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Abstract: In according with the non-linear character in the process
of EDMM, a tool wear prediction model is established based on artificial
neural network. The tool relative wear and machining rate can be
predicted by the network, which makes foundation for tool’s dynamic
compensation in EDMM. An improved evolutionary algorithm which could
adaptively adjust mutation rate and magnitude of mutation is presented
to optimize the neural network’s weights and topology in case of local
extremums obtained from traditional BP algorithm.
Key words: Evolutionary neural network Tool wear prediction Electrical
discharge milling machining
CLC No: TH16
国家自然科学基金资助项目(50275100).
Received 20030115, received in revised form 20030630
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