针对目前难以在复杂恶劣的油污粉尘环境中实现对刀具图像的高质量采集和刀具磨损视觉特征的高精检测,对磨损缺失刀刃这一类最为典型且危害最大的刀具磨损开展研究,提出一种采用切削刃重构的刀具磨损视觉检测方法。首先,在数控机床加工台一侧搭建集成了一套具有镜头保护与清洁功能的图像采集装置,用于在机定期自动采集刀具磨损图像;然后,将采集的图像经以太网传输至计算机图像处理系统,利用设计的切削刃重构法对刀具磨损缺失区域进行切削刃重构,以此得到完整刀具图像,进而利用图像差分,将重构后的刀具图像与磨损刀具图像相减,实现刀具磨损缺失区域的自动识别;最后,基于识别的磨损特征测量刀具磨损的评估指标参数值,并判断是否需要换刀。实验结果表明:所提检测方法具有较大优势,解决了油污粉尘机加环境下刀具磨损图像采集困难的难题和难以从图像中分割识别刀具磨损缺失特征的难题,实现了刀具磨损的视觉高精高效检测;与现有的刀具磨损视觉检测系统以及现有的Canny边缘检测法、自适应阈值法等6种图像分割方法相比,所提方法避免了拆卸刀具进行离线显微镜检测和模板匹配的烦琐过程,可进行在机自动检测,同时平均检测准确率至少提升20%。
陈晓波,梁伟云,习俊通,吴卓琦.用于检测立铣刀磨损状态的正交视觉检测系统[P].上海交通大学,2012.
邓晓鹏,王妍,洪煜,胡小锋.采用自适应区域生长的微型钻铣刀具磨损检测方法[J].西安交通大学学报,2021(12).
李恒,叶祖坤,查文彬,王禹林.基于多传感器信息决策级融合的刀具磨损在线监测[J].兵工学报,2021(09).
周俊杰,余建波.基于机器视觉的加工刀具磨损量在线测量[J].上海交通大学学报,2021(06).
叶祖坤,李恒,查文彬,何彦,王禹林.采用局部阈值分割的刀具损伤视觉检测方法[J].西安交通大学学报,2021(04).
朱爱斌,何仁杰,吴玥璇,王凯.考虑变换域融合方法的刀具磨损区域三维重构[J].西安交通大学学报,2017(12).
朱爱斌,胡浩强,何大勇,陈渭.采用频域融合方法的砂轮刀具磨损三维重构技术[J].西安交通大学学报,2015(05).
秦国华,易鑫,李怡冉,谢文斌.刀具磨损的自动检测及检测系统[J].光学精密工程,2014(12).
陈保家,陈雪峰,何正嘉,李兵.利用运行状态信息的机床刀具可靠性预测方法[J].西安交通大学学报,2010(09).
Mark Albert.The Strategic Value of Machine Tool Flexibility[J].Modern Machine Shop,2022.
Ma Kaile,Wang Guofeng,Yang Kai,Hu Mantang,Li Jiefeng.Tool wear monitoring for cavity milling based on vibration singularity analysis and stacked LSTM[J].The International Journal of Advanced Manufacturing Technology,2022.
Wang Dashuang,Hong Rongjing,Lin Xiaochuan.A method for predicting hobbing tool wear based on CNC real-time monitoring data and deep learning[J].Precision Engineering,2021.
Zhi Lei,Qinsong Zhu,Yuqing Zhou,Bintao Sun,Weifang Sun,Xiaoming Pan.A GAPSO-Enhanced Extreme Learning Machine Method for Tool Wear Estimation in Milling Processes Based on Vibration Signals[J].International Journal of Precision Engineering and Manufacturing-Green Technology,2021.
Nilesh Dhobale,Sharad Mulik,R. Jegadeeshwaran,Abhishek Patange.Supervision of Milling Tool Inserts using Conventional and Artificial Intelligence Approach: A Review[J].Sound and Vibration,2021.
Tongshun Liu,Kunpeng Zhu,Gang Wang.Micro-milling tool wear monitoring under variable cutting parameters and runout using fast cutting force coefficient identification method[J].The International Journal of Advanced Manufacturing Technology,2020.
grid.16821.3c, 0000 0004 0368 8293, School of Mechanical Engineering, Shanghai Jiao Tong University, 200240, Shanghai, China,grid.16821.3c, 0000 0004 0368 8293, School of Mechanical Engineering, Shanghai Jiao Tong University, 200240, Shanghai, China,grid.7372.1, 0000 0000 8809 1613, Warwick Business School, University of Warwick, CV47AL, Coventry, UK.Detection of tool breakage during milling process through acoustic emission[J].The International Journal of Advanced Manufacturing Technology,2020.
G. Serin,B. Sener,A. M. Ozbayoglu,H. O. Unver.Review of tool condition monitoring in machining and opportunities for deep learning[J].The International Journal of Advanced Manufacturing Technology,2020.
Yifan Gao,Jeong Hoon Ko,Heow Pueh Lee.Meso-scale tool breakage prediction based on finite element stress analysis for shoulder milling of hardened steel[J].Journal of Manufacturing Processes,2020.
Ruitao Peng,Haolin Pang,Haojian Jiang,Yunbo Hu.Study of Tool Wear Monitoring Using Machine Vision[J].Automatic Control and Computer Sciences,2020.
Maryam Jamshidi,Xavier Rimpault,Marek Balazinski,Jean-François Chatelain.Fractal analysis implementation for tool wear monitoring based on cutting force signals during CFRP/titanium stack machining[J].The International Journal of Advanced Manufacturing Technology,2020.
Qiulin Hou,Jie Sun,Panling Huang.A novel algorithm for tool wear online inspection based on machine vision[J].The International Journal of Advanced Manufacturing Technology,2019.
Jansi S.,Subashini P..Optimized Adaptive Thresholding based Edge Detection Method for MRI Brain Images[J].International Journal of Computer Applications,2012.
Jilin Zhang,Chen Zhang,Song Guo,Laishui Zhou.Research on tool wear detection based on machine vision in end milling process[J].Production Engineering: Research and Development,2012.
W. K. Mook,H. H. Shahabi,M. M. Ratnam.Measurement of nose radius wear in turning tools from a single 2D image using machine vision[J].The International Journal of Advanced Manufacturing Technology,2009.
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