Wei Wenming, Yin Jia, Zhang Jun, Zhang Huijie, Lu Zhuangzhuang
State Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
Materials (Basel). 2021 Sep 30;14(19):5690. doi: 10.3390/ma14195690.
Tool wear and breakage detection technologies are of vital importance for the development of automatic machining systems and improvement in machining quality and efficiency. The monitoring of integral spiral end milling cutters, however, has rarely been investigated due to their complex structures. In this paper, an image acquisition system and image processing methods are developed for the wear and breakage detection of milling cutters based on machine vision. The image acquisition system is composed of three light sources and two cameras mounted on a moving frame, which renders the system applicable in cutters of different dimensions and shapes. The images captured by the acquisition system are then preprocessed with denoising and contrast enhancing operations. The failure regions on the rake face, flank face and tool tip of the cutter are extracted with the Otsu thresholding method and the Markov Random Field image segmentation method afterwards. Eventually, the feasibility of the proposed image acquisition system and image processing methods is demonstrated through an experiment of titanium alloy machining. The proposed image acquisition system and image processing methods not only provide high quality detection of the integral spiral end milling cutter but can also be easily converted to detect other cutting systems with complex structures.
刀具磨损和破损检测技术对于自动加工系统的发展以及加工质量和效率的提高至关重要。然而，由于整体螺旋立铣刀结构复杂，对其监测的研究很少。本文基于机器视觉开发了一种用于铣刀磨损和破损检测的图像采集系统及图像处理方法。该图像采集系统由三个光源和安装在移动框架上的两个相机组成，这使得该系统适用于不同尺寸和形状的刀具。采集系统捕获的图像随后经过去噪和对比度增强操作进行预处理。之后采用大津阈值法和马尔可夫随机场图像分割法提取刀具前刀面、后刀面和刀尖上的失效区域。最终，通过钛合金加工实验验证了所提出的图像采集系统和图像处理方法的可行性。所提出的图像采集系统和图像处理方法不仅能对整体螺旋立铣刀进行高质量检测，还能轻松转换用于检测其他结构复杂的切削系统。
