This dataset is designed for long-time temperature-field prediction in single-track directed energy deposition. The data were generated from high-fidelity thermo-fluid simulations in FLOW-3D AM and include 36 stainless-steel Fe 316 single-track DED cases. The cases form a structured 6×6 power-speed process map with six laser power levels and six scan-speed levels. The laser power ranges from 2.0 to 4.5 kW, and the scan speed ranges from 6 to 11 mm/s.Each case was simulated for 2.0 s and curated into 1998 transient temperature frames. The raw simulation outputs are provided as VTK files. A graph-compatible HDF5 representation is also provided, in which all cases share a fixed 1466-node surface graph. The HDF5 data include nodal temperature histories, time-aligned equivalent moving heat-source fields, node coordinates, laser power, scan speed, and original simulation case identifiers.The dataset supports research on DED thermal surrogate modeling, long-horizon recursive prediction, generalization over held-out power-speed combinations, region-aware error analysis, and physics-structured machine learning. The 36 cases are split into 24 training cases, 6 validation cases, and 6 final test cases. The validation and final test cases use power-speed combinations not included in training, so that interpolation within the studied process map can be evaluated.
Human activity recognition (HAR) using sensor data allows the automatic detection of human behavior and actions in everyday environments. The development of scalable and privacy-preserving HAR systems is supported by the nonintrusive collection of time-series data using wearable…
Human activity recognition (HAR) using sensor data allows the automatic detection of human behavior and actions in everyday environments. The development of scalable and privacy-preserving HAR systems is supported by the nonintrusive collection of time-series data using wearable…
These are the data and results reported in the paper "A probabilistic digital twin framework for corrosion-fatigue prognosis of floating offshore wind turbines".
These are the data and results reported in the paper "A probabilistic digital twin framework for corrosion-fatigue prognosis of floating offshore wind turbines".