Trained LSTM Ensemble Models for "Real-Time Prediction of Thermal History and Hardness in Laser Powder Bed Fusion Using Deep Learning"
收藏DataCite Commons2026-04-15 更新2026-05-04 收录
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https://radar.kit.edu/radar/en/dataset/37da9d66y4t27q55
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资源简介：
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
提供机构：
Karlsruhe Institute of Technology
创建时间：
2026-04-15
