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Real Time Surface Angle Measurement of Dental Implants

Year 2020, Ejosat Special Issue 2020 (ARACONF), 130 - 136, 01.04.2020
https://doi.org/10.31590/ejosat.araconf17

Abstract

Dental implants and related product family are very important for jaw health. Dental implants are generally produced by titanium materials. Since titanium based alloys have high mechanical strength, quality control of these products is important to prevent possible defective products that may arise during CNC based biomaterial production processes. In addition, these parts are produced by a high number which are almost impossible to control visually. Machine vision prevents of human based errors in automated production. Presented system detects length and angle defects for dental products using image processing methods. The state of art of this work is developing machine vision based real time dental implant defection system design. Gaussian blur filtering and Otsu thresholding were used for detecting the titanium dental product in gray scaled images. Root mean squared linear regression model is applied to preprocessed images to detect the line of the product. The angle between the two lines is also calculated by trigonometric methods. As the result of the presented algorithm, maximum 0.0015 mm and 0.013° absolute deviations are observed for length and angle measurements, respectively. Presented measurement algorithms for dental implants show that these fast algorithms give feasible results for mass production lines.

Thanks

This study was performed at Gazi University, Engineering Faculty, Electrical & Electronics Engineering Department. Special thanks to Bilimplant and Karakamlar for providing dental materials and for providing mechanical and electronic infrastructure, respectively. This article was presented in ARACONF 2020 and published in full text.

References

  • Sing, S. L., An, J., Yeong, W. Y., & Wiria, F. E. (2016). Laser and electron‐beam powder‐bed additive manufacturing of metallic implants: A review on processes, materials and designs. Journal of Orthopaedic Research, 34(3), 369-385.
  • Park, J. W., Park, K. B., & Suh, J. Y. (2007). Effects of calcium ion incorporation on bone healing of Ti6Al4V alloy implants in rabbit tibiae. Biomaterials, 28(22), 3306-3313.
  • Pattanayak, D. K., Fukuda, A., Matsushita, T., Takemoto, M., Fujibayashi, S., Sasaki, K., ... & Kokubo, T. (2011). Bioactive Ti metal analogous to human cancellous bone: fabrication by selective laser melting and chemical treatments. Acta Biomaterialia, 7(3), 1398-1406.
  • Abe, F., Costa Santos, E., Kitamura, Y., Osakada, K., & Shiomi, M. (2003). Influence of forming conditions on the titanium model in rapid prototyping with the selective laser melting process. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 217(1), 119-126.
  • Atzlesberger, J., Zagar, B. G., Cihal, R., Brummayer, M., & Reisinger, P. (2013). Sub-surface defect detection in a steel sheet. Measurement Science and Technology, 24(8), 084003.
  • Li, Q., & Ren, S. (2012). A visual detection system for rail surface defects. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(6), 1531-1542.
  • Ng, T. W. (2007). Optical inspection of ball bearing defects. Measurement Science and Technology, 18(9), N73.
  • Yuzhen, M., Xuan, S., Guoping, L., & Xinjua, W. (2013, May). Surface defect detection based on capacitive probe for bearing ball. In 2013 25th Chinese Control and Decision Conference (CCDC) (pp. 2037-2040). IEEE.
  • Fischer, H., Karaca, F., & Marx, R. (2002). Detection of microscopic cracks in dental ceramic materials by fluorescent penetrant method. Journal of Biomedical Materials Research: An Official Journal of The Society for Biomaterials, The Japanese Society for Biomaterials, and The Australian Society for Biomaterials and the Korean Society for Biomaterials, 61(1), 153-158.
  • Kim, R. W., Kim, H. S., Choe, H. C., Son, M. K., & Chung, C. H. (2011). Microscopic analysis of fractured dental implant surface after clinical UseR. Procedia Engineering, 10, 1955-1960.
  • Brief, J., Edinger, D., Hassfeld, S., & Eggers, G. (2005). Accuracy of image‐guided implantology. Clinical Oral Implants Research, 16(4), 495-501.
  • Flusser, J., Farokhi, S., Höschl, C., Suk, T., Zitová, B., & Pedone, M. (2015). Recognition of images degraded by Gaussian blur. IEEE transactions on Image Processing, 25(2), 790-806.
  • T. Y. Goh, S. N. Basah, H. Yazid, M. J. Aziz Safar, and F. S. Ahmad Saad, “Performance analysis of image thresholding: Otsu technique,” Meas. J. Int. Meas. Confed., vol. 114, pp. 298–307, Jan. 2018.
  • Goh, T. Y., Basah, S. N., Yazid, H., Safar, M. J. A., & Saad, F. S. A. (2018). Performance analysis of image thresholding: Otsu technique. Measurement, 114, 298-307.
  • Haddad, R. A., & Akansu, A. N. (1991). A class of fast Gaussian binomial filters for speech and image processing. IEEE Transactions on Signal Processing, 39(3), 723-727.
  • Lin, H. D. (2007). Computer-aided visual inspection of surface defects in ceramic capacitor chips. Journal of Materials Processing Technology, 189(1-3), 19-25.
  • Yuan, X. C., Wu, L. S., & Peng, Q. (2015). An improved Otsu method using the weighted object variance for defect detection. Applied Surface Science, 349, 472-484.
  • He, Z., & Sun, L. (2015). Surface defect detection method for glass substrate using improved Otsu segmentation. Applied optics, 54(33), 9823-9830.
  • Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics, 9(1), 62-66.
  • Han, Y., Wu, Y., Cao, D., & Yun, P. (2017). Defect detection on button surfaces with the weighted least-squares model. Frontiers of Optoelectronics, 10(2), 151-159.
  • Liang, Z., Cao, S., & Tan, Y. (2019, April). Defect detection and recognition based on ADABOOT-SVM integrated model. In Journal of Physics: Conference Series (Vol. 1187, No. 4, p. 042025). IOP Publishing.

