자료유형  

 학위논문

Control Number  

0016932900

International Standard Book Number  

9798379840662

Dewey Decimal Classification Number  

600

Main Entry-Personal Name  

Wu, Yumeng.

Publication, Distribution, etc. (Imprint  

[S.l.] : Purdue University., 2022

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2022

Physical Description  

1 online resource(117 p.)

General Note  

Source: Dissertations Abstracts International, Volume: 85-01, Section: A.

General Note  

Advisor: Chiu, George T.-C.

Dissertation Note  

Thesis (Ph.D.)--Purdue University, 2022.

Restrictions on Access Note  

This item must not be sold to any third party vendors.

Summary, Etc.  

요약Among all additive manufacturing processes, material jetting, or inkjet 3D printing, builds the product similar to the traditional inkjet printing, either by drop-on-demand or continuous printing. Aside from the common advantages as other additive manufacturing methods, it can achieve higher resolution than other additive manufacturing methods. Combining its ability to accept a wide range of functional inks, inkjet 3D printing is predominantly used in pharmaceutical and biomedical applications. A height profile model is necessary to achieve better estimation of the geometry of a printed product. Numerical height profile models have been documented that can estimate the inkjet printing process from when the droplet hits the substrate till fully cured. Although they can estimate height profiles relatively accurately, these models generally take a long time to compute. A simplified model that can achieve sufficient accuracy while reducing computational complexity is needed for real-time process control.In this work, a layer-to-layer height propagation model that aims to balance computational complexity and model accuracy is proposed and experimentally validated. The model consists of two sub-models where one is dedicated to multi-layer line printing and the other is more broadly applicable for multi-layer 2D patterns. Both models predict the height profile of drops through separate volume and area layer-to-layer propagation. The layer-to-layer propagation is based on material flow and volume conservation. The models are experimentally validated on an experimental inkjet 3D printing system equipped with a heated piezoelectric dispenser head made by Microdrop.There are notable similarities between inkjet 3D printing and inkjet image printing, which has been studied extensively to improve color printing quality. Image processing techniques are necessary to convert nearly continuous levels of color intensities to binary printing map while satisfying the human visual system at the same time. It is reasonable to leverage such image processing techniques to improve the quality of inkjet 3D printed products, which might be more effective and efficient. A framework is proposed to adapt image processing techniques for inkjet 3D printing. Standard error diffusion method is chosen as a demonstration of the framework to be adapted for inkjet 3D printing and this adaption is experimentally validated. The adapted error diffusion method can improve the printing quality in terms of geometry integrity with low demand on computation power.Model predictive control has been widely used for process control in various industries. With a carefully designed cost function, model predictive control can be an effective tool to improve inkjet 3D printing. While many researchers utilized model predictive control to indirectly improves functional side of the printed products, geometry control is often overlooked. This is possibly due to the lack of high quality height profile models for inkjet 3D printing for real-time control. Height profile control of inkjet 3D printing can be formulated as a constrained non-linear model predictive control problem. The input to the printing system is always constrained, as droplet volume not only is bounded but also cannot be continuously adjusted due to the limitation of the printhead. A specific cost function is proposed to account for the geometry of both the final printed product and the intermediate layers better.

Subject Added Entry-Topical Term  

Metals.

Subject Added Entry-Topical Term  

Propagation.

Subject Added Entry-Topical Term  

Viscosity.

Subject Added Entry-Topical Term  

Pharmaceuticals.

Subject Added Entry-Topical Term  

Polymerization.

Subject Added Entry-Topical Term  

3-D printers.

Subject Added Entry-Topical Term  

Geometry.

Subject Added Entry-Topical Term  

Industrial Revolution.

Subject Added Entry-Topical Term  

Hydrogels.

Subject Added Entry-Topical Term  

Drug dosages.

Subject Added Entry-Topical Term  

Composite materials.

Subject Added Entry-Topical Term  

Robotics.

Subject Added Entry-Topical Term  

European history.

Subject Added Entry-Topical Term  

History.

Subject Added Entry-Topical Term  

Industrial engineering.

Subject Added Entry-Topical Term  

Materials science.

Subject Added Entry-Topical Term  

Pharmaceutical sciences.

Added Entry-Corporate Name  

Purdue University.

Host Item Entry  

Dissertations Abstracts International. 85-01A.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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Control Number  

joongbu:641675