---
configs:
  - config_name: raw
    data_files:
      - split: train
        path: data/raw/*.parquet
  - config_name: normalized
    data_files:
      - split: train
        path: data/normalized/*.parquet
---

# 🏭 FactoryNet: A Unified Multi-Machine Industrial Dataset

## Overview
FactoryNet is a large-scale, machine-learning-ready foundation dataset for industrial robotics and manufacturing anomaly detection. Historically, industrial datasets have been heavily siloed—every manufacturer and research team uses different column names, units, and structures. 

FactoryNet solves this by forging massive, high-frequency physical datasets from completely different machines into a **single, mathematically unified coordinate system**. By standardizing axes, effort signals, and kinematic feedback, this dataset allows neural networks to learn universal physical relationships across different hardware boundaries.

## 📦 Dataset Composition
This repository currently contains millions of rows of high-frequency sensor data merged from three distinct open-source industrial datasets:

### 1. UMich CNC Mill Tool Wear Dataset
* **Machine:** 3-Axis CNC Mill
* **Task:** Machining wax blocks under varying feedrates and clamp pressures.
* **Anomalies:** Tool wear (Unworn vs. Worn) and visual inspection failures.
* **Original Source:** University of Michigan (via Kaggle)

### 2. AURSAD (Automated UR3e Screwdriving Anomaly Dataset)
* **Machine:** UR3e 6-Axis Collaborative Robot
* **Task:** Automated screwdriving using an OnRobot Screwdriver.
* **Anomalies:** Normal operation, damaged screws, missing screws, extra parts, and damaged threads.
* **Original Source:** Zenodo (Record 4487073)

### 3. voraus-AD (Yu-Cobot Pick-and-Place)
* **Machine:** Yu-Cobot 6-Axis Collaborative Robot
* **Task:** Industrial pick-and-place task on a conveyor belt.
* **Anomalies:** 12 diverse physical anomalies including axis wear (friction/miscommutation), gripping errors, collisions, and added axis weights.
* **Original Source:** voraus robotik (via Kaggle)

---

## 🏗️ The Unified Schema
To allow cross-machine learning, all raw variables (which previously had over 300 conflicting names) have been mapped to a standardized `FactoryNet` schema. 

**Standardized Prefix Naming:**
* `setpoint_*`: The commanded target from the controller (e.g., `setpoint_pos_0`).
* `feedback_*`: The actual measured state from the sensors (e.g., `feedback_vel_1`).
* `effort_*`: The physical force/current applied (e.g., `effort_current_2`, `effort_torque_0`).
* `ctx_*`: Contextual metadata (e.g., `ctx_anomaly_label`, `ctx_busvoltage_0`).

**Standardized Axis Indexing:**
Regardless of how the original manufacturer numbered their joints (X/Y/Z or 1-6), all axes in this dataset are strictly zero-indexed (`0` through `5`).

---

## ⚙️ Configurations
This dataset is partitioned into highly compressed Parquet files and is available in two configurations:

1. **`raw`**: The original physical values (Amps, Volts, Radians, etc.) mapped directly into the new schema. Best for physics-informed neural networks or domain-specific thresholding.
2. **`normalized`**: All continuous physical variables have been independently standardized using a Z-score Scaler (`StandardScaler`) fitted specifically to that machine's domain. Best for immediate deep learning and foundation model training.

## 🚀 How to Use (Python)
Because this dataset is partitioned using Parquet, you can load the entire multi-gigabyte repository without crashing your local RAM.

```python
from datasets import load_dataset

# Load the mathematically normalized dataset for AI training
dataset = load_dataset("karimm6/FactoryNet_Dataset", "normalized")

# Convert to a Pandas DataFrame
df = dataset['train'].to_pandas()
print(df['machine_type'].value_counts())