TY  - CONF
VL  - 328
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044511220&doi=10.1088%2f1757-899X%2f328%2f1%2f012011&partnerID=40&md5=7901e84187f19bf7edf58ca8ee70856f
A1  - Sukeri, M.
A1  - Paiz Ismadi, M.Z.
A1  - Othman, A.R.
A1  - Kamaruddin, S.
SN  - 17578981
PB  - Institute of Physics Publishing
Y1  - 2018///
KW  - Bits; Condition monitoring; Drills; Edge detection; Image segmentation; Manufacture
KW  -  Cross correlation methods; Direct measurement method; Edge detection methods; Image-based analysis; Image-based techniques; Manufacturing industries; Thresholding techniques; Tool condition monitoring
KW  -  Cutting tools
TI  - Wear Detection of Drill Bit by Image-based Technique
ID  - scholars10449
N1  - cited By 5; Conference of 3rd International Conference on Mechanical, Manufacturing and Process Plant Engineering 2017, ICMMPE 2017 ; Conference Date: 22 November 2017 Through 23 November 2017; Conference Code:135320
N2  - Image processing for computer vision function plays an essential aspect in the manufacturing industries for the tool condition monitoring. This study proposes a dependable direct measurement method to measure the tool wear using image-based analysis. Segmentation and thresholding technique were used as the means to filter and convert the colour image to binary datasets. Then, the edge detection method was applied to characterize the edge of the drill bit. By using cross-correlation method, the edges of original and worn drill bits were correlated to each other. Cross-correlation graphs were able to detect the difference of the worn edge despite small difference between the graphs. Future development will focus on quantifying the worn profile as well as enhancing the sensitivity of the technique. © Published under licence by IOP Publishing Ltd.
AV  - none
ER  -