Processing parameters optimization of draw frame for rotor spun yarn strength using gene expression programming (GEP)

2011 ◽  
Vol 12 (7) ◽  
pp. 970-975 ◽  
Author(s):  
A. R. Moghassem ◽  
A. R. Fallahpour
Keyword(s):  
Gene Expression ◽  
Gene Expression Programming ◽  
Processing Parameters ◽  
Parameters Optimization ◽  
Yarn Strength ◽  
Spun Yarn ◽  
Rotor Spun Yarn ◽  
Processing Parameters Optimization

scholarly journals Open-End Yarn Properties Prediction Using HVI Fibre Properties and Process Parameters

2017 ◽  
Vol 17 (1) ◽  
pp. 6-11 ◽  
Author(s):  
Hanen Ghanmi ◽  
Adel Ghith ◽  
Tarek Benameur
Keyword(s):  
Response Surface ◽  
Process Parameters ◽  
Processing Parameters ◽  
Raw Material ◽  
Spun Yarn ◽  
Yarn Properties ◽  
Spun Yarns ◽  
Yarn Count ◽  
Input Variables ◽  
Rotor Spun Yarn

AbstractThis article provides three models to predict rotor spun yarn characteristics which are breaking strength, breaking elongation and unevenness. These models used noncorrelated raw material characteristics and some processing parameters. For this purpose, five different cotton blends were processed into rotor spun yarns having different metric numbers (Nm10, Nm15, Nm18, Nm22, Nm30 and Nm37). Each count was spun at different twist levels. Response surface method was used to estimate yarn quality characteristics and to study variable effects on these characteristics. In this study, predicting models are given by the analysis of response surface after many iterations in which nonsignificant terms are excluded for more accuracy and precision. It was shown that yarn count, twist and sliver properties had considerable effects on the open-end rotor spun yarn properties. This study can help industrial application since it allows a quality management-prediction based on input variables such as fibre characteristics and process parameters.

Parameters Optimization and Experimental Analysis in High Speed Machining

2012 ◽  
Vol 591-593 ◽  
pp. 480-483
Author(s):  
Huan Lin ◽  
Dong Qiang Gao ◽  
Zhong Yan Li ◽  
Jiang Miao Yi
Keyword(s):  
Genetic Algorithm ◽  
High Speed ◽  
Production Efficiency ◽  
Cutting Parameters ◽  
Processing Parameters ◽  
Parameters Optimization ◽  
High Speed Machining ◽  
Before And After ◽  
Processing Parameters Optimization ◽  
Cutting Parameters Optimization

First of all, in the cutting parameters optimization, according to the different processing conditions, optimization variables selection is different, including production efficiency the objective function and the constraint conditions of the machine tool. And then using genetic algorithm to build a high-speed processing parameters optimization model. The mathematical model explores the best solution through the software Matlab, and gets the optimal combination between the parameters of each cutting; high speed machining cutting parameters provides the reference for the choice of the user. Through the optimization of the comparison of the before and after that, using genetic algorithm cutting parameters optimization, mach inability got obvious improvement, in order to ensure the quality of processing also achieve the maximization of the production efficiency.

Processing Parameters Optimization and Experiment Research of Automatic Polishing Technology for Free Surface of Dies

2012 ◽  
Vol 159 ◽  
pp. 198-202
Author(s):  
Dong Ming Li ◽  
Ying Jia ◽  
Xiao Jing Tian ◽  
Xian Ke Ren ◽  
Jun Wei Yin
Keyword(s):  
Free Surface ◽  
Processing Parameters ◽  
Parameters Optimization ◽  
Numerical Control ◽  
Analysis Method ◽  
Automatic Polishing ◽  
Practical Research ◽  
Orthogonal Analysis ◽  
Processing Parameters Optimization ◽  
Machine Processing

Abstract: A kind of automatic polishing equipment for free surface of dies is designed, which is fixed on the backbone of numerical control machine. Processing parameters optimization are gotten by the orthogonal analysis method based on experiment data and surface roughness is about 1.0μm, which provides good basis of practical research and application of accurate working for free surface of dies.

scholarly journals An Intelligent Model to Predict Breaking Strength of Rotor Spun Yarns Using Gene Expression Programming

2012 ◽  
Vol 7 (2) ◽  
pp. 155892501200700 ◽  
Author(s):  
Abdolrasool Moghassem ◽  
Alireza Fallahpour ◽  
Mohsen Shanbeh
Keyword(s):  
Gene Expression ◽  
Breaking Strength ◽  
Gene Expression Programming ◽  
Ann Model ◽  
Spun Yarn ◽  
Yarn Properties ◽  
Spun Yarns ◽  
Artificial Neural Network Ann ◽  
High Degree ◽  
Prediction Approach

Exploring relationships between characteristics of a yarn and influencing factors is momentous subject to optimize the selection of the variables. Different modelling methodologies have been used to predict spun yarn properties. Developing a prediction approach with higher degree of precision is a subject that has received attention by the researchers. In the last decade, Artificial Neural Network (ANN) has been developed successfully for textile nonlinear processes. In spite of the precision, ANN is a black box and does not indicate inter-relationship between input and output parameters. Hence, Gene Expression Programming (GEP) is presented here as an intelligent algorithm to predict breaking strength of rotor spun yarns based on draw frame parameters as one of the most important stages in spinning line. Forty eight samples were produced and different models were evaluated. Prediction performance of the GEP was compared with that of ANN using Mean Square Error (MSE) and correlation coefficient (R2-Value) parameters on test data. The results showed a better capability of the GEP model in comparison to the ANN model. The R2-value and MSE were 97% and 0.071 respectively which means desirable predictive power of GEP algorithm. Finally, an equation was extracted to predict breaking strength of the yarns with a high degree of accuracy using GEP algorithm.

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