Araştırma Makalesi
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İşlenebilirlikte Kenar Belirleme Algoritmalarının Kullanılabilirliği

Yıl 2022, Cilt: 5 Sayı: 2, 707 - 719, 18.07.2022
https://doi.org/10.47495/okufbed.1064594

Öz

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.

Kaynakça

  • Atl, A. V., Urhan, O., Erturk, S. ve Sonmez, M., 2005, Wear condition monitoring of cuting tool using image processing, Proceedings of the IEEE 13th Signal Processing and Communications Applications Conference, 2005., 84-86.
  • Bagga, P., Makhesana, M. ve Patel, K., 2021a, A novel approach of combined edge detection and segmentation for tool wear measurement in machining, Production Engineering, 15 (3), 519-533.
  • Bagga, P., Makhesana, M., Patel, K. ve Patel, K., 2021b, Tool wear monitoring in turning using image processing techniques, Materials Today: Proceedings, 44, 771-775.
  • Canny, J., 1986, A computational approach to edge detection, IEEE Transactions on pattern analysis and machine intelligence (6), 679-698.
  • Castejón, M., Alegre, E., Barreiro, J. ve Hernández, L., 2007, On-line tool wear monitoring using geometric descriptors from digital images, International Journal of Machine Tools and Manufacture, 47 (12-13), 1847-1853.
  • Coromant, S., 1994, Modern metal cutting: a practical handbook, English Edition, Sandvik Coromant, Sweden, I-III, 35-120. D’Addona, D. ve Teti, R., 2013, Image data processing via neural networks for tool wear prediction, Procedia Cirp, 12, 252-257.
  • Danesh, M. ve Khalili, K., 2015, Determination of tool wear in turning process using undecimated wavelet transform and textural features, Procedia Technology, 19, 98-105.
  • Dieter, G. E., 1988, Mechanical metallurgy.
  • Dutta, S., Pal, S., Mukhopadhyay, S. ve Sen, R., 2013, Application of digital image processing in tool condition monitoring: A review, CIRP Journal of Manufacturing Science and Technology, 6 (3), 212-232.
  • Fadare, D. ve Oni, A., 2009, Development and application of a machine vision system for measurement of tool wear. Ganesan, P. ve Sajiv, G., 2017, A comprehensive study of edge detection for image processing applications, 2017 international conference on innovations in information, embedded and communication systems (ICIIECS), 1-6.
  • Gonzalez, R. ve Woods, R., 2002, Digital Image Processing. 2nd edn Prentice Hall, New Jersey, 793.
  • Juneja, M. ve Sandhu, P. S., 2009, Performance evaluation of edge detection techniques for images in spatial domain, International journal of computer theory and Engineering, 1 (5), 614.
  • Kendall, L. A., 1998, Friction and wear of cutting tools and cutting tool materials, ASM handbook, 18, 609-620. Kurada, S. ve Bradley, C., 1997, A machine vision system for tool wear assessment, Tribology International, 30 (4), 295-304.
  • Lins, R. G., de Araujo, P. R. M. ve Corazzim, M., 2020, In-process machine vision monitoring of tool wear for Cyber-Physical Production Systems, Robotics and computer-integrated manufacturing, 61, 101859.
  • Maini, R. ve Aggarwal, H., 2009, Study and comparison of various image edge detection techniques, International journal of image processing (IJIP), 3 (1), 1-11.
  • Nadernejad, E., Sharifzadeh, S. ve Hassanpour, H., 2008, Edge detection techniques: Evaluations and comparisons, Applied Mathematical Sciences, 2 (31), 1507-1520.
  • Obikawa, T. ve Shinozuka, J., 2004, Monitoring of flank wear of coated tools in high speed machining with a neural network ART2, International Journal of Machine Tools and Manufacture, 44 (12-13), 1311-1318.
  • Önal, Y. E., 2018, Gürültüye Karşı Dayanıklı Bir Kenar Tespit Algoritması Geliştirilmesi.
  • Roberts, L. G., 1963, Machine perception of three-dimensional solids, Massachusetts Institute of Technology. Shrivakshan, G. ve Chandrasekar, C., 2012, A comparison of various edge detection techniques used in image processing, International Journal of Computer Science Issues (IJCSI), 9 (5), 269.
  • Sortino, M., 2003, Application of statistical filtering for optical detection of tool wear, International Journal of Machine Tools and Manufacture, 43 (5), 493-497.
  • Stephenson, D. ve Agapiou, J., 2006, Metal Cutting Operations in Metal Cutting Theory and Practice, Taylor and Francis Group, Boca Raton, FL.
  • Thakre, A. A., Lad, A. V. ve Mala, K., 2019, Measurements of tool wear parameters using machine vision system, Modelling and Simulation in Engineering, 2019.
  • Yu, X., Lin, X., Dai, Y. ve Zhu, K., 2017, Image edge detection based tool condition monitoring with morphological component analysis, ISA transactions, 69, 315-322.
  • Zhang, C. ve Zhang, J., 2013, On-line tool wear measurement for ball-end milling cutter based on machine vision, Computers in industry, 64 (6), 708-719.
  • Zhang, M., Li, X., Yang, Z. ve Yang, Y., 2010, A novel zero-crossing edge detection method based on multi-scale space theory, IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS, 1036-1039.
  • url-1: https://www.nikon.com.tr/tr_TR/product/discontinued/digital-cameras/2015/d3100-black 27.01.2022

