A Hybrid System for Dental Milling Parameters Optimisation

Abstract

This study presents a novel hybrid intelligent system which focuses on the optimisation of machine parameters for dental milling purposes based on the following phases. Firstly, an unsupervised neural model extracts the internal structure of a data set describing the model and also the relevant features of the data set which represents the system. Secondly, the dynamic system performance of different variables is specifically modelled using a supervised neural model and identification techniques from relevant features of the data set. This model constitutes the goal function of the production process. Finally, a genetic algorithm is used to optimise the machine parameters from a non parametric fitness function. The reliability of the proposed novel hybrid system is validated with a real industrial use case, based on the optimisation of a high-precision machining centre with five axes for dental milling purposes.