Affiliation:
1. College of Mechanical Engineering, Chongqing University, Chongqing, China
2. College of Software Engineering, Chongqing University, Chongqing, China
Abstract
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.
Publisher
World Scientific Pub Co Pte Lt
Subject
Industrial and Manufacturing Engineering,Strategy and Management,Computer Science Applications
Cited by
10 articles.
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