Prediction of Breaking Elongation of Polyester/Cotton Yarn in Ring Spun Processing

2011 ◽  
Vol 366 ◽  
pp. 108-112
Author(s):  
Bo Zhao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Cotton Yarn ◽  
Ring Spinning ◽  
Breaking Elongation ◽  
Artificial Neural ◽  
Actual Test

The artificial neural network model is used to predict the breaking elongation of polyester/cotton ring spinning yarn in this paper. In order to achieve the objective, a series of trials is conducted. The prediction values and actual test values of which are found to be rather close. Therefore, the artificial neural network model proves to be more feasible in the prediction of breaking elongation of polyester/cotton ring spinning yarn.

Applications of Neural Networks in the Hairiness of Ring Spinning Polyester/Cotton Yarn Prediction

2012 ◽  
Vol 549 ◽  
pp. 1055-1059
Author(s):  
Bo Zhao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Processing Parameters ◽  
Cotton Yarn ◽  
Ring Spinning ◽  
Artificial Neural ◽  
Yarn Hairiness ◽  
Bp Neural Network Model

In this paper, back-propagation (BP) neural network model is introduced and established. This work describes the application of the BP artificial neural network model for the purpose of predicting the polyester/cotton yarn hairiness. This approach has been developed and evaluated with the use of multiple sets of data, comprising of a range of processing parameters. The yarn hairiness of ring spinning is strongly related to the processing parameters. However, it is difficult to establish physical models on the relationship between the processing parameters and the yarn hairiness. Due to the artificial neural network can fully approximate any complex nonlinear system and study dynamic behavior of any serious undetermined system. It has a highly parallel calculation ability, strong robustness and fault tolerance. So using the artificial neural network to predict the polyester/cotton yarn hairiness of ring spinning is a very effective way. The experimental results and corresponding analysis show that the BP neural network model is an efficient technique for the yarn hairiness of ring spinning prediction and has wide prospect in the application of ring spinning yarn production system.

scholarly journals Using Artificial Neural Networks to Predict Influences of Heterogeneity on Rock Strength at Different Strain Rates

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3042
Author(s):  
Sheng Jiang ◽  
Mansour Sharafisafa ◽  
Luming Shen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Strain Rate ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Rock Strength ◽  
Strain Rates ◽  
Filling Material ◽  
Artificial Neural

Pre-existing cracks and associated filling materials cause the significant heterogeneity of natural rocks and rock masses. The induced heterogeneity changes the rock properties. This paper targets the gap in the existing literature regarding the adopting of artificial neural network approaches to efficiently and accurately predict the influences of heterogeneity on the strength of 3D-printed rocks at different strain rates. Herein, rock heterogeneity is reflected by different pre-existing crack and filling material configurations, quantitatively defined by the crack number, initial crack orientation with loading axis, crack tip distance, and crack offset distance. The artificial neural network model can be trained, validated, and tested by finite 42 quasi-static and 42 dynamic Brazilian disc experimental tests to establish the relationship between the rock strength and heterogeneous parameters at different strain rates. The artificial neural network architecture, including the hidden layer number and transfer functions, is optimized by the corresponding parametric study. Once trained, the proposed artificial neural network model generates an excellent prediction accuracy for influences of high dimensional heterogeneous parameters and strain rate on rock strength. The sensitivity analysis indicates that strain rate is the most important physical quantity affecting the strength of heterogeneous rock.

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