№81-15

Automation of the quality control process of dental implantation products using machine vision

O. Shulhin¹

¹Dnipro University of Technology, Dnipro, Ukraine

Coll.res.pap.nat.min.univ. 2025, 81:149–158

Full text (PDF)

Abstract

Purpose. The development of an automated quality control system for dental implantation products to eliminate the shortcomings of manual quality control, which include the need for increased personnel, high time consumption, the possibility of errors due to the human factor, and the difficulty of timely defect detection.

The methods. The study was carried out through an analysis of the dental implant manufacturing process, with particular attention to the quality control procedures. The main issues associated with the traditional approach to quality control of dental implants were examined. Statistical data were collected and an analysis of existing product defects was conducted, followed by a proposed classification of these defects. Additionally, the study included a review of existing machine vision solutions that can be applied in the field of defectoscopy.

Findings. The study proposes the use of machine vision technologies for the automated detection of surface defects in dental implants. The suggested solution enables effective identification of defects, including those currently detectable only through visual inspection. The study also identifies the shortcomings of manual quality control, analyzes and proposes a classification of dental implantation product defects, and compiles statistics on their occurrence in the manufacturing process.

The originality. Within the scope of this study, a classification of defects in dental implant products was proposed, which enables the identification of target categories for a machine vision system. Based on statistical analysis, a predominance of visual defects was established, confirming the feasibility of implementing automated quality control using machine vision technologies based on artificial intelligence. Approaches to adapting such technologies to the specifics of dental implant production have been further developed.

Practical implementation. The automated quality control system for dental implants may be implemented in manufacturing to enhance product quality. The introduction of the proposed system will accelerate the quality control process, improve its accuracy, eliminate errors related to the human factor, and ensure timely response and elimination of defect causes.

Keywords: automation, quality control, machine vision, dental implantation.

References

1. АВМ Technology. (n.d.). Vyrobnytstvo. https://abmt.com.ua/ua/texnologichni-proczesi/.

2. Honcharenko, M., Shemena, E., & Motronenko, V. (2022, traven 25–27). Metody vyhotovlennia stomatolohichnykh implantiv. Materialy mizhnarodnoi naukovo-tekhnichnoi konferentsii «Suchasni tekhnolohii biomedychnoi inzhenerii» (s. 165–167). Odesa.

3. Adzhamskyi, S. V., Kononenko, H. A., & Podolskyi, R. V. (2021). Zastosuvannia SLM-tekhnolohii dlia vyhotovlennia dentalnykh implantiv zi splavu Ti–6Al–4V. Avtomatychne zvariuvannia, (11), 21–27.

4. Ningbo Easson Photoelectric Technology Co., Ltd. (n.d.). EP-1 2515 (reverse) optical projector. http://en.easson.net.cn/product/72.html.

5. Ningbo Easson Photoelectric Technology Co., Ltd. (n.d.). ATM-3020VT Automatic high-precision two-dimensional image measuring instrument. http://en.easson.net.cn/product/24.html.

6. Kvashuk, D., Gustera, O., & Yunashov, I. (2020). Identification of plant diseases in agriculture using machine vision. Investytsiyi: Praktyka Ta Dosvid, 4, 60–64. https://doi.org/10.32702/2306-6814.2020.4.60

7. Tsvirkun, L. O., Omelchenko, O. V., Tsvirkun, S. L., Shkilna, Yu. S., & Shylin, A. S. (2023). Avtomatyzovane upravlinnia protsesom sortuvannia kartopli z vykorystanniam systemy mashynnoho zoru na osnovi analizu kharakterystyk mekhanichnoho rukhu syrovyny. Obladnannia ta tekhnolohii kharchovykh vyrobnytstv, 46(1), 53–60.

8. Puida, V. Ya. (2022). Systema tekhnichnoho zoru dlia doslidzhen v oblasti defektoskopii materialiv ta vyrobiv. Computer Systems and Networks, 4(1), 122–130.