Application of artificial intelligence methods of tool path optimization in CNC machines: A review

Khashayar Danesh Narooei, Rizauddin Ramli

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Today, in most of metal machining process, Computer Numerical Control (CNC) machine tools have been very popular due to their efficiencies and repeatability to achieve high accuracy positioning. One of the factors that govern the productivity is the tool path travel during cutting a work piece. It has been proved that determination of optimal cutting parameters can enhance the machining results to reach high efficiency and minimum the machining cost. In various publication and articles, scientist and researchers adapted several Artificial Intelligence (AI) methods or hybrid method for tool path optimization such as Genetic Algorithms (GA), Artificial Neural Network (ANN), Artificial Immune Systems (AIS), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). This study presents a review of researches in tool path optimization with different types of AI methods that show the capability of using different types of optimization methods in CNC machining process.

Original languageEnglish
Pages (from-to)746-754
Number of pages9
JournalResearch Journal of Applied Sciences, Engineering and Technology
Volume8
Issue number6
Publication statusPublished - 2014

Fingerprint

Artificial intelligence
Machining
Ant colony optimization
Immune system
Machine tools
Particle swarm optimization (PSO)
Genetic algorithms
Productivity
Neural networks
Metals
Costs

Keywords

  • Artificial intelligence
  • CNC machines
  • Machining
  • Optimization
  • Tool path

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science(all)

Cite this

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