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README.md

@@ -9,6 +9,8 @@ tags:
   - anomaly-detection
   - physical-ai
   - industrial
+  - tool-wear
+  - cnc
 size_categories:
   - 1M<n<10M
 language:
@@ -22,12 +24,15 @@ A unified multi-robot time-series dataset for industrial anomaly detection and P
 
 ## Dataset Description
 
-FactoryNet unifies multiple robot operation datasets into a common schema for training anomaly detection and reasoning models. This release includes:
+FactoryNet unifies multiple industrial operation datasets into a common schema for training anomaly detection and reasoning models. This release includes:
 
-| Dataset | Robot | Task | Episodes | Signals | Faults |
-|---------|-------|------|----------|---------|--------|
-| **AURSAD** | UR3e (6-DOF) | Screwdriving | 4,094 | 134 | 5 types |
-| **voraus-AD** | Yu-Cobot (6-DOF) | Pick-and-place | 2,122 | 137 | 12 types |
+| Dataset | Machine | Task | Episodes | Rows | Faults |
+|---------|---------|------|----------|------|--------|
+| **AURSAD** | UR3e (6-DOF cobot) | Screwdriving | 4,094 | 6.2M | 5 types |
+| **voraus-AD** | Yu-Cobot (6-DOF) | Pick-and-place | 2,122 | 2.3M | 12 types |
+| **NASA Milling** | CNC (3-axis) | Milling | 167 | 1.5M | Tool wear |
+
+**Total: 6,383 episodes, 10M+ rows**
 
 ## FactoryNet Schema
 
@@ -39,7 +44,9 @@ Intent (setpoint) → Action (effort) → Outcome (feedback)
 
 ### Core Columns (Tier 1 - Universal)
 ```python
-setpoint_pos_0..N    # Commanded joint positions (rad)
+setpoint_pos_0..N    # Commanded joint positions (rad) - for robots
+setpoint_doc         # Depth of cut (mm) - for CNC
+setpoint_feed        # Feed rate (mm/rev) - for CNC
 effort_torque_0..N   # Motor torque/current (Nm / A)
 feedback_pos_0..N    # Actual joint positions (rad)
 timestamp            # Seconds since episode start
@@ -50,6 +57,8 @@ timestamp            # Seconds since episode start
 setpoint_vel_*       # Commanded velocities
 feedback_vel_*       # Actual velocities
 effort_force_x/y/z   # End-effector forces
+aux_vibration_*      # Vibration sensors
+aux_acoustic_*       # Acoustic emission
 ctx_temp_*           # Joint temperatures
 ```
 
@@ -58,12 +67,20 @@ ctx_temp_*           # Joint temperatures
 ```python
 from datasets import load_dataset
 
-# Load AURSAD subset
-ds = load_dataset("forgis/factorynet-hackathon", data_dir="aursad")
+# Load AURSAD (robot screwdriving)
+ds = load_dataset("Forgis/factorynet-hackathon", data_dir="aursad")
+df = ds['train'].to_pandas()
+print(f"AURSAD: {df['episode_id'].nunique()} episodes")
+
+# Load voraus-AD (robot pick-and-place)
+ds = load_dataset("Forgis/factorynet-hackathon", data_dir="voraus")
+df = ds['train'].to_pandas()
+print(f"voraus-AD: {df['episode_id'].nunique()} episodes")
 
-# Access time series
+# Load NASA Milling (CNC tool wear)
+ds = load_dataset("Forgis/factorynet-hackathon", data_dir="nasa_milling")
 df = ds['train'].to_pandas()
-print(df[['timestamp', 'setpoint_pos_0', 'effort_torque_0', 'feedback_pos_0']].head())
+print(f"NASA Milling: {df['episode_id'].nunique()} episodes")
 ```
 
 ## Minimum Viable Episode (MVE)
@@ -76,34 +93,39 @@ This enables causal analysis: if `effort` doesn't follow `setpoint`, something i
 
 ## Fault Types
 
-| Code | Description | Example |
+| Code | Description | Dataset |
 |------|-------------|---------|
-| `normal` | Normal operation | - |
-| `stiff_joint` | Increased joint friction | AURSAD: damaged_thread |
-| `collision` | Contact with obstacle | voraus: can_collision |
-| `grip_failure` | Gripper malfunction | voraus: vacuum_loss |
-| `missing_part` | Expected part absent | AURSAD: missing_screw |
-| `tool_wear` | Progressive degradation | PHM2010: flank_wear |
+| `normal` | Normal operation | All |
+| `stiff_joint` | Increased joint friction | AURSAD |
+| `collision` | Contact with obstacle | voraus-AD |
+| `grip_failure` | Gripper malfunction | voraus-AD |
+| `missing_part` | Expected part absent | AURSAD |
+| `tool_wear` | Progressive degradation | NASA Milling |
 
 ## File Structure
 
 ```
-forgis/factorynet-hackathon/
+Forgis/factorynet-hackathon/
 ├── aursad/
-│   ├── aursad_factorynet.parquet    # Time series (14K rows sample)
-│   ├── aursad_extensions.parquet    # Dataset-specific columns
-│   └── aursad_metadata.json         # Episode metadata
+│   └── aursad_factorynet.parquet        # 1.5 GB - UR3e time series
 ├── voraus/
-│   ├── voraus_ad_100hz_factorynet.parquet
-│   └── voraus_ad_100hz_metadata.json
-└── schema.json                       # FactoryNet schema reference
+│   └── voraus_ad_100hz_factorynet.parquet  # 461 MB - Yu-Cobot time series
+├── nasa_milling/
+│   ├── nasa_milling_factorynet.parquet  # 15 MB - CNC time series
+│   └── nasa_milling_metadata.json       # Episode metadata with wear labels
+├── metadata/
+│   ├── aursad_metadata.json             # 78 MB - AURSAD episode metadata
+│   └── voraus_metadata.json             # 53 MB - voraus episode metadata
+├── schema.json                          # FactoryNet schema reference
+├── factorynet_loader.py                 # Easy-load Python utility
+└── README.md
 ```
 
 ## Use Cases
 
 1. **Anomaly Detection**: Train classifiers to detect faulty operations
 2. **Fault Diagnosis**: Identify which component/joint is failing
-3. **Remaining Useful Life**: Predict when tool/component will fail
+3. **Remaining Useful Life**: Predict when tool/component will fail (NASA Milling)
 4. **Sim2Real Transfer**: Use real data to calibrate simulators
 5. **Robot Q&A**: Answer natural language questions about robot state
 
@@ -117,7 +139,7 @@ If you use this dataset, please cite:
   author={Forgis AI},
   year={2026},
   publisher={HuggingFace},
-  url={https://huggingface.co/datasets/forgis/factorynet-hackathon}
+  url={https://huggingface.co/datasets/Forgis/factorynet-hackathon}
 }
 ```
 
@@ -126,6 +148,7 @@ If you use this dataset, please cite:
 This dataset unifies and standardizes:
 - **AURSAD**: [Zenodo](https://zenodo.org/records/4487073) - CC BY 4.0
 - **voraus-AD**: [GitHub](https://github.com/vorausrobotik/voraus-ad-dataset) - MIT License
+- **NASA Milling**: [NASA Open Data](https://data.nasa.gov/dataset/milling-wear) - Public Domain
 
 ## License