PyScrew: A Comprehensive Dataset Collection from Industrial Screw Driving Experiments

• URL: http://arxiv.org/abs/2505.11925v1
• Date: Sat, 17 May 2025 09:20:20 GMT
• Title: PyScrew: A Comprehensive Dataset Collection from Industrial Screw Driving Experiments
• Authors: Nikolai West, Jochen Deuse,
• Abstract summary: The collection comprises six distinct datasets with over 34,000 individual screw driving operations conducted under dual conditions.<n>We detail the standardized experimental setup used across all datasets, including hardware specifications, process phases, and data acquisition methods.<n>The data model preserves the temporal and operational structure of screw driving processes, facilitating both exploratory analysis and the development of machine learning models.
• Score: 0.8287206589886881
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a comprehensive collection of industrial screw driving datasets designed to advance research in manufacturing process monitoring and quality control. The collection comprises six distinct datasets with over 34,000 individual screw driving operations conducted under controlled experimental conditions, capturing the multifaceted nature of screw driving processes in plastic components. Each dataset systematically investigates specific aspects: natural thread degradation patterns through repeated use (s01), variations in surface friction conditions including contamination and surface treatments (s02), diverse assembly faults with up to 27 error types (s03-s04), and fabrication parameter variations in both upper and lower workpieces through modified injection molding settings (s05-s06). We detail the standardized experimental setup used across all datasets, including hardware specifications, process phases, and data acquisition methods. The hierarchical data model preserves the temporal and operational structure of screw driving processes, facilitating both exploratory analysis and the development of machine learning models. To maximize accessibility, we provide dual access pathways: raw data through Zenodo with a persistent DOI, and a purpose-built Python library (PyScrew) that offers consistent interfaces for data loading, preprocessing, and integration with common analysis workflows. These datasets serve diverse research applications including anomaly detection, predictive maintenance, quality control system development, feature extraction methodology evaluation, and classification of specific error conditions. By addressing the scarcity of standardized, comprehensive datasets in industrial manufacturing, this collection enables reproducible research and fair comparison of analytical approaches in an area of growing importance for industrial automation.

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