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About this Abstract
Meeting TMS Specialty Congress 2026
Symposium 4th World Congress on Artificial Intelligence in Materials & Manufacturing (AIM 2026)
Presentation Title Machine Learning-Based Prediction of Mechanical Property Heterogeneity in Inconel 718 Superalloy Manufactured by Directed Energy Deposition
Author(s) Hanumesh Sai Pamisetty, Surya Teja Bijjala, Pankaj Kumar
On-Site Speaker (Planned) Hanumesh Sai Pamisetty
Abstract Scope The heterogeneous microstructure obtained by powder-based laser-directed energy deposition (L-DED) often leads to mechanical property heterogeneity in finished products. Hence it is crucial to establish process-property heterogeneity relationships for its large-scale implementation. To support such efforts, we developed neural network models trained on microstructural features and corresponding microhardness measurements to establish quantitative structure–property correlations. Rapid data generation was enabled through optical microscopy imaging paired with microhardness measurements for multiple layers sectioned at varying depths. Microstructural variability in the vicinity of each indent was efficiently encoded using microstructural descriptors and kernel mean embedding methods. Neural-network predictions were validated through additional microhardness measurements at previously untested locations. The resulting microstructure–property correlations provide a data-driven framework for selecting and optimizing L-DED processing parameters to control mechanical-property heterogeneity in additively manufactured components.
Proceedings Inclusion? Undecided

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