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

This archive contains three sets of trained LSTM ensemble models for surrogate-based thermal history prediction during laser powder bed fusion (PBF-LB/M) of 42CrMo4 steel. 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:
Code-Repository: https://doi.org/10.35097/dg39f4p0wxqdnfxy
Training-Validation-Testing Dataset: https://doi.org/10.35097/pmem1cb9gu1ck8xz

Contains 3 .zip files with the trained model ensembles and the torch.nn.Module class:

• 01Layers16Cells_Ensemble.zip
• 02Layers32Cells_Ensemble.zip
• 04Layers64Cells_Ensemble.zip
• recurrent_neuralnetworks.py
  Loading a checkpoint manually:

import torch
from recurrent_neuralnetworks import LSTMModelWithTeacherForcing
model = LSTMModelWithTeacherForcing(
    num_features=12,   # 11 inputs + 1 temperature feedback
    num_hidden=32,     # match the run (16 / 32 / 64)
    num_layers=2,      # match the run (1 / 2 / 4)
    num_labels=1,
)
state = torch.load("path/to/model_best_....pth", map_location="cpu")
model.load_state_dict(state)
model.eval()