The project aims to enhance the efficiency and reliability of 3D printing processes by integrating machine learning algorithms. Currently, 3D printing often involves trial and error to achieve optimal results, leading to wasted materials and time. By applying machine learning, the project seeks to predict and adjust printing parameters automatically, reducing guesswork and improving print quality. The learners will focus on developing a predictive model that can analyze data from previous prints to suggest optimal settings for future jobs. This project will allow students to apply their knowledge of machine learning, data analysis, and 3D printing technology, providing a practical solution to a common industry challenge. The goal is to create a prototype model that can be tested and refined for real-world applications.

The primary deliverable is a functional machine learning model capable of predicting optimal 3D printing parameters. Additionally, the team will provide a detailed report documenting the development process, data analysis, and testing results. A presentation summarizing the project findings and demonstrating the model's capabilities will also be required. The deliverables will showcase the learners' ability to apply theoretical knowledge to solve practical problems in 3D printing.