Study on Reliability of Accuracy Verification of Machine Vision Equipment Using Copper Wire
Year: 2025
Type: article
Abstract: 本研究基于机器视觉原理构建铜丝直径检测系统，通过标准化实验流程验证设备精度。检测系统由图像采集与处理两大核心模块构成，采用高分辨率工业相机配合专业级单远心镜头，在精密校准的显微视野范围内实现微米级测量精度。针对传统方法中机械定位引起的测量误差，本研究通过固定光学成像距离有效控制检测环境变量。实验选用多规格标准铜丝样本，结合接触式精密量具与视觉检测系统进行对比分析。在标准化操作流程中，物理测量采用多点密集采样取均值作为基准数据，同步进行的非接触式检测则通过多方位图像捕捉与智能算法提取特征参数。通过建立基准数据与视觉检测值的数学模型，最终验证了该视觉系统在精度指标与重复性方面均达到工业检测标准要求，证实了该检测方案在微细线材质量管控中的实用价值。This study establishes a copper wire diameter inspection system based on machine vision principles, with equipment accuracy validated through standardized experimental protocols. The detection system comprises two core modules: image acquisition and processing, achieving micrometer-level measurement accuracy within a precisely calibrated microscopic field of view through the integration of a high-resolution industrial camera and professional-grade single telecentric lens. Addressing measurement errors caused by mechanical positioning in traditional methods, this research effectively controls environmental variables through fixed optical imaging distance. Experimental verification employs multi-specification standard copper wire samples for comparative analysis between contact-based precision measuring tools and the vision-based inspection system. Under standardized operational procedures, physical measurements adopt multi-point dense sampling with averaging as reference data, while the non-contact detection simultaneously performs multi-angle image capture and feature parameter extraction through intelligent algorithms. By establishing a mathematical model between reference data and vision-derived measurements, the results confirm that the visual system meets industrial inspection standards in both precision metrics and repeatability, demonstrating its practical value in quality control of micro-diameter wire materials.
Author: 曼霞 周
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4409116292