Research Journal of Applied Sciences, Engineering and Technology 8(6): 746-754, 2014 ISSN: 2040-7459; e-ISSN: 2040-7467 © Maxwell Scientific Organization, 2014 Submitted: May 19, 2014 Accepted: June 16, 2014 Published: August 15, 2014 Corresponding Author: Khashayar Danesh Narooei, Department of Mechanical and Materials Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia, Tel.: +60 172324109 746 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 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 INTRODUCTION Recently, the advanced of computer system and improvement of semiconductors in various field have lead to the enhancement of machining process especially that involved Computer Numerical Control (CNC) (Li and Frank, 2006). The CNC machining are used mainly in manufacturing areas such as machining parts for automotive tools, jig and mold. The main advantage of CNC machining is catch high machining accuracy with easily programming and repeatability in complicated parts machining (Al-Kindi and Zughaer, 2012; Safaieh et al., 2013). The conventional ways of selecting the tool path or programming the NC code used data from machining handbooks and the knowledge of programmer for optimal processing (El-Midany et al., 2006; Suh and Lee, 1998). However, the conventional or traditional NC programming when compared to the modern CNC machining has many disadvantages for instance increasing time and cost production and, decreasing accuracy and quality of the workpiece (Liu et al., 2013; Lasemi et al., 2010). Nowadays, most of the CNC machines tools are programmed automatically using Computer Aided Manufacturing (CAM) software instead of manual program input in order to reduce programming time and to avoid human errors (Mattson and Mattson, 2009; Suneel and Pande, 2000). Therefore, one of the most essential factors in optimizing the machining process is the selection of tool path. For example, before a machinist performs an optimal cutting process using CNC machine tools, the tool path for tool processing should be determined before the actual tool processing (Zhang et al., 2011; Mayor and Sodemann, 2008). In general, the current way of tool path selection is based on the set of ordinary path such as zig, zig-zag, radial, spiral tool paths etc (López de Lacalle et al., 2007). The objective of this study is to review of prior works on tool path optimization in CNC machines in order to classify different types of machining process with CNC machine tools. First we describe an overview of optimization methods. In the next section, collection of all previous research in tool path with different types of Artificial Intelligence (AI) optimization methods will be presented to show the ability of various methods in optimizing machining process. DIFFERENT AI OPTIMIZATION METHOD Different types of optimization methods in AI have been adapted in many previous researches to find optimum parameters for machining process. In general, AI is a branch of Computer Science (CS) which are developed and emerged in the mid-1950s that deals with the intelligence of machines (Jones, 2008; El-Mounayri et al., 2002). Since then, it had generated numbers of powerful tools that have practical usage especially in engineering in order to solve difficult problems which normally need the secrets of Natural Intelligence (NI).