Research Article | OPEN ACCESS
The TiN Content Computer Prediction Based on ANN and AR Model
1Ma Chunyang, 1Ding Junjie, 2Ning Yumei and 3Chu Dianqing
1School of Mechanical Science and Engineering, Northeast Petroleum University, Daqing 163318, China
2School of computer science and technology, Xidian University, Xi’an 710071, China
3No. 4 Oil Production Plant, Petrochina Daqing Oilfield, Daqing 163000, China
Research Journal of Applied Sciences, Engineering and Technology 2013 13:3617-3621
Received: September 10, 2012 | Accepted: October 19, 2012 | Published: April 15, 2013
Abstract
Artificial Neural Network (ANN) and autoregressive model (AR model) of nano TiN particles content in Ni-TiN composite coating was established by the method of time series analysis. In this paper, we want to seek for the TiN content computer prediction in Ni-TiN composite coatings by using ANN and AR model. The trend of the nano TiN particles content variation was forecasted with the AR model, and the prediction value and experimental test results were compared. The XRD patterns were investigated using X-ray Diffraction (XRD).The results show the number of the neuron in hidden layers is 10, and the optimal epoch is 3740. The ANN and AR model can forecast the nano TiN particles content in Ni-TiN composite coating. And the average deviation is about 5.2884%. The average grain size for Ni and TiN is approximately 52.85 and 39.13 nm, respectively.
Keywords:
AR model, Ni-TiN composite coating, particles content,
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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