Intelligent operating conditions design by means of bio-inspired models.

José Ramón Villar,Javier Sedano,Emilio Corchado,Vicente Vera,Beatriz Hernando,Raquel Redondo

NaBIC（2011）

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摘要

This study presents a novel hybrid intelligent system, which focuses on the optimisation of machine parameters for dental milling purposes. The basis of this approach is hybridizing two bio-inspired algorithms, as Neural Networks with Genetic Algorithms for choosing and modelling the feature subset that best descript the operation conditions. These operating conditions are given as parameters for a dental drill machine. The aim of this approach is twofold: a feature selection process is carried out while the modelling of the operating conditions is achieved. 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.

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关键词

operant conditioning,use case,dentistry,genetic algorithm,artificial neural network,manufacturing,neural network,neural networks,computer model,artificial neural networks,hybrid intelligent system,data models,feature selection,drilling,computational modeling,genetic algorithms,hybrid system,data model,neural nets

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