Zhang Xianghui, Yu Haoyang, Li Chengchao, Yu Zhanjiang, Xu Jinkai, Li Yiquan, Yu Huadong
Ministry of Education Key Laboratory for Cross-Scale Micro and Nano Manufacturing, Changchun University of Science and Technology, Changchun 130022, China.
School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, China.
Micromachines (Basel). 2022 Dec 30;14(1):100. doi: 10.3390/mi14010100.
Most in situ tool wear monitoring methods during micro end milling rely on signals captured from the machining process to evaluate tool wear behavior; accurate positioning in the tool wear region and direct measurement of the level of wear are difficult to achieve. In this paper, an in situ monitoring system based on machine vision is designed and established to monitor tool wear behavior in micro end milling of titanium alloy Ti6Al4V. Meanwhile, types of tool wear zones during micro end milling are discussed and analyzed to obtain indicators for evaluating wear behavior. Aiming to measure such indicators, this study proposes image processing algorithms. Furthermore, the accuracy and reliability of these algorithms are verified by processing the template image of tool wear gathered during the experiment. Finally, a micro end milling experiment is performed with the verified micro end milling tool and the main wear type of the tool is understood via in-situ tool wear detection. Analyzing the measurement results of evaluation indicators of wear behavior shows the relationship between the level of wear and varying cutting time; it also gives the main influencing reasons that cause the change in each wear evaluation indicator.
大多数微端铣削过程中的原位刀具磨损监测方法依赖于从加工过程中捕获的信号来评估刀具磨损行为；在刀具磨损区域进行精确定位以及直接测量磨损程度很难实现。本文设计并建立了一种基于机器视觉的原位监测系统，用于监测钛合金Ti6Al4V微端铣削过程中的刀具磨损行为。同时，对微端铣削过程中刀具磨损区域的类型进行了讨论和分析，以获得评估磨损行为的指标。为了测量这些指标，本研究提出了图像处理算法。此外，通过处理实验过程中采集的刀具磨损模板图像，验证了这些算法的准确性和可靠性。最后，使用经过验证的微端铣刀进行了微端铣削实验，并通过原位刀具磨损检测了解了刀具的主要磨损类型。分析磨损行为评估指标的测量结果，揭示了磨损程度与切削时间变化之间的关系；还给出了导致各磨损评估指标变化的主要影响因素。