Diş İmplantların Gerçek Zamanlı Yüzey Açısı Ölçümü

Year 2020, Ejosat Special Issue 2020 (ARACONF), 130 - 136, 01.04.2020
https://doi.org/10.31590/ejosat.araconf17

Abstract

Diş implantları ve ilgili ürün ailesi çene sağlığı için çok önemlidir. Dental implantlar genellikle titanyum malzemelerden üretilir. Titanyum esaslı alaşımlar yüksek mekanik mukavemete sahip olduklarından, bu ürünlerin kalite kontrolü, CNC bazlı biyomateryal üretim süreçlerinde ortaya çıkabilecek olası kusurlu ürünleri önlemek için önemlidir. Ek olarak, bu parçalar görsel olarak kontrol edilmesi neredeyse imkansız olan yüksek sayıda üretilir. Yapay görme, otomatik üretimdeki insan kaynaklı hataları önler. Sunulan sistem, görüntü işleme yöntemlerini kullanarak dental ürünler için uzunluk ve açı kusurlarını tespit etmektedir. Bu çalışmanın özgün yönü, makine görmesine dayalı gerçek zamanlı dental implant hatalı ürün tespiti sistemi tasarımı geliştirilmesidir. Gri ölçekli görüntülerde titanyum dental ürünün saptanması için Gauss bulanıklaştırma filtresi ve Otsu eşiği kullanılmıştır. Ürün hattını tespit etmek için önceden işlenmiş görüntülere ortalama hataların karesini içeren doğrusal regresyon modeli uygulanmıştır. İki çizgi arasındaki açı ise, trigonometrik yöntemlerle hesaplanır. Sunulan algoritmanın sonucu olarak uzunluk ve açı ölçümleri için sırasıyla maksimum 0.0015 mm ve 0.013° mutlak sapmalar gözlenmiştir. Dental implantlar için sunulan ölçüm algoritmaları, bu hızlı algoritmaların seri üretim hatları için uygun sonuçlar verdiğini göstermektedir.

