Savei

Predicts particle impact velocity of cold spray systems.

What is Savei?

Savei is a Python-based application designed to predict particle impact velocity in cold spray additive manufacturing processes. This tool aims to assist engineers and researchers in optimizing and analyzing cold spray techniques by providing accurate velocity predictions based on various input parameters.

Features

• Make predictions using advanced computational models.
• Includes particle critical velocity and particle erosion velocity.
• Easy-to-use graphical user interface.
• Visualizes data and predictions.

Installation

Setup

1. Clone the Repository

   git clone https://github.com/noahnle/savei.git
   cd savei

2. Create and Activate a Virtual Environment

   python3.11 -m venv savei-venv

   source savei-venv/bin/activate  # On Windows: savei-venv\Scripts\activate

3. Install Dependencies

   pip install -r requirements.txt

Note

Tkinter is included with most Python installations, but it may not be available in some versions. If you encounter issues, verify that Tkinter is included in your Python installation. I recommend to use Python 3.11.

Usage

1. Run the Application

   python savei.py

2. Using the GUI

   1. Input the necessary parameters for your cold spray system.
   2. Click the "Predict" button to compute the particle impact velocity.
   3. View and analyze the results displayed in the application.

Example

Tests

Overview

Savei was validated using a single spray data set to assess its accuracy in predicting particle impact velocity.

Testing Methodology

1. Parameter Testing: The tool was tested using parameters from a single spray data set. This involved analyzing clumps of particles grouped together and assigned a specific speed.

2. Validation with Experimental Data: The validation was performed using experimental data from one spray, where the largest cluster of particles traveled at a speed of approximately 1034.51 m/s. Using the same parameters and the average size for this data set, Savei predicted a speed of 1065.88 m/s.

Results

• Accuracy: Achieved 97.06% accuracy for the single spray data set used in validation.
• Observations: The tool’s prediction was very close to the actual measured speed, indicating its effectiveness in predicting particle impact velocities for the tested parameter.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

This was a senior design project.

Shoutout to our mentors for their guidance and expertise throughout the project. We also appreciate the Department of Defense for their support in testing and validating the application's predictive capabilities.

The following sources were crucial in shaping the equations and concepts used in Savei:

• Assadi, H., Schmidt, T., Richter, H., Kliemann, J.-O., Binder, K., Gärtner, F., Klassen, T., & Kreye, H. (2011). On Parameter Selection in Cold Spraying. Journal of Thermal Spray Technology, 20(6), 1161–1176. DOI: 10.1007/s11666-011-9662-9

• Arbegast Materials Processing and Joining Lab. (2012). Cold Spray: A Guide to Best Practice. South Dakota School of Mines and Technology, Rapid City, SD.

• Champagne Jr., V. K., Ozdemir, O. C., & Nardi, A. (Eds.). (2020). Practical Cold Spray. Springer. ISBN 978-3-030-70055-3; ISBN 978-3-030-70056-0 (eBook). DOI: 10.1007/978-3-030-70056-0

• Zou, Y. (2021). Cold Spray Additive Manufacturing: Microstructure Evolution and Bonding Features. Accounts of Materials Research, 2, 1071−1081. DOI: 10.1021/accountsmr.1c00138

⚠️ DISCLAIMER ⚠️

This tool is intended for educational and research purposes only. It is your responsibility to ensure that any use of this tool complies with all applicable laws and regulations. The developers and contributors of this project are not responsible for any misuse or consequences arising from the use of this tool.