Industrial Screw Driving Datasets

Overview

This repository contains a collection of real-world industrial screw driving datasets, designed to support research in manufacturing process monitoring, anomaly detection, and quality control. Each dataset represents different aspects and challenges of automated screw driving operations, with a focus on natural process variations and degradation patterns.

Dataset Collection

The datasets were collected from operational industrial environments, specifically from automated screw driving stations used in manufacturing. Each scenario investigates specific mechanical phenomena that can occur during industrial screw driving operations:

Currently Available Datasets:

1. S01_thread-degradation

• Focus: Investigation of thread degradation through repeated fastening
• Samples: 5,000 screw operations (4,089 normal, 911 faulty)
• Features: Natural degradation patterns, no artificial error induction
• Equipment: Delta PT 40x12 screws, thermoplastic components
• Process: 25 cycles per location, two locations per workpiece
• First published in: HICSS 2024 (West & Deuse, 2024)

2. S02_surface-friction

• Focus: Surface friction effects on screw driving operations
• Samples: 12,500 screw operations (9,512 normal, 2,988 faulty)
• Features: Eight distinct surface conditions (baseline to mechanical damage)
• Equipment: Delta PT 40x12 screws, thermoplastic components, surface treatment materials
• Process: 25 cycles per location, two locations per workpiece
• First published in: CIE51 2024 (West & Deuse, 2024) [DOI will be added after publication]

Upcoming Datasets:

3. S03_screw-error-collection-1 (recorded but unpublished)

• Focus: Varius manipulations of the screw driving process
• Features: More than 20 different errors recorded 
• First published in: Publication planned
• Status: In preparation

4. S04_screw-error-collection-2 (recorded but unpublished)

• Focus: Varius manipulations of the screw driving process
• Features: 25 distinct errors recorded over the course of a week
• First published in: Publication planned
• Status: In preparation

5. S05_upper-workpiece-manipulations (recorded but unpublished)

• Manipulations of the injection molding process with no changes during tightening

6. S06_lower-workpiece-manipulations (recorded but unpublished)

• Manipulations of the injection molding process with no changes during tightening

Additional scenarios may be added to this collection as they become available.

Data Format

Each dataset follows a standardized structure:

• JSON files containing individual screw operation data
• CSV files with operation metadata and labels
• Comprehensive documentation in README files
• Example code for data loading and processing is available in the companion library PyScrew

Research Applications

These datasets are suitable for various research purposes:

• Machine learning model development and validation
• Process monitoring and control systems
• Quality assurance methodology development
• Manufacturing analytics research
• Anomaly detection algorithm benchmarking

Usage Notes

• All datasets include both normal operations and natural process anomalies
• Complete time series data for torque, angle, and additional parameters available
• Detailed documentation of experimental conditions and setup
• Data collection procedures and equipment specifications available

Access and Citation

These datasets are provided under an open-access license to support research and development in manufacturing analytics. When using any of these datasets, please cite the corresponding publication as detailed in each dataset's README file. 

Related Tools

We recommend using our library PyScrew to load and prepare the data. However, the the datasets can be processed using standard JSON and CSV processing libraries. Common data analysis and machine learning frameworks may be used for the analysis. The .tar file provided all information required for each scenario. 

Documentation

Each dataset includes:

• Detailed README files
• Data format specifications
• Equipment and process parameters
• Experimental setup documentation
• Citation information

Contact and Support

For questions, issues, or collaboration interests regarding these datasets, please:

1. Open an issue in the respective GitHub repository
2. Contact the authors through the provided institutional channels

Acknowledgments

These datasets were collected and prepared from:

• RIF Institute for Research and Transfer e.V.
• Technical University Dortmund, Institute for Production Systems

The research was supported by:

• German Ministry of Education and Research (BMBF) 
• European Union's "NextGenerationEU" program
• The research is part of this funding program
• More information regarding the research project is available here