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Optimization method of machining parameters based on intelligent algorithm

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Abstract

The processing parameters have a particularly significant impact on the quality and efficiency of processing. Selecting the correct processing parameters can greatly improve the processing performance of the machine tool. To this end, by improving the chromosome structure and genetic operators of the GA algorithm, a new GA-BP neural network algorithm is proposed and combined BP neural network method for adaptive crossover and mutation probability optimization. Then, through comparison experiments. After selecting a certain type of CNC EDM machine, find its standard process parameter table and select 50 groups of data as preparation. 30 groups of data are randomly sampled from the inside to serve as training sample data, and the remaining 20 groups serve as performance test samples. Experimental results show that the prediction accuracy of the new algorithm is higher than that of the conventional algorithm, pulse width or peak current. The new prediction results are often closer to the true value, and the prediction accuracy is higher, which can better meet the processing requirements.

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Acknowledgements

This work was support by the Zhejiang provincial key research and development project (No. 2021C01149), Zhejiang provincial Science Foundation (No. LGF19F020010).

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Authors and Affiliations

  1. School of Intelligent Manufacturing, Zhejiang Institute of Mechanical & Electrical Engineering, Hangzhou, 310053, Zhejiang, China

    Jie Cai & Wei Zhang

  2. School of Creative Arts and Design, Zhejiang Institute of Mechanical & Electrical Engineering, Hangzhou, 310053, Zhejiang, China

    Jinlian Deng

  3. School of Automation, Zhejiang Institute of Mechanical & Electrical Engineering, Hangzhou, 310053, Zhejiang, China

    Weisheng Zhao

Authors
  1. Jie Cai
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  2. Wei Zhang
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  3. Jinlian Deng
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  4. Weisheng Zhao
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Correspondence to Jie Cai.

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Cai, J., Zhang, W., Deng, J. et al. Optimization method of machining parameters based on intelligent algorithm. Distrib Parallel Databases 40, 737–752 (2022). https://doi.org/10.1007/s10619-021-07357-8

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