2026-10-14T03:10:41Zhttps://www.radar-service.eu/oai/OAIHandler10.35097/37da9d66y4t27q552026-10-09T11:05:28Zradar4kit
https://dx.doi.org/10.35097/37da9d66y4t27q55
Schüßler, Philipp
Schulze, Volker
Dietrich, Stefan
Trained LSTM Ensemble Models for "Real-Time Prediction of Thermal History and Hardness in Laser Powder Bed Fusion Using Deep Learning"
Karlsruhe Institute of Technology
2026
Materials Science
dataset
Dataset
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
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: <added later>
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:
```python
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()
```
Additive Manufacturing
Deep Learning
Surrogate model
Carbon Steel
AISI 4140
42CrMo4
Thermal history
Hardness
Trained model
https://publikationen.bibliothek.kit.edu/1000192076
application/x-tar