The Availability of Edge Detection Algorithms in Machinability

Yıl 2022, Cilt: 5 Sayı: 2, 707 - 719, 18.07.2022
https://doi.org/10.47495/okufbed.1064594

Öz

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.

Kaynakça

  • Atl, A. V., Urhan, O., Erturk, S. ve Sonmez, M., 2005, Wear condition monitoring of cuting tool using image processing, Proceedings of the IEEE 13th Signal Processing and Communications Applications Conference, 2005., 84-86.
  • Bagga, P., Makhesana, M. ve Patel, K., 2021a, A novel approach of combined edge detection and segmentation for tool wear measurement in machining, Production Engineering, 15 (3), 519-533.
  • Bagga, P., Makhesana, M., Patel, K. ve Patel, K., 2021b, Tool wear monitoring in turning using image processing techniques, Materials Today: Proceedings, 44, 771-775.
  • Canny, J., 1986, A computational approach to edge detection, IEEE Transactions on pattern analysis and machine intelligence (6), 679-698.
  • Castejón, M., Alegre, E., Barreiro, J. ve Hernández, L., 2007, On-line tool wear monitoring using geometric descriptors from digital images, International Journal of Machine Tools and Manufacture, 47 (12-13), 1847-1853.
  • Coromant, S., 1994, Modern metal cutting: a practical handbook, English Edition, Sandvik Coromant, Sweden, I-III, 35-120. D’Addona, D. ve Teti, R., 2013, Image data processing via neural networks for tool wear prediction, Procedia Cirp, 12, 252-257.
  • Danesh, M. ve Khalili, K., 2015, Determination of tool wear in turning process using undecimated wavelet transform and textural features, Procedia Technology, 19, 98-105.
  • Dieter, G. E., 1988, Mechanical metallurgy.
  • Dutta, S., Pal, S., Mukhopadhyay, S. ve Sen, R., 2013, Application of digital image processing in tool condition monitoring: A review, CIRP Journal of Manufacturing Science and Technology, 6 (3), 212-232.
  • Fadare, D. ve Oni, A., 2009, Development and application of a machine vision system for measurement of tool wear. Ganesan, P. ve Sajiv, G., 2017, A comprehensive study of edge detection for image processing applications, 2017 international conference on innovations in information, embedded and communication systems (ICIIECS), 1-6.
  • Gonzalez, R. ve Woods, R., 2002, Digital Image Processing. 2nd edn Prentice Hall, New Jersey, 793.
  • Juneja, M. ve Sandhu, P. S., 2009, Performance evaluation of edge detection techniques for images in spatial domain, International journal of computer theory and Engineering, 1 (5), 614.
  • Kendall, L. A., 1998, Friction and wear of cutting tools and cutting tool materials, ASM handbook, 18, 609-620. Kurada, S. ve Bradley, C., 1997, A machine vision system for tool wear assessment, Tribology International, 30 (4), 295-304.
  • Lins, R. G., de Araujo, P. R. M. ve Corazzim, M., 2020, In-process machine vision monitoring of tool wear for Cyber-Physical Production Systems, Robotics and computer-integrated manufacturing, 61, 101859.
  • Maini, R. ve Aggarwal, H., 2009, Study and comparison of various image edge detection techniques, International journal of image processing (IJIP), 3 (1), 1-11.
  • Nadernejad, E., Sharifzadeh, S. ve Hassanpour, H., 2008, Edge detection techniques: Evaluations and comparisons, Applied Mathematical Sciences, 2 (31), 1507-1520.
  • Obikawa, T. ve Shinozuka, J., 2004, Monitoring of flank wear of coated tools in high speed machining with a neural network ART2, International Journal of Machine Tools and Manufacture, 44 (12-13), 1311-1318.
  • Önal, Y. E., 2018, Gürültüye Karşı Dayanıklı Bir Kenar Tespit Algoritması Geliştirilmesi.
  • Roberts, L. G., 1963, Machine perception of three-dimensional solids, Massachusetts Institute of Technology. Shrivakshan, G. ve Chandrasekar, C., 2012, A comparison of various edge detection techniques used in image processing, International Journal of Computer Science Issues (IJCSI), 9 (5), 269.
  • Sortino, M., 2003, Application of statistical filtering for optical detection of tool wear, International Journal of Machine Tools and Manufacture, 43 (5), 493-497.
  • Stephenson, D. ve Agapiou, J., 2006, Metal Cutting Operations in Metal Cutting Theory and Practice, Taylor and Francis Group, Boca Raton, FL.
  • Thakre, A. A., Lad, A. V. ve Mala, K., 2019, Measurements of tool wear parameters using machine vision system, Modelling and Simulation in Engineering, 2019.
  • Yu, X., Lin, X., Dai, Y. ve Zhu, K., 2017, Image edge detection based tool condition monitoring with morphological component analysis, ISA transactions, 69, 315-322.
  • Zhang, C. ve Zhang, J., 2013, On-line tool wear measurement for ball-end milling cutter based on machine vision, Computers in industry, 64 (6), 708-719.
  • Zhang, M., Li, X., Yang, Z. ve Yang, Y., 2010, A novel zero-crossing edge detection method based on multi-scale space theory, IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS, 1036-1039.
  • url-1: https://www.nikon.com.tr/tr_TR/product/discontinued/digital-cameras/2015/d3100-black 27.01.2022
Toplam 26 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Araştırma Makaleleri (RESEARCH ARTICLES)
Yazarlar

