İşlenebilirlikte Kenar Belirleme Algoritmalarının Kullanılabilirliği

Year 2022, Volume: 5 Issue: 2, 707 - 719, 18.07.2022

Demet Zalaoğlu , Pinar Karakus

https://doi.org/10.47495/okufbed.1064594

Abstract

Talaşlı imalat işlemlerinde parça kalitesini etkileyen en önemli parametrelerden biri de işleme sırasında kullanılan kesici ucun aşınmasıdır. Elde edilen ürünlerin daha iyi yüzey kalitesi sahip olmaları için kesici ucun aşınma durumunu bilmek ve kullanılan takımın ömrünü en iyi şekilde tahmin etmek gerekmektedir. Bu amaç doğrultusunda yapılan çalışmada, işlemler sırasında otomatik olarak kesici takım aşınmasının takibine de izin veren görüntü işleme yöntemlerinden kenar belirleme algoritmalarının kullanılabilirliği incelenmiştir. Yapılan incelemeler sürecinde, Ø11, Ø5,5 ve Ø2,5 mm olmak üzere üç farklı boyutta aşınmamış durumda olan parmak frezelere ve kesici uçlara Canny, Prewitt, Sobel, Roberts, Log ve Zerocross algoritmaları uygulanmıştır. Elde edilen sonuçlar incelendiğinde, uçlara uygulanan tüm algoritmalara ait kenarların benzer olarak yakalandığı ve uçlara ait kenarların doğru bir şekilde belirlendiği gözlemlenmiştir.

Keywords

görüntü işleme, kenar belirleme, işlenebilirlik

The Availability of Edge Detection Algorithms in Machinability

Abstract

One of the most important parameters affecting the part quality in machining processes is the wear of the cutting tip used during operation. In order to have better surface quality of the obtained products, it is necessary to know the wear status of the cutting insert and to estimate the life of the tool used ideally. In the study done for this aim, the usability of edge detection algorithms, which is one of the image processing methods that allows tracking of cutting tool wear during operations, has been investigated. During the investigations, Canny, Prewitt, Sobel, Roberts, Log and Zerocross algorithms were applied to end mills and cutting tool insert, being in the unworn condition with three different sizes such as Ø11, Ø5.5 and Ø2.5 mm. When the results obtained are examined, it has been observed that the edges of all algorithms applied to the ends are captured similarly and the edges belonging to the ends are correctly determined. Since the edge detection algorithms used in the study are applied to a very small sized object and therefore work with small sized images, no difference was found between the performances of the edge detection algorithms to be obtained by visual interpretation.

Keywords

Image processing, Edge detection, Machineability

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