Intelligent Choice of Characteristic Parameters of Oilstone in Honing of Titanium Alloy Cylinder Based on Artificial Neural Network

2010 ◽  
Vol 426-427 ◽  
pp. 35-39 ◽  
Author(s):  
Yi Fang Wen ◽  
Yan Nian Rui ◽  
Jian Dong Cao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Titanium Alloy ◽  
High Reliability ◽  
Experimental Studies ◽  
Surface Hardness ◽  
Characteristic Parameters ◽  
Workpiece Material ◽  
Organizational Stability ◽  
Artificial Neural

Titanium alloys have good mechanical properties and organizational stability. However, due to the larger viscousity of titanium, a reasonable choice of the characteristic parameters of oilstone will directly affect the quality and efficiency of honing processing. This article solved multi-objective problem using artificial neural network with fast convergence and high precision. Based on a comprehensive analysis of the relationship between the workpiece material, materials status, surface hardness, the required surface quality and various parameters of oilstone, the improved artificial neural network algorithm-GCAQBP was adopted, through coding optimization of input and output parameters, model of intelligent choice of oilstone’s parameters was constructed about titanium alloy cylinder honing processing. Through experimental studies, it is shown that the intelligent model can choose quickly with high reliability compared with the traditional experience.

Simulation of Performance Deterioration of a Microturbine and Application of Neural Network to Its Performance Diagnosis

2008 ◽  
Author(s):  
Jae Eun Yoon ◽  
Jong Joon Lee ◽  
Tong Seop Kim ◽  
Jeong Lak Sohn
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Test Data ◽  
Input Data ◽  
Data Sets ◽  
Characteristic Parameters ◽  
Performance Diagnosis ◽  
The Neural Network ◽  
Performance Deterioration ◽  
Artificial Neural

This study aims to simulate performance deterioration of a microturbine and apply artificial neural network to its performance diagnosis. As it is hard to obtain test data with degraded component performance, the degraded engine data have been acquired through simulation. Artificial neural network is adopted as the diagnosis tool. First, the microturbine has been tested to get reference operation data, assumed to be degradation free. Then, a simulation program was set up to regenerate the performance test data. Deterioration of each component (compressor, turbine and recuperator) was modeled by changes in the component characteristic parameters such as compressor and turbine efficiency, their flow capacities and recuperator effectiveness and pressure drop. Single and double faults (deterioration of single and two components) were simulated to generate fault data. The neural network was trained with majority of the data sets. Then, the remaining data sets were used to check the predictability of the neural network. Given measurable performance parameters (power, temperatures, pressures) as inputs to the neural network, characteristic parameters of each component were predicted as outputs and compared with original data. The neural network produced sufficiently accurate prediction. Reducing the number of input data decreased prediction accuracy. However, excluding up to a couple of input data still produced acceptable accuracy.

scholarly journals Online learning method based on artificial neural network to optimize magnetic shielding characteristic parameters

2019 ◽  
Vol 68 (13) ◽  
pp. 130701
Author(s):  
Xiang-Kai Peng ◽  
Jing-Wei Ji ◽  
Lin Li ◽  
Wei Ren ◽  
Jing-Feng Xiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Online Learning ◽  
Magnetic Shielding ◽  
Learning Method ◽  
Characteristic Parameters ◽  
Artificial Neural
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