Work Description

Title: Modeling of the High-Viscosity Fluid Transient Flow for Material Deposition in Direct Ink Writing Open Access Deposited

h
Attribute Value
Methodology
  • The pipe and static mixer (SM) used for the experimental step response tests: Two piezoresistive pressure sensors (Model 24PCGFH6G, Honeywell Charlotte, NC, USA) were placed at the fluid inlet and near the fluid outlet of the pipe or SM. The pressure sensors were placed at the pipe and SM inlet (marked as Pressure Sensor #1) and 150 mm from the inlet (marked as Pressure Sensor #2). An op-amp circuit amplifies the signal from the pressure sensors with a gain of 10. The amplified pressure sensor signal was read by an Arduino microprocessor (Arduino GIGA R1 WiFi) at a 1000 Hz sampling rate. The pressure sensors were calibrated against a pressure gauge (Model DPGA-07, Dwyer Instruments Michigan City, Indiana) using a custom pressure manifold. The pressure drop is defined as the difference in pressure from Pressure Sensor #1 to Pressure Sensor #2. Video footage of outlet flow was captured with a digital microscope camera (Dino-Lite AM73115MZT, Dunwell Tech Inc. Torrance, CA), placed at the outlet of the pipe or SM. Images of the outlet flow were captured at a frame rate of 45 frames per second, and were digitally processed to quantify volumetric flow.

  • The experimental configuration for the printing of the 90º corner: For each acceleration value, the experimental test was repeated five times for a total of 30 tests. After each test, a picture of the fluid deposition was taken by a digital microscope camera (UWT500X020M, AmScope Irvine California) placed directly over the point B. The microscope camera was calibrated with a digital caliper. The image from the microscope camera, Figure 15, is processed in Python 3.11 to measure the print profile, tool path, and corner swell of the DIW extrusion.
Description
  • A transient flow model is developed to predict the flow of high-viscosity fluid dispensing for precision direct ink writing (DIW) in additive manufacturing. Models for pump deformation and fluid friction to accurately predict the flow of a high-viscosity non-Newtonian fluid through a progressive cavity pump, static mixer, and a tapered nozzle are created. Inside the progressive cavity pump, the effect of elastic deformation on modeling high-viscosity fluid transient flow is included. Based on the Characteristic Method (CM) and boundary conditions for DIW, the continuity and momentum equations are numerically solved. Using deformation modeling and CM, the transient response of the DIW system with step changes to the input volumetric flow rate is modeled for both a tube and spiral static mixer. The transient response of the DIW output volumetric flow rate is recorded using flow and pressure sensors and found to match the flow model. The deformation and CM models are applied to predict the corner swelling of a 90º corner DIW tool path from trapezoidal motion planning with accelerations from 100 to 2000 mm/s2. The predicted corner swelling is matched with the actual corner swelling found through image processing of the 90º corner produced via DIW. The corner swelling is significant, ranging from 0.76 to 0.37 mm for a line width of 0.25 mm and a height of 0.15 mm, and represents the model’s ability to quantify print errors. This study demonstrates that the flow model can accurately predict the transient response of the DIW volumetric flow rate, which is foundational to high-fidelity flow control and compensation in precision DIW.
Creator
Creator ORCID iD
Depositor
Depositor creator
  • true
Contact information
Discipline
Funding agency
  • National Science Foundation (NSF)
  • Other Funding Agency
Other Funding agency
  • Dow Inc.
Keyword
Resource type
Last modified
  • 03/13/2025
Published
  • 03/13/2025
Language
DOI
  • https://doi.org/10.7302/d4w8-kh94
License
To Cite this Work:
Lorenz, J., Hildner, M., van den Bogert, W., Zhu, B., Yee, S., Fazeli, N., Shih, A. J. (2025). Modeling of the High-Viscosity Fluid Transient Flow for Material Deposition in Direct Ink Writing [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/d4w8-kh94

Relationships

This work is not a member of any user collections.

Files (Count: 2; Size: 88.3 GB)

Thumbnailthumbnail-column Title Original Upload Last Modified File Size Access Actions
dataset.zip 2025-03-13 2025-03-13 88.3 GB Open Access
README.txt 2025-03-13 2025-03-13 6.86 KB Open Access

Dataset Title: Modeling of the High-Viscosity Fluid Transient Flow for Material Deposition in Direct Ink Writing

Dataset Contact: James Lorenz, [email protected]

Dataset Creators:
Name: James Lorenz
Email: [email protected]
Institution: University of Michigan Department of Mechanical Engineering
ORCID: https://orcid.org/0000-0002-4339-763X

Name: Matthew Hildner
Email: [email protected]
Institution: University of Michigan Department of Mechanical Engineering

Name: William van den Bogert
Email: [email protected]
Institution: University of Michigan Department of Mechanical Engineering
ORCID: https://orcid.org/0009-0001-2142-8116

Name: Bizhong Zhu
Email: [email protected]
Institution: Dow Inc.

Name: Stanley Yee
Email: [email protected]
Institution: Dow Inc.

Name: Nima Fazeli
Email: [email protected]
Institution: University of Michigan Department of Robotics

Name: Albert J. Shih
Email: [email protected]
Institution: University of Michigan Department of Mechanical Engineering

Funding: 2231607 (NSF Award), Dow Inc.