References

  • Sing, S. L., An, J., Yeong, W. Y., & Wiria, F. E. (2016). Laser and electron‐beam powder‐bed additive manufacturing of metallic implants: A review on processes, materials and designs. Journal of Orthopaedic Research, 34(3), 369-385.
  • Park, J. W., Park, K. B., & Suh, J. Y. (2007). Effects of calcium ion incorporation on bone healing of Ti6Al4V alloy implants in rabbit tibiae. Biomaterials, 28(22), 3306-3313.
  • Pattanayak, D. K., Fukuda, A., Matsushita, T., Takemoto, M., Fujibayashi, S., Sasaki, K., ... & Kokubo, T. (2011). Bioactive Ti metal analogous to human cancellous bone: fabrication by selective laser melting and chemical treatments. Acta Biomaterialia, 7(3), 1398-1406.
  • Abe, F., Costa Santos, E., Kitamura, Y., Osakada, K., & Shiomi, M. (2003). Influence of forming conditions on the titanium model in rapid prototyping with the selective laser melting process. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 217(1), 119-126.
  • Atzlesberger, J., Zagar, B. G., Cihal, R., Brummayer, M., & Reisinger, P. (2013). Sub-surface defect detection in a steel sheet. Measurement Science and Technology, 24(8), 084003.
  • Li, Q., & Ren, S. (2012). A visual detection system for rail surface defects. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(6), 1531-1542.
  • Ng, T. W. (2007). Optical inspection of ball bearing defects. Measurement Science and Technology, 18(9), N73.
  • Yuzhen, M., Xuan, S., Guoping, L., & Xinjua, W. (2013, May). Surface defect detection based on capacitive probe for bearing ball. In 2013 25th Chinese Control and Decision Conference (CCDC) (pp. 2037-2040). IEEE.
  • Fischer, H., Karaca, F., & Marx, R. (2002). Detection of microscopic cracks in dental ceramic materials by fluorescent penetrant method. Journal of Biomedical Materials Research: An Official Journal of The Society for Biomaterials, The Japanese Society for Biomaterials, and The Australian Society for Biomaterials and the Korean Society for Biomaterials, 61(1), 153-158.
  • Kim, R. W., Kim, H. S., Choe, H. C., Son, M. K., & Chung, C. H. (2011). Microscopic analysis of fractured dental implant surface after clinical UseR. Procedia Engineering, 10, 1955-1960.
  • Brief, J., Edinger, D., Hassfeld, S., & Eggers, G. (2005). Accuracy of image‐guided implantology. Clinical Oral Implants Research, 16(4), 495-501.
  • Flusser, J., Farokhi, S., Höschl, C., Suk, T., Zitová, B., & Pedone, M. (2015). Recognition of images degraded by Gaussian blur. IEEE transactions on Image Processing, 25(2), 790-806.
  • T. Y. Goh, S. N. Basah, H. Yazid, M. J. Aziz Safar, and F. S. Ahmad Saad, “Performance analysis of image thresholding: Otsu technique,” Meas. J. Int. Meas. Confed., vol. 114, pp. 298–307, Jan. 2018.
  • Goh, T. Y., Basah, S. N., Yazid, H., Safar, M. J. A., & Saad, F. S. A. (2018). Performance analysis of image thresholding: Otsu technique. Measurement, 114, 298-307.
  • Haddad, R. A., & Akansu, A. N. (1991). A class of fast Gaussian binomial filters for speech and image processing. IEEE Transactions on Signal Processing, 39(3), 723-727.
  • Lin, H. D. (2007). Computer-aided visual inspection of surface defects in ceramic capacitor chips. Journal of Materials Processing Technology, 189(1-3), 19-25.
  • Yuan, X. C., Wu, L. S., & Peng, Q. (2015). An improved Otsu method using the weighted object variance for defect detection. Applied Surface Science, 349, 472-484.
  • He, Z., & Sun, L. (2015). Surface defect detection method for glass substrate using improved Otsu segmentation. Applied optics, 54(33), 9823-9830.
  • Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics, 9(1), 62-66.
  • Han, Y., Wu, Y., Cao, D., & Yun, P. (2017). Defect detection on button surfaces with the weighted least-squares model. Frontiers of Optoelectronics, 10(2), 151-159.
  • Liang, Z., Cao, S., & Tan, Y. (2019, April). Defect detection and recognition based on ADABOOT-SVM integrated model. In Journal of Physics: Conference Series (Vol. 1187, No. 4, p. 042025). IOP Publishing.
There are 21 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Uğurkan Kutbay 0000-0003-2167-9107

Publication Date April 1, 2020
Published in Issue Year 2020 Ejosat Special Issue 2020 (ARACONF)

Cite

APA Kutbay, U. (2020). Real Time Surface Angle Measurement of Dental Implants. Avrupa Bilim Ve Teknoloji Dergisi130-136. https://doi.org/10.31590/ejosat.araconf17