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

2026-05-28T06:57:13Zhttps://www.radar-service.eu/oai/OAIHandler

10.35097/37da9d66y4t27q552026-04-15T06:28:36Zradar4kit

10.35097/37da9d66y4t27q55 Schüßler, Philipp Philipp Schüßler 0000-0002-7081-1680 Institut für Angewandte Materialien – Werkstoffkunde (IAM-WK), Karlsruher Institut für Technologie (KIT) Schulze, Volker Volker Schulze Institut für Angewandte Materialien – Werkstoffkunde (IAM-WK), Karlsruher Institut für Technologie (KIT) Dietrich, Stefan Stefan Dietrich 0000-0002-2955-4125 Institut für Angewandte Materialien – Werkstoffkunde (IAM-WK), Karlsruher Institut für Technologie (KIT) 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 2026 Materials Science Additive Manufacturing Deep Learning Surrogate model Carbon Steel AISI 4140 42CrMo4 Thermal history Hardness Trained model Open Access Creative Commons Attribution Non Commercial No Derivatives 4.0 International Schüßler, Philipp 0000-0002-7081-1680 Schulze, Volker Dietrich, Stefan 0000-0002-2955-4125 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() ``` https://publikationen.bibliothek.kit.edu/1000192076 1,7 GB application/x-tar