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A hybrid model using genetic algorithm and neural network for process parameters optimization in NC camshaft grinding

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Abstract

Camshaft grinding is more complex comparing with the ordinary cylindrical grinding. Since its quality is mostly influenced by more factors, how to select process parameters quickly and accurately becomes the key to improve its quality and processing efficiency. In this paper, a hybrid artificial neural network (ANN) and genetic algorithm (GA) model is proposed to optimize the process parameters. In this method, a BP neural network model is developed to map the complex nonlinear relationship between process parameters and processing requirements, and a GA is used in order to improve the accuracy and speed based on the ANN model. The results show that the hybrid ANN/GA model is an effective tool for the process parameters optimization in NC camshaft grinding.

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Authors and Affiliations

  1. National Engineering Research Center for High Efficiency Grinding, Hunan University, Hunan, China

    Z H Deng, X H Zhang, W Liu & H Cao

  2. Huda Haijie Manufacture Technology Co., Ltd, Hunan University, Hunan, China

    H Cao

Authors
  1. Z H Deng
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  2. X H Zhang
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  3. W Liu
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  4. H Cao
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Correspondence to X H Zhang.

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Deng, Z.H., Zhang, X.H., Liu, W. et al. A hybrid model using genetic algorithm and neural network for process parameters optimization in NC camshaft grinding. Int J Adv Manuf Technol 45, 859–866 (2009). https://doi.org/10.1007/s00170-009-2029-4

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  • DOI: https://doi.org/10.1007/s00170-009-2029-4

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