Research Article | OPEN ACCESS
Application of Artificial Intelligence Methods of Tool Path Optimization in CNC Machines: A Review
Khashayar Danesh Narooei and Rizauddin Ramli
Department of Mechanical and Materials Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia
Research Journal of Applied Sciences, Engineering and Technology 2014 6:746-754
Received: May 19, 2014 | Accepted: June 16, 2014 | Published: August 15, 2014
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.
Keywords:
Artificial intelligence , CNC machines, machining, optimization , tool path,
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