Assessment and visualisation of machine tool wear using computer vision

被引:79
作者
Kerr, D [1 ]
Pengilley, J [1 ]
Garwood, R [1 ]
机构
[1] Loughborough Univ Technol, Sch Mech & Mfg Engn, Loughborough LE11 3TU, Leics, England
关键词
computer vision; image processing; texture measurement; tool wear monitoring;
D O I
10.1007/s00170-004-2420-0
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Tool wear monitoring is an integral part of modern CNC machine control. Cutting tools must be periodically checked for possible or actual premature failures, and it is necessary to record the cutting history for a tool's full life of utilisation. This means that an on-line monitoring system would be of great benefit to overall process control in manufacturing systems. Computer vision has already shown promise as a candidate technology for this task. In this paper, we describe the use of digital image processing techniques in the analysis of images of worn cutting tools in order to assess their degree of wear and thus remaining useful life. It is shown that a processing strategy using a variety of image texture measures allows for effective visualisation and assessment of tool wear, and indicates good correlation with the expected wear characteristics.
引用
收藏
页码:781 / 791
页数:11
相关论文
共 29 条
[1]  
[Anonymous], 1993, 3685 ISO
[2]   Texture characterization of digital images which have a periodicity or a quasi-periodicity [J].
Bonetto, RD ;
Forlerer, E ;
Ladaga, JL .
MEASUREMENT SCIENCE AND TECHNOLOGY, 2002, 13 (09) :1458-1466
[3]   Surface texture indicators of tool wear - A machine vision approach [J].
Bradley, C ;
Wong, YS .
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2001, 17 (06) :435-443
[4]   New range-based neighbourhood operator for extracting edge and texture information from mammograms for subsequent image segmentation and analysis [J].
Chandrasekhar, R ;
Attikiouzel, Y .
IEE PROCEEDINGS-SCIENCE MEASUREMENT AND TECHNOLOGY, 2000, 147 (06) :408-413
[5]  
DeGarmo E.P., 1988, Materials and processes in manufacturing, V7th
[6]   The correlation of vibration signal features to cutting tool wear in a metal turning operation [J].
Dimla Snr. D.E. .
International Journal of Advanced Manufacturing Technology, 2002, 19 (10) :705-713
[7]   Drill wear monitoring using cutting force signals [J].
Ertunc, HM ;
Oysu, C .
MECHATRONICS, 2004, 14 (05) :533-548
[8]  
GIUSTI F, 1984, ANN CIRP, V33, P229
[9]   TEXTURAL FEATURES FOR IMAGE CLASSIFICATION [J].
HARALICK, RM ;
SHANMUGAM, K ;
DINSTEIN, I .
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS, 1973, SMC3 (06) :610-621
[10]   Surface defect detection with histogram-based texture features [J].
Iivarinen, J .
INTELLIGENT ROBOTS AND COMPUTER VISION XIX: ALGORITHMS, TECHNIQUES, AND ACTIVE VISION, 2000, 4197 :140-145