Major Process Quality Control for Light Seal of Piston Ring Based on KPCA and Elman Neural Network

Abstract:

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Analyzing the mutil-process manufacturing for nitride piston ring, and construct a multi-criteria decision system with AHP, then the nitride, stereotype and microhoning process are selected as the first, second and third key process of quality control. Further, six principle components are extracted with KPCA, and the nitride temperature, nitride time and activator are selected as the input of the prediction model, while the nitride rigidity is selected as the output of the prediction model. Then the quality prediction model based on an Elman neural network is successfully applied in nitride process, which inspects any odd change and predicts the process characteristics, and the predictive accuracy achieves that ninety-two percent of the actual value.

Info:

Periodical:

Advanced Materials Research (Volumes 314-316)

Edited by:

Jian Gao

Pages:

397-400

DOI:

10.4028/www.scientific.net/AMR.314-316.397

Citation:

J. Yang and Y. H. Deng, "Major Process Quality Control for Light Seal of Piston Ring Based on KPCA and Elman Neural Network", Advanced Materials Research, Vols. 314-316, pp. 397-400, 2011

Online since:

August 2011

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Price:

$35.00

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