Key Points:
-Novel transient flow model for direct ink writing in additive manufacturing
-Integrates pump deformation, static mixer, and tapered nozzle for fluid dynamics
-Robust fluid continuity simulation is developed using the Method of Characteristics
-Experimental validation confirms accurate prediction of direct ink write corner swell
-Image analysis quantifies deposition profiles for improved DIW control

Research Overview:
A transient flow model is developed to predict the flow of high-viscosity fluid dispensing for precision direct ink writing (DIW) in additive manufacturing. Models for pump deformation and fluid friction to accurately predict the flow of a high-viscosity non-Newtonian fluid through a progressive cavity pump, static mixer, and a tapered nozzle are created. Inside the progressive cavity pump, the effect of elastic deformation on modeling high-viscosity fluid transient flow is included. Based on the Characteristic Method (CM) and boundary conditions for DIW, the continuity and momentum equations are numerically solved. Using deformation modeling and CM, the transient response of the DIW system with step changes to the input volumetric flow rate is modeled for both a tube and spiral static mixer. The transient response of the DIW output volumetric flow rate is recorded using flow and pressure sensors and found to match the flow model. The deformation and CM models are applied to predict the corner swelling of a 90º corner DIW tool path from trapezoidal motion planning with accelerations from 100 to 2000 mm/s2. The predicted corner swelling is matched with the actual corner swelling found through image processing of the 90º corner produced via DIW. The corner swelling is significant, ranging from 0.76 to 0.37 mm for a line width of 0.25 mm and a height of 0.15 mm, and represents the model’s ability to quantify print errors. This study demonstrates that the flow model can accurately predict the transient response of the DIW volumetric flow rate, which is foundational to high-fidelity flow control and compensation in precision DIW.

Methodology:
The pipe and static mixer (SM) used for the experimental step response tests: Two piezoresistive pressure sensors (Model 24PCGFH6G, Honeywell Charlotte, NC, USA) were placed at the fluid inlet and near the fluid outlet of the pipe or SM. The pressure sensors were placed at the pipe and SM inlet (marked as Pressure Sensor #1) and 150 mm from the inlet (marked as Pressure Sensor #2). An op-amp circuit amplifies the signal from the pressure sensors with a gain of 10. The amplified pressure sensor signal was read by an Arduino microprocessor (Arduino GIGA R1 WiFi) at a 1000 Hz sampling rate. The pressure sensors were calibrated against a pressure gauge (Model DPGA-07, Dwyer Instruments Michigan City, Indiana) using a custom pressure manifold. The pressure drop is defined as the difference in pressure from Pressure Sensor #1 to Pressure Sensor #2. Video footage of outlet flow was captured with a digital microscope camera (Dino-Lite AM73115MZT, Dunwell Tech Inc. Torrance, CA), placed at the outlet of the pipe or SM. Images of the outlet flow were captured at a frame rate of 45 frames per second, and were digitally processed to quantify volumetric flow. The experimental configuration for the printing of the 90º corner: For each acceleration value, the experimental test was repeated five times for a total of 30 tests. After each test, a picture of the fluid deposition was taken by a digital microscope camera (UWT500X020M, AmScope Irvine California) placed directly over the point B. The microscope camera was calibrated with a digital caliper. The image from the microscope camera, Figure 15, is processed in Python 3.11 to measure the print profile, tool path, and corner swell of the DIW extrusion.

Files contained here:
The folders show divisions based on each experiment or simulation conducted:
- "corner experiment data": data relating to the 90-deg corner DIW experiment. Contains unprocessed images and processed images from the experiment, as well as Matlab code used for image processing and data visualization.
- "step experiment data": data relating to the step-response flowrate experiment. Contains raw video, processed video frame with image segmentation, as well as figures and code used for image processing and data visualization.
- "data": raw data for the static mixer step-response flowrate experiment
- "open_pipe_data": raw data for the pipe step-response flowrate experiment
- "no_mixer_data": raw data of the pump step-response with no pipe or static mixer installed
- "Flowrate Calibration": data and code related to calibrating the progressive cavity pumps
- "Pressure Calibration": data and core related to calibrating the pressure sensors
- "pressure_sensors": Arduino code for live measurement of the pressure sensors
- "Sensor Documentation": data sheets and documentation relating to the pressure sensors
- "simulation results data": data relating to the fluid flow simulations for both the corner experiment and step experiment.
- "flow modeling code": code collection relating to the fluid flow simulation developed by the authors. Contains the Python script created to run the computational flow dynamics simulation, as well as a directory of input flowrates and physical constants used to generate the simulation results data.

Related publication(s):
Lorenz, J., et al. (2025). Modeling of the High-Viscosity Fluid Transient Flow for Material Deposition in Direct Ink Writing. Forthcoming.

Use and Access:
This data set is made available under the Attribution-NonCommercial 4.0 International license (CC BY 4.0).

To Cite Data:
Lorenz, J., Hildner, M., van den Bogert, W., Zhu, B., Yee, S., Fazeli, N., Shih, A. J. Dataset for Modeling of the High-Viscosity Fluid Transient Flow for Material Deposition in Direct Ink Writing [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/d4w8-kh94

Download All Files (To download individual files, select them in the “Files” panel above)

Total work file size of 88.3 GB is too large to download directly. Consider using Globus (see below).



Best for data sets > 3 GB. Globus is the platform Deep Blue Data uses to make large data sets available.   More about Globus

Remediation of Harmful Language

The University of Michigan Library aims to describe its collections in a way that respects the people and communities who create, use, and are represented in them. We encourage you to contact us anonymously if you encounter harmful or problematic language in catalog records or finding aids. More information about our policies and practices is available at Remediation of Harmful Language.