Paper Title:
Surface Roughness Prediction in Precision Surface Grinding of Nano-Ceramic Coating Based on Improved ANFIS
  Abstract

For improving surface integrity and machining quality after precision grinding of the parts of nano-ceramic coating, and investigating its prediction technique of surface roughness, the prediction model of surface roughness in precision surface grinding of nano-ceramic coating based on adaptive network-based fuzzy inference system (ANFIS) was proposed in this paper. Then, the proposed prediction model was improved by hybrid Taguchi genetic algorithm (HTGA). At last, by comparative analysis of prediction results from traditional BP neural network model, simple ANFIS model and improved ANFIS model, the effectiveness of the proposed model was verified using grinding parameters and measured surface roughness in grinding tests as training and testing samples. It showed that the prediction accuracy of the improved ANFIS model proposed in this paper was higher, and it was an effective prediction method of surface roughness in precision grinding of nano-ceramic coating.

  Info
Periodical
Edited by
Ran Chen
Pages
2293-2298
DOI
10.4028/www.scientific.net/AMM.44-47.2293
Citation
X. H. Guo, M. F. Liu, C. R. Zhao, "Surface Roughness Prediction in Precision Surface Grinding of Nano-Ceramic Coating Based on Improved ANFIS", Applied Mechanics and Materials, Vols. 44-47, pp. 2293-2298, 2011
Online since
December 2010
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