Zeng Li, Wan Feng, Zhang Baiyun, Zhu Xu
School of Mechanical and Electrical Engineering, Zhejiang Industry Polytechnic College, Shaoxing 312000, China.
Ningbo Jiapeng Machinery Equipment Manufacturing Co., Ltd., Ningbo 315101, China.
Sensors (Basel). 2024 Dec 7;24(23):7824. doi: 10.3390/s24237824.
In the high-stakes domain of precision manufacturing, Cubic Boron Nitride (CBN) inserts are pivotal for their hardness and durability. However, post-production surface defects on these inserts can compromise product integrity and performance. This paper proposes an automated detection and classification system using machine vision to scrutinize these surface defects. By integrating an optical bracket, a high-resolution industrial camera, precise lighting, and an advanced development board, the system employs digital image processing to ascertain and categorize imperfections on CBN inserts. The methodology initiates with a high-definition image capture by the imaging platform, tailored for CBN insert inspection. A suite of defect detection algorithms undergoes comparative analysis to discern their efficacy, emphasizing the impact of algorithm parameters and dataset diversity on detection precision. The most effective algorithm is then encapsulated into a versatile application, ensuring compatibility with various operating systems. Empirical verification of the system shows that the detection accuracy of multiple defect types exceeds 90%, and the tooth surface recognition efficiency significantly reaches three frames per second, with the front and side cutting surfaces of the tool in each frame. This breakthrough indicates a scalable, reliable solution for automatically detecting and classifying surface defects on CBN inserts, paving the way for enhanced quality control in automated, high-speed production lines.
在精密制造这个高风险领域，立方氮化硼（CBN）刀片因其硬度和耐用性而至关重要。然而，这些刀片生产后的表面缺陷会损害产品的完整性和性能。本文提出了一种使用机器视觉的自动检测和分类系统，以仔细检查这些表面缺陷。通过集成一个光学支架、一台高分辨率工业相机、精确照明和一块先进的开发板，该系统利用数字图像处理来确定和分类CBN刀片上的缺陷。该方法首先由成像平台针对CBN刀片检测进行高清图像采集。对一套缺陷检测算法进行比较分析以辨别其功效，强调算法参数和数据集多样性对检测精度的影响。然后将最有效的算法封装到一个通用应用程序中，确保与各种操作系统兼容。该系统的实证验证表明，多种缺陷类型的检测准确率超过90%，齿面识别效率显著达到每秒三帧，且每帧中都有刀具的前刀面和侧刀面。这一突破表明了一种可扩展、可靠的解决方案，用于自动检测和分类CBN刀片上的表面缺陷，为自动化高速生产线中加强质量控制铺平了道路。
