The Prediction of Cotton Yarn Irregularity Based on the ‘AFIS’ Measurement

Zhu, R. Y.; Ethridge, M. Dean

doi:10.1080/00405009608631352

Cited by 29 publications

(6 citation statements)

References 5 publications

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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. In a similar research, Guha [15] predicted the unevenness of ring and rotor spun yarns using ANN and found that the mean error of prediction was always less than 4 % for three different datasets.…”

Section: Introductionmentioning

confidence: 95%

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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Section: Introductionmentioning

confidence: 95%

Modelling of ring yarn unevenness by soft computing approach

Majumdar¹,

Ciocoiu

Blaga

2008

Fibers Polym

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“…The advent of artificial intelligence has provided a new impetus in the research on modelling of yarn properties. Cheng and Adams [10], Ramesh, Rajamanickam and Jayaraman, [11], Zhu and Ethridge [12,13], Guha, Chattopadhyay and Jayadeva [14] and Majumdar and Majumdar [15] have successfully used the artificial neural network (ANN) and neural-fuzzy methods to predict various properties of spun yarns. The prediction accuracy of ANN has been acclaimed by most of these researchers.…”

Section: Introductionmentioning

confidence: 99%

Yarn Strength Modelling Using Fuzzy Expert System

Majumdar¹,

Ghosh²

2008

Journal of Engineered Fibers and Fabrics

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Yarn strength modelling and prediction has remained as the cynosure of research for the textile engineers although the investigation in this domain was first reported around one century ago. Several mathematical, statistical and empirical models have been developed in the past only to yield limited success in terms of prediction accuracy and general applicability. In recent years, soft computing tools like artificial neural networks and neural-fuzzy models have been developed, which have shown remarkable prediction accuracy. However, artificial neural network and neural-fuzzy models are trained using enormous amount of noise free input-output data, which are difficult to collect from the spinning industries. In contrast, fuzzy logic based models could be developed by using the experience of the spinner only and it gives good understanding about the roles played by various inputs on the outputs. This paper deals with the modelling of ring spun cotton yarn strength using a simple fuzzy expert system. The prediction accuracy of the model was found to be very encouraging.

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“…Related to this context, this paper deals with the modelling of the relationships between the structural parameters and the expected features of nonwovens. In this research work, soft computing techniques such as neural networks are implemented for modelling these relationships (Ramesh et al, 1995;Ethridge, 1996 &Sette et al, 1997;Kuo et al, 1998;Fan and Hunter, 1998;Kuo, et al 1999;Xu et al, 1999). Our major goal is to propose appropriate methods to simultaneously handle the following three important issues: i) lack of information -usually very few samples are available, ii) a large number of available processes for manufacturing nonwoven products, iii) inaccuracy of the predicted features.…”

Section: Introductionmentioning

confidence: 99%

Forecasting End-Uses of Nonwovens by Integrating Measured Data and Human Expertise

Koehl¹,

Chen²,

Vroman

et al. 2005

Textile and Apparel

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This paper, which deals with the forecasting of nonwovens end-uses, is divided in two parts. The first part presents optimized methods for measuring the structures of nonwovens. The raw data are extracted directly from 3D images of the accurate topographic surfaces of the materials and also from other instruments. Next, data analysis techniques are applied to select relevant structural parameters and forecast the expected end-uses of nonwovens. Relevant physical features are selected by integrating measured data and the knowledge of experts. The effectiveness of these methods has been shown through a number of nonwoven products designed for filtration.

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The Prediction of Cotton Yarn Irregularity Based on the ‘AFIS’ Measurement

Cited by 29 publications

References 5 publications

Modelling of ring yarn unevenness by soft computing approach

Modelling of ring yarn unevenness by soft computing approach

Yarn Strength Modelling Using Fuzzy Expert System

Forecasting End-Uses of Nonwovens by Integrating Measured Data and Human Expertise

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