About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Presentation Title |
Correlation Between Additive Manufacturing Process Parameters and Microstructural Descriptors Via Automatic Feature Engineering |
Author(s) |
Mohamed Imad Eddine Heddar, Mehdi Brahim, Nedjoua Matougui |
On-Site Speaker (Planned) |
Mohamed Imad Eddine Heddar |
Abstract Scope |
This paper explore the use of automatic feature engineering coupled with machine learning for Additive Manufacturing (AM) processing of metals, to derive empirical relationships between processing parameters and microstructural descriptors. A kinetic Monte Carlo algorithm is used to generate the microstructures. The resulting artificial microstructures were then analyzed using image processing to extract key morphological properties. Additional non-linear transformations and combination using arithmetic operators were then applied on to the features dataset. This transformation increases the performance of the linear model for predicting all the microstructural descriptors, for grain size prediction the R2 score improves from 0.4 to 0.902, similar improvements were also recorded for other descriptors. To further simplify the models, the number of added features was reduced by analyzing feature importance and coefficients magnitude while retaining a reasonable prediction accuracy. |
Proceedings Inclusion? |
Undecided |