A novel bio-inspired computational high-precision dental milling system is proposed in this interdisciplinar research. The system applies several bio-inspired models, based on unsupervised learning, that analyse and identify the most relevant features of high-precision dental-milling data sets and their internal structures. Finally, a supervised neural architecture and certain identification techniques are applied, in order to model and to optimize the high-precision process. This is done by empirically testing the model using a real data set taken from a dynamic high-precision machining centre with five axes.
This study presents a novel bio-inspired knowledge system, based in closed loop tuning, for the calculation of the Proportional-Integral-Derivative (PID) controller parameters. The aim is to achieve automatically the best parameters according with the work point and the dynamics of the plant. For it, in our study, several typical expressions and systems have been taken into account to build the model. Each of these expressions is appropriated for a particular system. The novel method is empirically verified with a real dataset obtained by a liquid-level laboratory plant.
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