Meta-heuristics: The State of the Art
ECAI '00 Proceedings of the Workshop on Local Search for Planning and Scheduling-Revised Papers
Ant colony optimization theory: a survey
Theoretical Computer Science
Study on Reduction of Machining Time in CNC Turning Centre by Genetic Algorithm
ICCIMA '07 Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) - Volume 01
PSO for Selecting Cutting Tools Geometry
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
A Modified Ant Colony System for the Selection of Machining Parameters
GCC '08 Proceedings of the 2008 Seventh International Conference on Grid and Cooperative Computing
ICMTMA '09 Proceedings of the 2009 International Conference on Measuring Technology and Mechatronics Automation - Volume 03
Cutting Parameter Optimization Based on Particle Swarm Optimization
ICICTA '09 Proceedings of the 2009 Second International Conference on Intelligent Computation Technology and Automation - Volume 01
A review of optimization techniques in metal cutting processes
Computers and Industrial Engineering
Parameter Tuning for the Artificial Bee Colony Algorithm
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
MICAI '09 Proceedings of the 2009 Eighth Mexican International Conference on Artificial Intelligence
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
International Journal of Computer Integrated Manufacturing
Classification With Ant Colony Optimization
IEEE Transactions on Evolutionary Computation
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
In highly competitive manufacturing industries nowadays, the manufactures ultimate goals are to produce high quality product with less cost and time constraints. To achieve these goals, one of the considerations is by optimizing the machining process parameters such as the cutting speed, depth of cut, radial rake angle. Recently, alternative to conventional techniques, evolutionary optimization techniques are the new trend for optimization of the machining process parameters. This paper gives an overview and the comparison of the latest five year researches from 2007 to 2011 that used evolutionary optimization techniques to optimize machining process parameter of both traditional and modern machining. Five techniques are considered, namely genetic algorithm (GA), simulated annealing (SA), particle swarm optimization (PSO), ant colony optimization (ACO) and artificial bee colony (ABC) algorithm. Literature found that GA was widely applied by researchers to optimize the machining process parameters. Multi-pass turning was the largest machining operation that deals with GA optimization. In terms of machining performance, surface roughness was mostly studied with GA, SA, PSO, ACO and ABC evolutionary techniques.