eprintid: 10449
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/04/49
datestamp: 2023-11-09 16:37:04
lastmod: 2023-11-09 16:37:04
status_changed: 2023-11-09 16:31:26
type: conference_item
metadata_visibility: show
creators_name: Sukeri, M.
creators_name: Paiz Ismadi, M.Z.
creators_name: Othman, A.R.
creators_name: Kamaruddin, S.
title: Wear Detection of Drill Bit by Image-based Technique
ispublished: pub
keywords: Bits; Condition monitoring; Drills; Edge detection; Image segmentation; Manufacture, Cross correlation methods; Direct measurement method; Edge detection methods; Image-based analysis; Image-based techniques; Manufacturing industries; Thresholding techniques; Tool condition monitoring, Cutting tools
note: 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
abstract: 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.
date: 2018
publisher: Institute of Physics Publishing
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044511220&doi=10.1088%2f1757-899X%2f328%2f1%2f012011&partnerID=40&md5=7901e84187f19bf7edf58ca8ee70856f
id_number: 10.1088/1757-899X/328/1/012011
full_text_status: none
publication: IOP Conference Series: Materials Science and Engineering
volume: 328
number: 1
refereed: TRUE
issn: 17578981
citation:   Sukeri, M. and Paiz Ismadi, M.Z. and Othman, A.R. and Kamaruddin, S.  (2018) Wear Detection of Drill Bit by Image-based Technique.  In: UNSPECIFIED.