2026-10-14T07:22:21Zhttps://www.radar-service.eu/oai/OAIHandler

10.35097/dg39f4p0wxqdnfxy2026-10-09T10:48:50Zradar4kit

https://dx.doi.org/10.35097/dg39f4p0wxqdnfxy Schüßler, Philipp Schulze, Volker Dietrich, Stefan Code Repository 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 Software info:eu-repo/semantics/openAccess Other info:eu-repo/semantics/openAccess https://opensource.org/licenses/MIT 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: https://doi.org/10.1016/j.commatsci.2026.115025 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 Additive Manufacturing Deep Learning Surrogate model Carbon Steel AISI 4140 42CrMo4 Thermal history Hardness https://publikationen.bibliothek.kit.edu/1000192079 application/x-tar