A Computational Fluid Dynamics (CFD) Study of Pneumatic Atomization in Aerosol Jet Printing (AJP) Process

2019 ◽  
Author(s):  
Roozbeh (Ross) Salary ◽  
Jack P. Lombardi ◽  
Darshana L. Weerawarne ◽  
Prahalad K. Rao ◽  
Mark D. Poliks
Keyword(s):  
Process Monitoring ◽  
Nonlinear Process ◽  
Cfd Model ◽  
Monitoring And Control ◽  
Loop Control ◽  
Manufacturing Method ◽  
Aerosol Generation ◽  
Process Monitoring And Control ◽  
Jet Printing ◽  
Aerosol Jet Printing

Abstract Aerosol jet printing (AJP) is a direct-write additive manufacturing method, which has been utilized particularly for the fabrication of flexible and hybrid electronics (FHE). In spite of the advantages of AJP — e.g., high-resolution material deposition on nonplanar surfaces and accommodation of a wide renege of ink viscosity — AJP inherently is a complex process, prone to nonlinear process changes. Consequently, real-time process monitoring and control (with an understanding of the physics behind aerosol generation and transport) are inevitable. The overarching goal of this work is to establish a physics-based framework for process monitoring and closed-loop control (for correction) in AJP. In pursuit of this goal, the objective is to forward a CFD model to explain the underlying physical phenomena behind aerosol nebulization in AJP. To realize this objective, a 3D compressible, turbulent multi-phase flow CFD model is forwarded. The geometry of the pneumatic atomizer is modeled based on X-ray computed tomography (CT) imaging. The boundary conditions of the problem are defined based on experimental observations. The outcome of this study paves the way for understanding the complex mechanisms of aerosol generation in AJP and also design of efficient atomizers.

A Computational Fluid Dynamics Investigation of Pneumatic Atomization, Aerosol Transport, and Deposition in Aerosol Jet Printing Process

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Roozbeh (Ross) Salary ◽  
Jack P. Lombardi ◽  
Darshana L. Weerawarne ◽  
Prahalada Rao ◽  
Mark D. Poliks
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
In Situ Monitoring ◽  
Cfd Model ◽  
Monitoring And Control ◽  
Aerosol Transport ◽  
Jet Printing ◽  
And Control ◽  
Aerosol Jet Printing

Abstract Aerosol jet printing (AJP) is a direct-write additive manufacturing technique, which has emerged as a high-resolution method for the fabrication of a broad spectrum of electronic devices. Despite the advantages and critical applications of AJP in the printed-electronics industry, AJP process is intrinsically unstable, complex, and prone to unexpected gradual drifts, which adversely affect the morphology and consequently the functional performance of a printed electronic device. Therefore, in situ process monitoring and control in AJP is an inevitable need. In this respect, in addition to experimental characterization of the AJP process, physical models would be required to explain the underlying aerodynamic phenomena in AJP. The goal of this research work is to establish a physics-based computational platform for prediction of aerosol flow regimes and ultimately, physics-driven control of the AJP process. In pursuit of this goal, the objective is to forward a three-dimensional (3D) compressible, turbulent, multiphase computational fluid dynamics (CFD) model to investigate the aerodynamics behind: (i) aerosol generation, (ii) aerosol transport, and (iii) aerosol deposition on a moving free surface in the AJP process. The complex geometries of the deposition head as well as the pneumatic atomizer were modeled in the ansys-fluent environment, based on patented designs in addition to accurate measurements, obtained from 3D X-ray micro-computed tomography (μ-CT) imaging. The entire volume of the constructed geometries was subsequently meshed using a mixture of smooth and soft quadrilateral elements, with consideration of layers of inflation to obtain an accurate solution near the walls. A combined approach, based on the density-based and pressure-based Navier–Stokes formation, was adopted to obtain steady-state solutions and to bring the conservation imbalances below a specified linearization tolerance (i.e., 10−6). Turbulence was modeled using the realizable k-ε viscous model with scalable wall functions. A coupled two-phase flow model was, in addition, set up to track a large number of injected particles. The boundary conditions of the CFD model were defined based on experimental sensor data, recorded from the AJP control system. The accuracy of the model was validated using a factorial experiment, composed of AJ-deposition of a silver nanoparticle ink on a polyimide substrate. The outcomes of this study pave the way for the implementation of physics-driven in situ monitoring and control of AJP.

