Manufacturing Science and Technology(CEASE PUBLICATION) Vol. 4(2), pp. 25 - 33
DOI: 10.13189/mst.2017.040202
Reprint (PDF) (676Kb)

Observing Tool Bit Conditions in Turning Process through OpenCV Programming Function

Ahmad Yusairi Bani Hashim ^*, Muhammad Amirul Amin Suaadi
Department of Robotics and Automation, Faculty of Manufacturing Engineering, Technical University of Malaysia Malacca, Melaka, Malaysia

ABSTRACT

Machine vision provides flexibility and further automation options to manufacturers. It helps detect product defects, complete a different number of tasks efficiently. The goal of this work was to develop a machine vision program that would monitor the state of the tool bit state and signals it through a software pop-up when the tool bit exhibited anomaly while cutting. The program would sense when the tool bit shows an anomaly of its sharpness while cutting. Canny algorithm and thresholding were the techniques to perform video analysis. Canny edge detector algorithm was used to change the RGB image to a grayscale-based image. The image would display only the object's edges so that the tool bit's sharp edge was visible and was analyzable for tool wear condition. The monitoring of the tool wear condition using OpenCV programming functions was proven workable. Further codes enhancements are needed to provide an effective program and to produce reliable results.

KEYWORDS
Tool Bit, Turning Process, Tool Wear, OpenCV, Canny Edge Detector, Python

Cite This Paper in IEEE or APA Citation Styles
(a). IEEE Format:
[1] Ahmad Yusairi Bani Hashim , Muhammad Amirul Amin Suaadi , "Observing Tool Bit Conditions in Turning Process through OpenCV Programming Function," Manufacturing Science and Technology(CEASE PUBLICATION), Vol. 4, No. 2, pp. 25 - 33, 2017. DOI: 10.13189/mst.2017.040202.

(b). APA Format:
Ahmad Yusairi Bani Hashim , Muhammad Amirul Amin Suaadi (2017). Observing Tool Bit Conditions in Turning Process through OpenCV Programming Function. Manufacturing Science and Technology(CEASE PUBLICATION), 4(2), 25 - 33. DOI: 10.13189/mst.2017.040202.