scholarly journals Erratum to: Application of particle swarm optimisation in artificial neural network for the prediction of tool life

2017 ◽  
Vol 90 (5-8) ◽  
pp. 2411-2411 ◽  
Author(s):  
U. Natarajan ◽  
R. Saravanan ◽  
V. M. Periasamy
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Tool Life ◽  
Particle Swarm ◽  
Particle Swarm Optimisation ◽  
Artificial Neural

COMPARISON BETWEEN PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM IN ARTIFICIAL NEURAL NETWORK FOR LIFE PREDICTION OF NC TOOLS

2008 ◽  
Vol 07 (01) ◽  
pp. 1-7 ◽  
Author(s):  
SHILONG WANG ◽  
FEI ZHENG ◽  
LING XU
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Particle Swarm Optimization ◽  
Convergence Rate ◽  
Tool Life ◽  
Life Prediction ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Artificial Neural

Accurate life prediction of NC (Numeric Control) tools is very essential in an advanced manufacturing system. In this paper, tool life prediction in a drilling process was researched. An Artificial Neural Network (ANN) has been established for prediction, with drill diameter, cutting speed and feed rate as input parameters and tool life as an output parameter. To improve the performance of the network, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) were applied independently to train the network instead of standard Backward Propagation (BP) algorithm, which has drawbacks of low convergence rate and weak generalization capacity. And the two methods were compared in terms of algorithm complexity, convergence rate and prediction accuracy, with reference to standard BP method.

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