A State-of-the-Art Review on Aerosol Jet Printing (AJP) Additive Manufacturing Process

2019 ◽  
Author(s):  
Roozbeh Ross Salary ◽  
Jack P. Lombardi ◽  
Darshana L. Weerawarne ◽  
Prahalad K. Rao ◽  
Mark D. Poliks
Keyword(s):  
Additive Manufacturing ◽  
Flexible Electronics ◽  
Manufacturing Process ◽  
Device Fabrication ◽  
Synthesis Process ◽  
Monitoring And Control ◽  
Loop Control ◽  
Wide Range ◽  
Jet Printing ◽  
Aerosol Jet Printing

Abstract The goal of this work is to forward a comprehensive framework, relating to the most recent research works carried out in the area of flexible and hybrid electronics (FHE) fabrication with the aid of aerosol jet printing (AJP) additive manufacturing process. In pursuit of this goal, the objective is to review and classify a wide range of articles, published recently, concerning various aspects of AJP-based device fabrication, such as material synthesis, process monitoring, and control. AJP has recently emerged as the technique of choice for integration as well as fabrication of a broad spectrum of electronic components and devices, e.g., interconnects, sensors, transistors, optical waveguides, quantum dot arrays, photodetectors, and circuits. This is preeminently because of advantages engendered by AJP process. AJP not only allows for high-resolution deposition of microstructures, but also accommodates a wide renege of ink viscosity. However, AJP is intrinsically complex and prone to gradual drifts of the process output (stemming from ink chemistry and formulation). Consequently, a large number of research works in the literature has focused on in situ process characterization, real-time monitoring, and closed-loop control with the aim to make AJP a rapid, reliable, and robust additive manufacturing method for the manufacture of flexible and hybrid electronic devices. It is expected that the market for flexible electronics will be worth over $50 billion by 2020 [1].

Fractal Pattern Recognition of Image Profiles for Manufacturing Process Monitoring and Control

2018 ◽  
Author(s):  
Farhad Imani ◽  
Bing Yao ◽  
Ruimin Chen ◽  
Prahalada Rao ◽  
Hui Yang
Keyword(s):  
Additive Manufacturing ◽  
Process Monitoring ◽  
Manufacturing Process ◽  
Manufacturing Systems ◽  
Experimental Studies ◽  
Precision Machining ◽  
Monitoring And Control ◽  
Process Monitoring And Control ◽  
Ultra Precision ◽  
And Control

Nowadays manufacturing industry faces increasing demands to customize products according to personal needs. This trend leads to a proliferation of complex product designs. To cope with this complexity, manufacturing systems are equipped with advanced sensing capabilities. However, traditional statistical process control methods are not concerned with the stream of in-process imaging data. Also, very little has been done to investigate nonlinearity, irregularity, and inhomogeneity in image stream collected from manufacturing processes. This paper presents the multifractal spectrum and lacunarity measures to characterize irregular and inhomogeneous patterns of image profiles, as well as detect the hidden dynamics of the underlying manufacturing process. Experimental studies show that the proposed method not only effectively characterizes the surface finishes for quality control of ultra-precision machining but also provides an effective model to link process parameters with fractal characteristics of in-process images acquired from additive manufacturing. This, in turn, will allow a swift response to processes changes and consequently reduce the number of defective products. The proposed fractal method has strong potentials to be applied for process monitoring and control in a variety of domains such as ultra-precision machining, additive manufacturing, and biomanufacturing.

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