Update README.md

README.md

@@ -4,5 +4,119 @@ task_categories:
 language:
 - en
 ---
+language:
+  - en
+pretty_name: "NASA Milling Dataset"
+tags:
+  - manufacturing
+  - time-series
+  - sensors
+  - tool-wear
+license: "cc-by-4.0"
+task_categories:
+  - other
+---
+
+# Dataset Card for NASA Milling Dataset
+
+## Dataset Summary
+
+This dataset contains measurements from **milling machine experiments** conducted at NASA Ames Research Center (Goebel, 1996) in collaboration with UC Berkeley.  
+The experiments investigated **tool wear** during milling under different conditions. Multiple sensors were used, including acoustic emission, vibration, and spindle motor current sensors, capturing the machine’s behavior during the cutting process.  
+
+The dataset has been reformatted into **Parquet** for easier use in data science workflows. It provides both experimental parameters and sensor signals, along with measurements of **flank wear (VB)** taken intermittently.  
+
+This dataset can be used for research in:
+- Tool condition monitoring  
+- Sensor fusion  
+- Predictive maintenance  
+- Time-series analysis in manufacturing  
+
+## Supported Languages
+
+The dataset contains **numeric sensor data only**.  
+Language-agnostic; `language: en` is used since the documentation is in English.
+
+## Dataset Structure
+
+The dataset is provided as a **single Parquet file** (`data.parquet`).  
+
+Each row corresponds to a recorded **experimental run** with sensor measurements and metadata.  
+
+### Features (columns):
+
+- `case`: Case number (1–16), defines experimental setup.  
+- `run`: Counter for experimental runs in each case.  
+- `VB`: Flank wear (mm), measured intermittently.  
+- `time`: Duration of experiment run.  
+- `DOC`: Depth of cut (mm).  
+- `feed`: Feed rate (mm/rev).  
+- `material`: Workpiece material (1 = cast iron, 2 = steel).  
+- `smcAC`: AC spindle motor current.  
+- `smcDC`: DC spindle motor current.  
+- `vib_table`: Table vibration.  
+- `vib_spindle`: Spindle vibration.  
+- `AE_table`: Acoustic emission at the table.  
+- `AE_spindle`: Acoustic emission at the spindle.  
+
+### Experimental Conditions
+
+The 16 cases represent combinations of:  
+
+- **Depth of cut:** 0.75 mm or 1.5 mm  
+- **Feed rate:** 0.25 mm/rev or 0.5 mm/rev  
+- **Material:** Cast iron or steel  
+
+Each condition was repeated with two different sets of inserts, giving 16 total cases.
 
-Milling data with tool wear. Source: https://data.nasa.gov/Raw-Data/Milling-Wear/vjv9-9f3x/data
+## Data Splits
+
+The dataset has **no predefined splits**.  
+All data is provided in a single file. Users may create their own train/validation/test splits.
+
+## Dataset Creation
+
+### Curation Rationale
+
+The dataset was collected to study **tool wear progression** during milling, and to investigate how different sensor signals correlate with wear.
+
+### Source Data
+
+- **Original source:** NASA Ames & UC Berkeley milling experiments  https://data.nasa.gov/Raw-Data/Milling-Wear/vjv9-9f3x/data
+- **Documentation:** Goebel, K. (1996). *Management of Uncertainty in Sensor Validation, Sensor Fusion, and Diagnosis of Mechanical Systems Using Soft Computing Techniques.* Ph.D. Thesis, UC Berkeley.  
+
+### Collection Process
+
+- Milling performed on a Matsuura machining center (MC-510V) at **cutting speed 200 m/min** (826 RPM).  
+- Sensors: Acoustic emission, vibration (spindle & table), spindle motor current.  
+- Data acquisition: High-speed DAQ at up to 100 kHz, filtered and RMS-processed to ~250 Hz effective sampling.  
+- Tool wear measured as flank wear (VB) using a microscope, at irregular intervals.  
+
+## Considerations for Using the Data
+
+- Flank wear (`VB`) was not measured after every run; some values are missing.  
+- Signals were preprocessed (filtered and RMS smoothed). Raw AE and vibration were also recorded but are not included here.  
+- Units vary by signal type (currents in A, vibrations in g, AE in V).  
+
+## License
+
+This dataset is shared under the **CC-BY-4.0** license. Please cite appropriately when using.
+
+## Citation
+
+If you use this dataset, please cite:  
+
+```
+@phdthesis{goebel1996,
+  title={Management of Uncertainty in Sensor Validation, Sensor Fusion, and Diagnosis of Mechanical Systems Using Soft Computing Techniques},
+  author={Goebel, Kai},
+  school={University of California, Berkeley},
+  year={1996}
+}
+```
+
+## Dataset Card Contact
+
+For issues with this dataset card, please open an issue or pull request on the Hugging Face dataset repository.
+
+---