Study On Friction And Wear Characteristics Of Dental Materials And Natural Teeth

With the general application of dental restoration in the field of repairing dental hard tissue, the friction and wear behavior between dental restoration and human teeth or other dental restoration during occlusion and mastication have been increasingly prominent. The dissertation aimed to reveal the influence law of oral dynamic factors and environmental factors on the friction and wear characteristics between dental materials and human teeth by using a reciprocating friction and wear test machine. Meanwhile, to study the wear loss prediction model of dental materials and wear loss evaluating method of human teeth.Experiment researches were carried out to explore the influence law of oral dynamic factors and environmental factors on the friction and wear characteristics between dental materials and human teeth. The results indicated that a positive correlation between the wear loss of dental materials against human teeth and the oral dynamic factors including load, frequency and cycles was discovered. And the wear surface on TC4 alloy presented varying degrees of furrow characteristic, while the wear surface of zirconia ceramic presented Minor abrasion. What’s more, the human teeth which were grinded with zirconia only generated some tiny cracks. It could be deduced that the friction and wear characteristics of zirconia is better than TC4 alloy. In addition, the COF (coefficient of friction) between dental materials and human teeth after acid etching was lower than non-acid etching. With the decrease of pH value and the increase of acid etching time, the wear became worse and worse.A wear loss prediction model of dental materials based on ensemble learning was build. By taking the two results in the above experiment for testing samples and others for training samples, the RBF and MLP neural network model, LMS and KStar model were used for predicting wear loss respectively. Then an ensemble learning model was built which integrated with all the single models based on its mean absolute error. By compared with the testing results, it obtained that the error for ensemble learning model was between3%～5%, which reflected good stability and high precision on predicting the wear loss for dental restoration material.A method based on reverse engineering is proposed to evaluate wear loss of human teeth. By using 3D data acquisition, point cloud processing and model reconstruction technologies, the 3D digital models of natural tooth before and after the wear test were obtained. Then the ICP (Iterative Closest Point) arithmetic was used to align the two models in order to obtain the wear space geometry. Wear characteristic quantities including largest vertical substance loss, mean vertical substance loss and volume loss were calculated in Geomagic studio, one of the Reverse Engineering software. It not only realized the accurate measurement of natural teeth tiny wear loss, but also made the process digital, quantitative and visible.