面向人工智能的紧固件制造检验检测高质量数据集构建方法
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张骄阳,李梦,朱松,董敬,郑天翼
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航天精工股份有限公司，天津，300300
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摘要：随着工业4.0 和智能制造的深入推进，人工智能技术，特别是深度学习，在工业视觉检测领域展现出巨大潜力。紧固件作为工业基础件，其质量直接影响装备的安全与可靠性。然而，当前AI 在紧固件缺陷检测中的应用效果严重受限于训练数据的质量与规模。本文系统性地探讨了面向AI 的紧固件制造检验检测高质量数据集的构建方法。首先，分析了工业现场对数据集的核心需求，包括缺陷多样性、数据真实性、标注精确性等。接着，提出了一套涵盖数据需求分析与规划、多源数据采集、专业化数据标注、数据预处理与增强、数据集管理与版本控制五个关键阶段的完整构建流程。本文重点论述了针对小样本、难例缺陷的数据生成与增强策略，并引入数据质量评估指标体系以确保数据集的可靠性。最后，通过一个面向“螺栓螺纹表面缺陷”的案例，验证了所提方法的有效性与实用性。本研究旨在为紧固件制造业构建高质量AI 数据集提供一套标准化、可落地的技术方案，以加速AI 质检技术的工业化应用。
关健词：人工智能；紧固件；缺陷检测；数据集构建；数据标注；深度学习；智能制造 |
A High-Quality Dataset Construction Method for AI-Based Fastener Manufacturing Inspection and Testing
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Jiaoyang Zhang, Meng Li, Song Zhu, Jing Dong, Tianyi Zheng
Aerospace Precision Products Co., Ltd., Tianjin 300300, China
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Abstract：With the deep advancement of Industry 4.0 and intelligent manufacturing, artificial intelligence (AI) technologies, particularly deep learning, have shown tremendous potential in industrial visual inspection. As fundamental industrial components, fasteners directly affect the safety and reliability of equipment. However, the effectiveness of current AI applications in fastener defect detection is severely limited by the quality and scale of training data. This paper systematically investigates the methodology for constructing high-quality datasets tailored for AI-based manufacturing inspection and testing of fasteners. First, the core requirements of industrial scenarios for datasets are analyzed, including defect diversity, data authenticity, and annotation accuracy. Subsequently, a complete construction process is proposed, encompassing five key stages: data requirement analysis and planning, multi-source data acquisition, professional data annotation, data preprocessing and augmentation, and dataset management with version control. This paper emphasizes data generation and augmentation strategies for few-shot and hard-sample defects and introduces a data quality assessment metric system to ensure dataset reliability. Finally, a case study on "bolt thread surface defects" validates the effectiveness and practicality of the proposed method. This research aims to provide a standardized and implementable technical solution for constructing high-quality AI datasets in the fastener manufacturing industry, thereby accelerating the industrial application of AI-based quality inspection.
Keywords : Artificial intelligence; fasteners; defect detection; dataset construction; data annotation; deep learning; intelligent manufacturing
参考文献
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