Analysis of the Modeling Methodologies for Predicting the Strength of Air-Jet Spun Yarns

Rajamanickam, Rangaswamy; Hansen, S. M.; Jayaraman, Sundaresan

doi:10.1177/004051759706700109

Cited by 48 publications

(29 citation statements)

References 4 publications

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“…This computational model was trained to capture nonlinear relationship between input and output variables with scientific and mathematical basis. In recent days, commonly used model is layered feed-forward neural network with multi layer perceptions and back propagation learning algorithms (Vangheluwe et al, 1993, Rajamanickam et al, 1997, Zhu & Ethridge, 1997and Wen et al, 1998.…”

Section: Artificial Neural Network Modelmentioning

confidence: 99%

Modelling of Needle-Punched Nonwoven Fabric Properties Using Artificial Neural Network

Debnath¹

2011

Artificial Neural Networks - Industrial and Control Engineering Applications

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Section: Artificial Neural Network Modelmentioning

confidence: 99%

Modelling of Needle-Punched Nonwoven Fabric Properties Using Artificial Neural Network

Debnath¹

2011

Artificial Neural Networks - Industrial and Control Engineering Applications

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“…Rajamanickam et al [11], Guha et al [12] and Majumdar and Majumdar [13] have demonstrated that the prediction performance of ANN models is much better than that of classical mechanistic or regression models. Zhu and Ethridge [14] predicted the unevenness of spun yarns from advanced fiber information system (AFIS) parameters using ANN and obtained good prediction results.…”

Section: Introductionmentioning

confidence: 99%

Modelling of ring yarn unevenness by soft computing approach

Majumdar¹,

Ciocoiu

Blaga

2008

Fibers Polym

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This paper demonstrates the application of two soft computing approaches namely artificial neural network (ANN) and neural-fuzzy system to forecast the unevenness of ring spun yarns. The cotton fiber properties measured by advanced fiber information system (AFIS) and yarn count have been used as inputs. The prediction accuracy of the ANN and neuralfuzzy models was compared with that of linear regression model. It was found that the prediction performance was very good for all the three models although ANN and neural-fuzzy models seem to have some edge over the linear regression model. The linguistic rules developed by the neural-fuzzy system unearth the role of input variables on the yarn unevenness.

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“…Zhu and Ethridge [21] used ANN models for predicting ring or rotor yarn hairiness. Yarn strength is predicted using different process conditions and material parameters in [6,19,20].…”

Section: Introductionmentioning

confidence: 99%

Prediction of chenille yarn and fabric abrasion resistance using radial basis function neural network models

Çeven

Tokat

Özdemir

2006

Neural Comput & Applic

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The abrasion resistance of chenille yarn is crucially important in particular because the effect sought is always that of the velvety feel of the pile. Thus, various methods have been developed to predict chenille yarn and fabric abrasion properties. Statistical models yielded reasonably good abrasion resistance predictions. However, there is a lack of study that encompasses the scope for predicting the chenille yarn abrasion resistance with artificial neural network (ANN) models. This paper presents an intelligent modeling methodology based on ANNs for predicting the abrasion resistance of chenille yarns and fabrics. Constituent chenille yarn parameters like yarn count, pile length, twist level and pile yarn material type are used as inputs to the model. The intelligent method is based on a special kind of ANN, which uses radial basis functions as activation functions. The predictive power of the ANN model is compared with different statistical models. It is shown that the intelligent model improves prediction performance with respect to statistical models.

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Analysis of the Modeling Methodologies for Predicting the Strength of Air-Jet Spun Yarns

Cited by 48 publications

References 4 publications

Modelling of Needle-Punched Nonwoven Fabric Properties Using Artificial Neural Network

Modelling of Needle-Punched Nonwoven Fabric Properties Using Artificial Neural Network

Modelling of ring yarn unevenness by soft computing approach

Prediction of chenille yarn and fabric abrasion resistance using radial basis function neural network models

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