Prediction of Cutting Tool Life Based on ACO-BP Meural Network

Abstract:

For predicting the tool life combine the ant colony optimization(ACO) with the back propagation (BP) neural networks, use the the ACO to train BP neural network, build the prediction model based ACO-BP neural network. Some disadvantages are overcame in the BP algorithm, such as the low convergence speed, easily falling into local minimum point and weak global search capablity in the prediction process. Satisfies the requirement of global search capability and the robustness of the model. The experiment results show the prediction model has high precision in predicting the tool life. By the prediction model can provide a reasonable basis for planing production schedule and cutting tool requirement, calculating the cost, selecting the machining parameters,etc.

• Periodical: Advanced Materials Research (Volumes 655-657)
• Pages: 1714-1717
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.655-657.1714
• Online since: January 2013
• Authors: Tie Liu Wang, Xian Ming Chen, Shui Bin Chen
• Keywords: ACO, BP Neural Network (BPNN), Prediction, Tool Life

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