Özet:

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.

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