In order to inspect the condition of micro milling cutter automatically and accurately in the online process, a dedicated micro milling cutter condition inspection system was established in this paper, which can effectively inspect micro cutter condition from both radial and axial direction. The key methods—the automatic dimension measurement and the fusion method for compositing all-in-focus cutting edge image of micro milling cutters—are studied. The experiments verify that the proposed methods and the developed inspection system can fulfill the needs of industrial applications.

References

References
1.
Li
,
J. X.
,
Hu
,
X. B.
, and
Yang
,
Y.
,
2007
, “
Development and Application of Micro Manufacturing Technology
,”
Mod. Mach.
,
4
, pp.
76
78
.
2.
Wang
,
C. C.
,
2014
, “
On-Site Tool Wear Detection Based on High Precision Computer Vision
,” M.S. thesis, Donghua University, Shanghai, China.
3.
Huo
,
D.
, and
Cheng
,
K.
,
2010
, “
Experimental Investigation on Micro Milling of Oxygen-Free, High-Conductivity Copper Using Tungsten Carbide, Chemistry Vapour Deposition, and Single-Crystal Diamond Micro Tools
,”
Proc. Inst. Mech. Eng. Part B
,
224
(
6
), pp.
995
1003
.
4.
Cheng
,
K.
, and
Huo
,
D.
,
2013
,
Micro-Cutting: Fundamentals and Applications
,
Wiley
,
Chichester, UK
, pp.
4
10
.
5.
Jia
,
B. H.
,
2014
, “
Key Technology Research of Tool Condition Detection On-Machine Based on Machine Vision
,” M.S. thesis, South China University of Technology, Guangzhou, China.
6.
Tansel
,
I.
,
Rodriguez
,
O.
,
Trujillo
,
M.
,
Paz
,
E.
, and
Li
,
W.
,
1998
, “
Micro-End-Milling—I: Wear and Breakage
,”
Int. J. Mach. Tools Manuf.
,
38
(
12
), pp.
1419
1436
.
7.
Hsieh
,
W. H.
,
Lu
,
M. C.
, and
Chiou
,
S. J.
,
2012
, “
Application of Backpropagation Neural Network for Spindle Vibration-Based Tool Wear Monitoring in Micro-Milling
,”
Int. J. Adv. Manuf. Technol.
,
61
(
1–4
), pp.
53
61
.
8.
Yan
,
H.
,
2016
, “
Research on Acoustic Emission Characteristics of the Tool Wear in Micro Milling
,” M.S. thesis, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
9.
BIG DAISHOWA, 2000, “
Non-Contact Tool Inspection Device
,” BIG DAISHOWA, Osaka, Japan, accessed Dec. 15, 2016, http://big-daishowa.co.jp/product_page/data_18_dyna-vision.php/
10.
MARPOSS, 2011, “
Non-Contact Visual Tool Setter
,” MARPOSS, Bentivoglio, Italy, accessed Dec. 15, 2016, https://www.marposs.com/eng/product/non-contact-visual-tool-setter
11.
Szydłowski
,
M.
,
Powałka
,
B.
,
Matuszak
,
M.
, and
Kochmański
,
P.
,
2016
, “
Machine Vision Micro-Milling Tool Wear Inspection by Image Reconstruction and Light Reflectance
,”
Precis. Eng.
,
44
, pp.
236
244
.
12.
Dai
,
Y. Q.
, and
Zhu
,
K. P.
,
2017
, “
A Machine Vision System for Micro-Milling Tool Condition Monitoring
,”
Precis. Eng.
,
52
, pp. 183–191.
13.
Cheng
,
X.
,
Wei
,
X. T.
,
Yang
,
X. H.
, and
Guo
,
Y. B.
,
2014
, “
Unified Criterion for Brittle-Ductile Transition in Mechanical Microcutting of Brittle Materials
,”
ASME J. Manuf. Sci. Eng.
,
136
(
5
), p.
051013
.
14.
Zhang
,
X.
,
Tsang
,
W. M.
, and
Yamazaki
,
K.
,
2013
, “
A Study on Automatic on-Machine Inspection System for 3D Modeling and Measurement of Cutting Tools
,”
J. Intell. Manuf.
,
24
(
1
), pp.
71
86
.
15.
Rosin
,
P. L.
,
1997
, “
Technique for Assessing Polygonal Approximations of Curves
,”
IEEE Trans. Pattern Anal. Mach. Intell.
,
19
(
6
), pp.
659
666
.
16.
Rosin
,
P. L.
,
2003
, “
Assessing the Behavior of Polygonal Approximation Algorithms
,”
Pattern Recognit.
,
36
(
2
), pp.
508
518
.
17.
Steger
,
C.
,
Ulrich
,
M.
, and
Wiedemann
,
C.
,
2008
,
Machine Vision Algorithms and Applications
,
Tsinghua University Press
,
Beijing, China
, pp.
250
255
.
18.
Gao
,
Z.
,
2007
, “
Study on the Accuracy and Stability of Auto-Focusing Function
,” M.S. thesis, Shandong University, Jinan, China.
19.
Eltoukhy
,
H. A.
, and
Kavusi
,
S.
,
2003
, “
A Computationally Efficient Algorithm for Multi Focus Image Reconstruction
,”
Proc. SPIE
,
5017
, pp.
332
341
.
20.
Liu
,
Z.
,
Tsukada
,
K.
,
Hanasaki
,
K.
,
Ho, Y. K.
, and
Dai, Y. P.
,
2001
, “
Image Fusion by Using Steerable Pyramid
,”
Pattern Recognit. Lett.
,
22
(
9
), pp.
929
939
.
You do not currently have access to this content.