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Single and Multi-objective Optimization Methodologies in CNC Machining

  • Nikolaos Fountas
  • Agis Krimpenis
  • Nikolaos M. Vaxevanidis
  • J. Paulo Davim

Abstract

Aiming to optimize both productivity and quality in modern manufacturing industries, a wide range of optimization techniques and strategies has been presented by numerous researchers. Optimization modules such as Genetic Algorithms, Evolutionary Algorithms and Fuzzy systems are capable of exploiting manufacturing data with high efficiency and reliability, in order to provide optimal sets of solutions for machining processes. The main scope of this chapter is to present the fundamentals in formulating and developing optimization methodologies, which ultimately offer optimal cutting conditions for both prismatic and sculptured surface part machining and actually improve industrial practice.

Keywords

Genetic Algorithm Particle Swarm Optimization Simulated Annealing Quality Characteristic Candidate Solution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Nikolaos Fountas
    • 1
  • Agis Krimpenis
    • 1
  • Nikolaos M. Vaxevanidis
    • 1
  • J. Paulo Davim
    • 2
  1. 1.Department of Mechanical Engineering Technology EducatorsSchool of Pedagogical and Technological Education (ASPETE)N. Heraklion AttikisGreece
  2. 2.Department of Mechanical EngineeringUniversity of Aveiro, Campus SantiagoAveiroPortugal

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