A neural network technique to develop a vase life prediction model of cut roses

2009 ◽  
Vol 52 (3) ◽  
pp. 273-278 ◽  
Author(s):  
Byung-Chun In ◽  
Katsuhiko Inamoto ◽  
Motoaki Doi
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Life Prediction ◽  
Vase Life ◽  
Life Prediction Model

Tool Life Prediction Model Based on GA-BP Neural Network

2016 ◽  
Vol 836-837 ◽  
pp. 256-262 ◽  
Author(s):  
Zheng Zhang ◽  
Liang Li ◽  
Wei Zhao
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Tool Life ◽  
Bp Neural Network ◽  
Life Prediction ◽  
Affecting Factors ◽  
Tool Selection ◽  
Model Based ◽  
Bp Model ◽  
Life Prediction Model

In order to improve the working efficiency of a manufacturing system, tool life estimation is very essential. In this paper, the dominant factors affecting tool life are analyzed by theoretical analysis. According to the nonlinear relationship between affecting factors and tool life, a tool life prediction model based on BP neural network, which is optimized by genetic algorithm (GA), is built up. 15 network patterns are trained to get the best network structure. The accuracy of GA-BP model is verified through computing and compared with the standard BP model. The results show that GA-BP model prediction value is exactly closed to the expected value of tool life and the prediction accuracy can be improved more than 5% compared than the standard BP model. The model is proved to be accuracy and it can be used as an effective method of tool selection decision.

scholarly journals Life Prediction Model of Mineral Admixture Cement Based-Materials under Early Age CO2-Erosion

Coatings ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 413
Author(s):  
Saisai Wang ◽  
Jian Chen ◽  
Xiaodong Wen
Keyword(s):  
Prediction Model ◽  
Life Prediction ◽  
Experimental Tests ◽  
Mineral Admixture ◽  
Model Parameters ◽  
Influence Coefficient ◽  
Test Pieces ◽  
Cement Based Materials ◽  
Particle Group ◽  
Life Prediction Model

Most of the existing models of structural life prediction in early carbonized environment are based on accelerated erosion after standard 28 days of cement-based materials, while cement-based materials in actual engineering are often exposed to air too early. These result in large predictions of the life expectancy of mineral-admixture cement-based materials under early CO2-erosion and affecting the safe use of structures. To this end, different types of mineral doped cement-based material test pieces are formed, and early CO2-erosion experimental tests are carried out. On the basis of the analysis of the existing model, the influence coefficient of CO2-erosion of the mineral admixture Km is introduced, the relevant function is given, and the life prediction model of the mineral admixture cement-based material under the early CO2-erosion is established and the model parameters are determined by using the particle group algorithm (PSO). It has good engineering applicability and guiding significance.

The construction of the life prediction model of 1N and 4N monopropellant thrusters

2013 ◽  
Author(s):  
Go Fujii ◽  
Daisuke Goto ◽  
Hideshi Kagawa ◽  
Shingo Murayama ◽  
Kenichi Kajiwara ◽  
...  
Keyword(s):  
Prediction Model ◽  
Life Prediction ◽  
Life Prediction Model

Wire Fatigue Models for Power Electronic Modules

2003 ◽  
Author(s):  
Karumbu Nathan Meyyappan ◽  
Peter Hansen ◽  
Patrick McCluskey
Keyword(s):  
Prediction Model ◽  
Life Prediction ◽  
Damage Model ◽  
Cyclic Strain ◽  
Temperature Cycling ◽  
Transformation Model ◽  
Time To Failure ◽  
Flexural Failure ◽  
The Wire ◽  
Life Prediction Model

This paper presents two, semi-analytical, physics-of-failure based life prediction model formulations for flexural failure of wires ultrasonically wedge bonded to pads at different heights. The life prediction model consists of a load transformation model and a damage model. The load transformation model determines the cyclic strain is created by a change in wire curvature at the heel of the wire resulting from expansion of the wire and displacement of the frame. The damage model calculates the life based on the strain cycle magnitude and the elastic-plastic fatigue response of the wire. The first formulation provides quick calculation of the time to failure for a wire of known geometry. The second formulation optimizes the wire geometry for maximum time to failure. These model formulations support virtual qualification of power modules where wire flexural fatigue is a dominant failure mechanism. The model has been validated using temperature cycling test results.

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