Machining optimization using swarm intelligence in titanium (6Al 4V) alloy

The process of titanium machining in the aerospace industry today is by personal experience, producing non-efficient results. Assignment of the correct parameter for machining is hard to determine because the material has a high chemical reaction with other materials and has low thermal conductivity. These are the reasons why researchers are developing new prediction models to optimize such parameters. In this paper, particle swarm optimization (PSO) is used to optimize machining parameters in high-speed milling processes where multiple conflicting objectives are presented. The relationships between machining parameters and the performance measures of interest are obtained by using experimental data and a hybrid system using a PSO and a neural network. Results showed that particle swarm optimization is an effective method for solving multi-objective optimization problems and also that an integrated system of neural networks and swarm intelligence can be used to solve complex machining optimization problems.