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SliceHub: An Ecosystem for Re-using, Exploring, and Sharing of Slicing Results
6: Electrical Engineering and Computer Science
15th June 2020
Faraz Faruqi: email@example.com
Imagine you have a model you would like to 3D print. What printer should you use? What materials can you use? Which slicer software should you use to generate the 3D print (gcode), and what is the probability of this print to succeed? These are pertinent questions that come up when we are trying to 3D print our models. While there are online repositories such as Thingiverse that allow users to share and reuse 3D models, there is no such repository that allows users to explore and compare previously generated gcodes. We are designing an online application that facilitates this, and also allows the generation of new printing configurations using cloud-compute. The data so generated will be used to create a deep learning system to predict and generate the slicing results for non-processed configurations and new models. Your job: There are two aspects of this project you can contribute to: 1) design and improve the web application to communicate with the database and the backend processing using cloud compute. 2) use machine learning to generate data for slicing configurations not processed yet. Experience in web development (React.js, Three.js, Python, HTML, CSS) and databases such as MongoDB would be required. If you are interested in the ML part of the project, experience in Deep Learning Frameworks (Tensorflow and PyTorch) and 3D graphics would be required. This is a great project for students interested in web-application development, 3D printing software, and deep learning.
Experience in web development, React.js, Python, MongoDB, and basic Computer Graphics