Development of predictive algorithms for the wear resistance of denture teeth materials

Abstract

ObjectivesTo investigate the wear resistance of conventional, CAD-milled and 3D-printed denture teeth in vitro with simulated aging. To use the collected data to train single time series sample model LSTM and provide proof of concept. MethodsSix denture teeth materials (Three Conventional; Double-cross linked PMMA (G1), Nanohybrid composite (G2), PMMA with microfillers (G3), CAD-milled (G4), two 3D-printed teeth (G5, G6) (Total n = 60) underwent simulation for 24 and 48 months of linear reciprocating wear using a universal testing machine (UFW200, NeoPlus) under 49 N load, 1 Hz and linear stroke of 2 mm in an artificial saliva medium. Single samples were parsed through Long Short-Term Memory (LSTM) neural network model using Python. To determine minimal simulation times, multiple data splits for training were trialled (10/20/30/40%). Scanning electron microscopy (SEM) was performed for material surface evaluation. Results3D printed tooth material (G5) had the lowest wear resistance (59 ± 35.71 μm) whereas conventional PMMA with microfillers (G3) shown the highest wear rate (303 ± 0.06 μm) after 48 months of simulation. The LSTM model successfully predicted up to 48 months wear using 30% of the collected data. Compared with the actual data, the model had a root-mean-square error range between 6.23 and 88.56 μm, mean-absolute-percentage-error 12.43-23.02% and mean-absolute-error 7.47-70.71 μm. SEM images revealed additional plastic deformations and chipping of materials, that may have introduced data artifacts. Conclusions3D printed denture teeth materials showed the lowest wear out of all studied for 48 months simulation. LSTM model was successfully developed to predict wear of various denture teeth. The developed LSTM model has the potential to reduce simulation duration and specimen number for wear testing of various dental materials, while potentially improving the accuracy and reliability of wear testing predictions. This work paves the way for generalized multi-sample models enhanced with empirical information.