2026-05-23T21:56:04Zhttps://www.radar-service.eu/oai/OAIHandler10.35097/dg39f4p0wxqdnfxy2026-04-15T06:22:36Zradar4kit
10.35097/dg39f4p0wxqdnfxy
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)
Code Repository 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
Open Access
Other
Schüßler, Philipp
0000-0002-7081-1680
Schulze, Volker
Dietrich, Stefan
0000-0002-2955-4125
A PyTorch LSTM model that predicts the thermal history of individual measurement points during laser powder bed fusion (PBF-LB/M) additive manufacturing. The model uses teacher-forcing during training and supports both teacher-forcing and auto-regressive (inference) forward modes. An ensemble training workflow is included for uncertainty quantification.
Version v1.0.0
Full-text publication: <added later>
GitLab repository: https://gitlab.kit.edu/kit/iam-wk-public/iam-wk-fub-deep-learning-pbf-lb
Trained model dataset: https://doi.org/10.35097/37da9d66y4t27q55
Training-Validation-Testing Dataset: https://doi.org/10.35097/pmem1cb9gu1ck8xz
Full-text publication for the FEM simulation model: https://doi.org/10.1080/17452759.2023.2271455
Readme.md is located in the .zip archive
https://publikationen.bibliothek.kit.edu/1000192079
123,9 kB
application/x-tar