DOI: 10.5281/zenodo.14729547
title: Industrial screw driving dataset collection: Time series data for process monitoring and anomaly detection

description: 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




Scenario name
Observations
Classes
Purpose


s01_variations-in-thread-degradation
 5,000
 1
Studies natural degradation of plastic threads over repeated use cycles, documenting wear patterns and failure progression


s02_variations-in-surface-friction
12,500
 8
Examines effects of different surface conditions (lubricants, surface treatments, contamination) on screw driving performance


s03_variations-in-assembly-conditions-1
 1,700
26
Investigates diverse component and assembly faults including washer modifications, thread deformations, and alignment issues


s04_variations-in-assembly-conditions-2
 5,000
25
Features methodically arranged assembly fault conditions in 5 distinct error groups with paired normal/abnormal operations


s05_variations-in-upper-workpiece-fabrication
 2,400
42
Analyzes how injection molding parameter variations in the upper component affect screw driving metrics


s06_variations-in-lower-workpiece-fabrication
 7,482
44
Explores effects of injection molding parameter variations in the lower component on fastening quality




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:



Open an issue in our GitHub repository PyScrew

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
description:  




Version
Date
Features


 v1.2.3
21.07.2025




Fix a "labeling" issue in s01, s02 and s03 where the "workpiece_usage" was set to 0 constantly, due to an error in the generator script for automated csv generation 

No changes any raw data, only adding a (now useful) supporting feature in the prep-pipeline for better sampling 




v.1.2.2
13.05.2025




Fixed an duplication issue in s03 by the removing 003_control-group-3 (86% duplicates when compared to the class 004_control-group-from-s01 due to a sampling error)




v1.2.1
25.04.2025




Standardized zenodo citation in all readme files for the datasets

Fixed an inconsistency in the class naming conventions in s05 and s06




v1.2.0
17.04.2025




Upload of s03 with variations in assembly conditions in 27 classes (n=1800)

Upload of s04 with variations in assembly conditions in 25 classes (n=2500)

Comprehensive overhaul of documentation and naming conventions for clarity

Removed the .tar files to simplify the data access (with just one file per scenario)




v1.1.4
02.04.2025




Upload of s06 with injection molding manipulations in 47 classes 




v1.1.3
18.02.2025




Upload of s05 with injection molding manipulations in 44 classes 




v1.1.2
12.02.2025




Change to default names for `label.csv` and `README.md` in all scenarios




v1.1.1
12.02.2025




Reupload of both s01 and s02 as zip (smaller size) and tar (faster extraction) files

Change to the data structure (now organized as subdirectories per class in `json/`)




v1.1.0
30.01.2025




Initial uplload of the second scenario `s02_surface-friction`




v1.0.0
24.01.2025




Initial upload of the first scenario `s01_thread-degradation`