Nowadays, a tool wear prediction in advanced manufacturing systems is becoming very important for optimizing cutting processes. In the present article, a particle swarm optimization algorithm for ...prediction of tool wear in end milling has been used. Helix angle, spindle speed, feed rate, axial depth of cut and radial depth of cut were taken as input parameters and tool wear as an output parameter. In the authors application, the particle swarm optimization algorithm has searched for the optimal solution of developed polynomial model. Predicted values of PSO model are compared with experimental results. The best solution of polynomial model was proposed as a model for predicting of tool wear.
