Micro-milling is an extensively used micro-machining process for producing high precision 3D components from varied materials. However, tool wear in micro-tools is a big concern, as component accuracy directly depends on it. Also, size effects limit the monitoring by the naked eye, but it can be compensated by implying a proper wear monitoring mechanism. Various direct and indirect methods have earlier been used for monitoring purposes, and considering the needs of the fourth industrial revolution, one of the direct methods, machine vision, when combined with image processing algorithms, can play a more prominent role. Current work focuses on creating a wear monitoring algorithm based on fuzzy c-means clustering technique directly implied on acquired colour micro-tool images. The proposed algorithm has three steps: the first step is Region of Interest (ROI) extraction, where the background is removed, orientation correction is done, and ROI on each tooth is extracted from micro-tool colour images. The second uses the fuzzy c-means technique on ROI to cluster them, from which wear cluster is chosen and morphologically enhanced. The last step performs pixel level measurement and results in numerical wear width. Overall, quantitative results at each step are correlation coefficient of 99 % after image registration, segmentation accuracy of 92 % and wear measurement accuracy of 97 %. A comparison is also made between the proposed algorithm, k-means clustering and RGB thresholding technique, where the proposed algorithm outshines. Lastly, the wear measurement error of the proposed algorithm is less than 5 %, indicating its repeatable, reliable, and robust nature.

微铣削是一种广泛使用的微加工工艺，用于从各种材料生产高精度 3D 部件。然而，微型刀具的刀具磨损是一个大问题，因为零件精度直接取决于它。此外，尺寸效应限制了肉眼的监测，但可以通过暗示适当的磨损监测机制来补偿。各种直接和间接方法早先被用于监控目的，考虑到第四次工业革命的需要，直接方法之一，机器视觉，当结合图像处理算法时，可以发挥更突出的作用。目前的工作重点是创建一种基于模糊 c 均值聚类技术的磨损监测算法，直接隐含在获取的彩色微工具图像上。所提出的算法分为三个步骤：第一步是感兴趣区域（ROI）提取，其中去除背景，完成方向校正，并从微型工具彩色图像中提取每颗牙齿的 ROI。第二种在 ROI 上使用模糊 c 均值技术对它们进行聚类，从中选择磨损聚类并对其进行形态增强。最后一步执行像素级测量并得出数值磨损宽度。总体而言，每一步的定量结果是图像配准后相关系数为 99%，分割精度为 92%，磨损测量精度为 97%。还对所提出的算法、k-means 聚类和 RGB 阈值技术进行了比较，其中所提出的算法优于其他算法。最后，所提出算法的磨损测量误差小于 5%，表明其具有可重复性、可靠性和鲁棒性。