dr. Mark D. Poliks

• Applied sciences
• Advanced/additive manufacturing
• Aerosol jet printing (AJP)
• Computational fluid dynamics (CFD)
• Flexible and hybrid electronics
• Physics-based monitoring and control
• Operations Research, Systems Engineering and Industrial Engineering

Repository record dc:identifier

https://orb.binghamton.edu/dissertation_and_theses/80

OAI identifier oai:identifier

oai:orb.binghamton.edu:dissertation_and_theses-1085