PyScrew：来自工业螺丝拧紧实验的综合数据集集合
PyScrew: A Comprehensive Dataset Collection from Industrial Screw Driving Experiments
AI总结：
本文发布含六组、逾34,000次操作的工业螺丝拧紧数据集集合，并通过Zenodo与PyScrew库支持标准化访问，以促进制造监测、质量控制和机器学习方法的可复现研究。
摘要：
本文提出了一个工业螺丝拧紧数据集的综合集合，旨在推进制造过程监测和质量控制方面的研究。该集合包含六个不同的数据集，涵盖在受控实验条件下开展的超过34,000次独立螺丝拧紧操作，捕捉了塑料部件中螺丝拧紧过程的多面性。每个数据集系统地研究特定方面：通过重复使用呈现的自然螺纹退化模式（s01）；包括污染和表面处理在内的表面摩擦条件变化（s02）；多达27种错误类型的多样化装配故障（s03–s04）；以及通过调整注塑成型设置而在上、下工件中产生的制造参数变化（s05–s06）。我们详细介绍了所有数据集采用的标准化实验设置，包括硬件规格、工艺阶段和数据采集方法。分层数据模型保留了螺丝拧紧过程的时间结构和操作结构，从而便于探索性分析以及机器学习模型的开发。为最大限度提升可访问性，我们提供双重访问途径：通过 Zenodo 获取具有持久 DOI 的原始数据，以及使用专门构建的 Python 库 PyScrew；该库为数据加载、预处理以及与常见分析工作流的集成提供一致接口。这些数据集服务于多种研究应用，包括异常检测、预测性维护、质量控制系统开发、特征提取方法评估以及特定错误状态分类。通过解决工业制造中标准化、综合数据集稀缺的问题，该集合使可复现研究以及对分析方法进行公平比较成为可能，而这一领域对工业自动化的重要性正不断提升。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
