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

Jie Yang; Yao Hua Deng

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

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 314-316Major Process Quality Control for Light Seal of...

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

Abstract:

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.

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Advanced Manufacturing Technology, ADME 2011

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Periodical:

Advanced Materials Research (Volumes 314-316)

Pages:

397-400

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.314-316.397

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Online since:

August 2011

Authors:

Jie Yang, Yao Hua Deng

Keywords:

Analysis Hierarchy Process (AHP), Elman Neural Network (ENN), Principle Component Extraction

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References

[1] Petros A. Pilavachi, Anatoli I. Chatzipanagi, Antonia I. Spyropoulou: Hydrogen Energy. Vol. 34 (2009), p.5294.

Google Scholar

[2] Alberto A. Aguilar-Lasserre, Marco A. Bautista Bautista, Antonin Ponsich, Magno A. González Huerta: Computers & Operations Research. Vol. 36 (2009), p.711.

DOI: 10.1016/j.cor.2007.10.029

Google Scholar

[3] Robert B. Heimann: Surface and Coatings Technology. Vol. 205 (2010), p.943.

Google Scholar

[4] Michael B. Richman, Indra Adrianto: Physics and Chemistry of the Earth. Vol. 35 (2010), p.316.

Google Scholar

[5] Diego Martinez Prata, Enrique Luis Lima: Computer Aided Chemical Engineering. Vol. 26 (2009), p.47.

Google Scholar

[6] Li, Jing; Zuo, Bin; Hu, Yun-An: Journal of Jilin University. Vol. 38 (2008), p.460.

Google Scholar