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Application of BP Neural Network Model Based on Improved DNA Genetic Algorithm to Tool Wear Monitoring

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Abstract:

In this paper, proposed a model of BP neural network based on improved DNA genetic algorithm, applied it to monitor and forecast cutting tool wear. Designed subsection crossover and subsection mutation based on process code, adopted fitness scaling method and ranking method to select operators, optimized the initial weight values of BP neural network, repeatedly trained the BP neural network model, improved the overall convergence rate, avoided falling into local minimum, improved the precision and accuracy of prediction. Through simulation, tested the designed method performance, compared with other test results, the simulation results show the effectiveness of the proposed method.

Keywords: BP NEURAL NETWORK MODEL; IMPROVED DNA GENETIC ALGORITHM; SUBSECTION CROSSOVER; SUBSECTION MUTATION; TOOL WEAR MONITORING

Document Type: Research Article

DOI: https://doi.org/10.1166/asl.2012.2245

Publication date: 2012-03-01

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