Trained LSTM Ensemble Models for "Real-Time Prediction of Thermal History and Hardness in Laser Powder Bed Fusion Using Deep Learning"

Each ensemble consists of five independently seeded models trained with a six-stage curriculum that incrementally expands the training data selection. The three ensembles differ only in LSTM architecture depth and width, enabling a systematic comparison of model complexity.

The trained weights are consumed by the companion framework via its inference and testing entry points. Full-text publication: <added later> Code-Repository: doi.org/10.35097/dg39f4p0wxqdnfxy Training-Validation-Testing Dataset: doi.org/10.35097/pmem1cb9gu1ck8xz