Machine Learning Framework for Optimization of Metal Additive Manufacturing Process Parameters
Machine Learning Framework for Optimization of Metal Additive Manufacturing Process Parameters
This project focuses on developing a machine learning–based optimization framework for metal additive manufacturing processes. A large dataset will be generated using finite element simulations (Netfabb) to capture the relationship between process parameters, thermal behavior, residual stress, and distortion in metal additive manufacturing. The generated data will then be used to train AI/ML models capable of predicting process outcomes and identifying optimized manufacturing parameters for improved part quality and process efficiency.
Advisor:
Description:
This project focuses on developing a machine learning–based optimization framework for metal additive manufacturing processes. A large dataset will be generated using finite element simulations (Netfabb) to capture the relationship between process parameters, thermal behavior, residual stress, and distortion in metal additive manufacturing. The generated data will then be used to train AI/ML models capable of predicting process outcomes and identifying optimized manufacturing parameters for improved part quality and process efficiency.
Relevant Technologies:
- Machine Learning
- AI
- Optimization
- Data Analyitics
- Additive Manufacturing
- Modeling & Simulation
Prerequisites:
- Experience with Python or Excel is helpful.
- Familiarity with data science or business analytics is encouraged but not required.
- Students from engineering, statistics, business, or data science backgrounds are welcome.