Intelligent operating conditions design by means of bio-inspired models

Author

Villar, J.R. ; Sedano, J. ; Corchado, Emilio ; Vera, Vicente ; Hernando, Blanca ; Redondo, R.

Author_Institution

Comput. Sci. Dept., Univ. of Oviedo, Gijon, Spain

fYear

2011

fDate

19-21 Oct. 2011

Firstpage

608

Lastpage

613

Abstract

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.

Keywords

drilling; genetic algorithms; milling; neural nets; production engineering computing; bio-inspired models; dental drill machine; dental milling purposes; feature selection process; genetic algorithms; hybrid intelligent system; intelligent operating conditions; neural networks; Artificial neural networks; Biological system modeling; Computational modeling; Data models; Dentistry; Genetic algorithms; Manufacturing; Bio-inspired Systems; Genetic Algorithms; Neural Networks; Optimising Operating Conditions;

fLanguage

English

Publisher

ieee

Conference_Titel

Nature and Biologically Inspired Computing (NaBIC), 2011 Third World Congress on

Conference_Location

Salamanca

Print_ISBN

978-1-4577-1122-0

Type

conf

DOI

10.1109/NaBIC.2011.6089731

Filename

6089731