Forecasting of Cutting Forces in Dental Adjustment of Ceramic Prostheses Using an Artificial Neural Network

Intraoral adjustment of ceramic prostheses involving cutting process is a central procedure in restorative dentistry because the quality of ceramic prostheses depends on the cutting process. In this paper, an artificial neural network (ANN) model was developed for the first time to forecast the dynamic forces in dental cutting process as functions of clinical operational parameters. The predicted force values were compared with the measured values in in vitro dental cutting of porcelain prostheses obtained using a novel two-degrees-of-freedom computer-assisted testing apparatus with a high-speed dental handpiece and diamond burs. The results indicate that there existed nonlinear relationships between the cutting forces and clinical operational parameters. It is found that the ANN-forecasted forces were in good agreement with the experiment-measured values. This indicates that the established ANN model can provide insights into the force-related process assessment and forecast for clinical dental cutting of ceramic prostheses.