Industrial Screw Driving Datasets

This comprehensive dataset collection captures time-series data from industrial screw driving operations in plastic components. Designed for research in manufacturing process monitoring, anomaly detection, and quality control, it includes over 34,000 individual screw driving operations across six distinct scenarios. Each scenario investigates specific aspects of the screw driving process, from natural wear patterns to controlled material variations.

Overview

Experimental Setup

• Automatic screwing station (EV motor control unit assembly)
• Delta PT 40x12 screws optimized for thermoplastics
• Target torque: 1.4 Nm (range: 1.2-1.6 Nm)
• Sampling frequency: 833.33 Hz
• Data completeness: >95%
• Detailed time-series measurements including torque, angle, gradient, and time values

Dataset Features

These datasets are suitable for various research purposes. Each ZIP file contains: 

• Complete raw data in JSON format for maximum flexibility
• Standardized labels.csv files for metadata and classification
• Comprehensive README.md documentation for each scenario
• Various error classes with varying degrees of severity
• Time series data for the complete screwing process

Research Applications

• Development of machine learning models for anomaly detection
• Process monitoring and quality control system development
• Manufacturing analytics and parameter optimization
• Digital twin development for screw driving operations
• Material property influences on assembly processes

Access and Usage

For data handling, we recommend our PyScrew Python package (https://github.com/nikolaiwest/pyscrew). However, this is optional: The data is easily accessible using standard JSON and CSV processing. No changes were made to the raw data and all information on the experiments can be found in the labels.csv file. 

Citation Request

When using this dataset in academic work, please cite this project or the mentioned papers from the README.md files.

Contact and Support

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

1. Open an issue in our GitHub repository PyScrew
2. Contact us directly via email

Acknowledgments

These datasets were collected and prepared by:

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

The preparation and provision of 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