Demet Zalaoğlu

Pinar Karakus

Yayımlanma Tarihi 18 Temmuz 2022
Gönderilme Tarihi 28 Ocak 2022
Kabul Tarihi 12 Nisan 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 5 Sayı: 2

Kaynak Göster

APA Zalaoğlu, D., & Karakus, P. (2022). İşlenebilirlikte Kenar Belirleme Algoritmalarının Kullanılabilirliği. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 5(2), 707-719. https://doi.org/10.47495/okufbed.1064594
AMA Zalaoğlu D, Karakus P. İşlenebilirlikte Kenar Belirleme Algoritmalarının Kullanılabilirliği. OKÜ Fen Bil. Ens. Dergisi ((OKU Journal of Nat. & App. Sci). Temmuz 2022;5(2):707-719. doi:10.47495/okufbed.1064594
Chicago Zalaoğlu, Demet, ve Pinar Karakus. “İşlenebilirlikte Kenar Belirleme Algoritmalarının Kullanılabilirliği”. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 5, sy. 2 (Temmuz 2022): 707-19. https://doi.org/10.47495/okufbed.1064594.
EndNote Zalaoğlu D, Karakus P (01 Temmuz 2022) İşlenebilirlikte Kenar Belirleme Algoritmalarının Kullanılabilirliği. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 5 2 707–719.
IEEE D. Zalaoğlu ve P. Karakus, “İşlenebilirlikte Kenar Belirleme Algoritmalarının Kullanılabilirliği”, OKÜ Fen Bil. Ens. Dergisi ((OKU Journal of Nat. & App. Sci), c. 5, sy. 2, ss. 707–719, 2022, doi: 10.47495/okufbed.1064594.
ISNAD Zalaoğlu, Demet - Karakus, Pinar. “İşlenebilirlikte Kenar Belirleme Algoritmalarının Kullanılabilirliği”. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 5/2 (Temmuz 2022), 707-719. https://doi.org/10.47495/okufbed.1064594.
JAMA Zalaoğlu D, Karakus P. İşlenebilirlikte Kenar Belirleme Algoritmalarının Kullanılabilirliği. OKÜ Fen Bil. Ens. Dergisi ((OKU Journal of Nat. & App. Sci). 2022;5:707–719.
MLA Zalaoğlu, Demet ve Pinar Karakus. “İşlenebilirlikte Kenar Belirleme Algoritmalarının Kullanılabilirliği”. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, c. 5, sy. 2, 2022, ss. 707-19, doi:10.47495/okufbed.1064594.
Vancouver Zalaoğlu D, Karakus P. İşlenebilirlikte Kenar Belirleme Algoritmalarının Kullanılabilirliği. OKÜ Fen Bil. Ens. Dergisi ((OKU Journal of Nat. & App. Sci). 2022;5(2):707-19.

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