sinatayebati/CladNet-ML-for-AM
A hybrid machine learning framework for clad characteristics prediction in metal additive manufacturing
This framework helps additive manufacturing engineers and researchers predict the geometric features of a clad (like its height, width, and penetration) and identify the best process settings for metal additive manufacturing. It takes in process parameters and material characteristics, then outputs predictions for clad geometry and an optimal process window. This is for professionals involved in designing, optimizing, and controlling metal additive manufacturing processes.
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Use this if you need to quickly understand how different manufacturing parameters will affect the final clad geometry and want to find optimal settings to achieve specific material properties without extensive physical testing.
Not ideal if you are looking for a tool to simulate the entire additive manufacturing process at a microscopic level or for general machine learning tasks outside of clad prediction.
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Jupyter Notebook
License
MIT
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Last pushed
Mar 29, 2024
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