BTA深孔钻磨损检测识别及应用
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摘要
BTA深孔钻是一种典型的深孔加工内排屑钻头,加工用量大加工过程封闭。针对工业现场BTA深孔钻磨损人工检测误差大的不足,基于最小外接矩形的磨损视觉检测方法对BTA深孔钻的磨损进行了检测。通过对刀具磨损图像进行图像预处理,包括图像的灰度化、滤波去噪处理以及阈值分割,得到了清晰的磨损区域图像。将预处理图像应用最小外接矩形方法得到刀具的磨损量,分析了磨损量对加工孔径的影响。采用上述检测方法对BTA深孔钻进行了试验分析,为提高刀具的使用寿命提供数据支持。
BAT deep-hole drill is a typical inner-chip removal drill,which is processed in a large amount and the machining process is closed. In view of the deficiency of the industrial field BTA deep hole drilling wear artificial detection error,the wear of BTA deep hole drilling was detected based on the wear visual inspection method of the minimum external rectangle. First,getting a clear image of the wear area through the tool wear image preprocessing which include image gray,filter denoising and threshold segmentation; Then the tool wear was obtained by using the minimum external rectangle method in the preprocessed image,and the effect of the wear rate on the processing aperture was analyzed; The above detection method is used to test the BTA deep hole drilling,which provides data support for improving the service life of the tool.
BAT deep-hole drill is a typical inner-chip removal drill,which is processed in a large amount and the machining process is closed. In view of the deficiency of the industrial field BTA deep hole drilling wear artificial detection error,the wear of BTA deep hole drilling was detected based on the wear visual inspection method of the minimum external rectangle. First,getting a clear image of the wear area through the tool wear image preprocessing which include image gray,filter denoising and threshold segmentation; Then the tool wear was obtained by using the minimum external rectangle method in the preprocessed image,and the effect of the wear rate on the processing aperture was analyzed; The above detection method is used to test the BTA deep hole drilling,which provides data support for improving the service life of the tool.
引文
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[2]王世清.深孔加工技术[M].西安:西北工业大学出版社,2003:7-9.
[3]WANG Zhongren,ZOU Yufeng,ZHANG Fan.A Machine Vision Approach to Tool Wear Monitoring Based on the Image of Workpiece Surface Texture[J].Advanced Materials Research,2010(154/155):412-416.
[4]ZHOU Junhong,CHEEK.Tool Wear Monitoring Using Acoustice Missions by Dominant-feature Identification[J].Instrumentation and Measuremwnt,2010,60(2):547-559.
[5]王明,高东方.基于振动信号的铣刀磨损状态识别[J].制造业自动化,2010,32(11):96-99.
[6]姜宇,杨国辉.基于光栅投影技术的刀具磨损三维特征提取方法[J].光学精密工程,2007,15(3):390-395.
[7]贾冰慧,全燕鸣,朱正伟.面向刀具磨损在机检测的机器视觉系统[J].中国测试,2014,40(6):60-63.
[8]KASSIM A A,MANNAN M A,MIAN Z.Texture Analysis Methods for Tool Condition Monitoring[J].Image and Vision Computing,2007,25(7):1080-1090.
[9]冯冀宁,刘彬,任玉艳.基于细胞神经网络刀具磨损图像处理的研究[J].微计算机信息,2003,19(6):92-93.
[10]廖玉松,韩江.基于像素分布特征的图像去噪法在刀具磨损检测中的应用[J].机械设计,2015(6):93-98.
[11]舒平生.基于分层图像采集和三维重建的刀具磨损检测方法[J].制造技术与机床,2015(4):149-153.
[12]朱爱斌,胡浩强,何大勇,等.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,2015,49(5):82-86.
[13]尤海燕,杨树财,吴雪峰.BTA深孔钻设计技术[J].机械设计与研究,2014,10(5):110-113.
[14]蒋理科,祝益军,冯炎清,等.难加工材料刀具磨损检测技术研究与应用[J].航空制造技术,2010(22):59-63.
[15]张吉林.基于机器视觉的铣削刀具磨损监测技术研究[D].南京:南京航空航天大学,2013.
[16]LOIZOU J,TIAN W,ROBERTSON J,et al.Automated Wear Characterization for Broaching Tools Based on Machine Vision Systems[J].North American Manufacturing Research,2015(37):558-563.
[17](美)冈萨雷斯等著.数值图像处理[M].北京:电子工业出版社,2003:183-189.
[18]张铮,倪红霞,苑春苗,等.精通MATLAB数字图像处理与识别[M].人民邮电出版社,2013:42-106.
[19]靳雁霞.数字图像的灰度处理[C]//中国兵工学会测试技术学术年会,2004:97-100.
[20]王志欣,田学民.太阳能电池缺陷检测图像的分割技术[J].电子设计工程,2014,22(10):68-70.
[21]戴艳芳,宋志刚.一种由Mat Lab实现的图像分割改进算法[J].微处理机,2014(5):31-33.
[22]程鹏飞,闫浩文,韩振辉.一个求解多边形最小面积外接矩形的算法[J].工程图学学报,2008,29(1):122-126.
[23]李洋,李岳阳.一种快速提取植物叶片最小外接矩形的算法[J].江南大学学报(自然科学版),2015,14(3):273-277.