Application of artificial neural network (ANN) for prediction of fabrics’ extensibility

Rolich, Tomislav; Šajatović, Anica Hursa; Pavlinić, Daniela Zavec

doi:10.1007/s12221-010-0917-8

Cited by 7 publications

(5 citation statements)

References 11 publications

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“…The values of experimental measurement of extensibility using a KES-FB1 measuring device were compared with predicted values computed with ANN. The ANN model was successful in predicting the fabrics' extensibility, and the predicted values were in good agreement with the experimental measurements; the prediction of fabrics' extensibility can be used in computer simulations for clothing design [81].…”

Section: Ai For Garment Propertiessupporting

confidence: 54%

Artificial-Neural-Network-Based Predicted Model for Seam Strength of Five-Pocket Denim Jeans: A Review

Zulfiqar,

Manzoor,

Ijaz

et al. 2024

Textiles

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This study explores previous research efforts concerning prediction models related to the textile and polymer industry, especially garment seam strength, emphasizing critical parameters such as stitch density, fabric GSM, thread type, thread count, stitch classes, and seam types. These parameters play a pivotal role in determining the durability and overall quality of denim jeans based on cellulosic polymer. A significant focus is dedicated to the mathematical computational models employed for predicting seam strength in five-pocket denim jeans. Herein, the discussion poses the application of AI for manufacturing industries, especially for textile and clothing sectors, and highlights the importance of using a machine learning prediction model for sewing thread consumption, seam strength analysis, and seam performance analysis. Therefore, the authors suggest the significant importance of the machine learning prediction model, as future trends anticipate advancements in AI-driven methodologies, potentially leading to high-profile predictions and superior manufacturing processes. The authors also describe the limitation of AI and address a comprehensive model of risk outlines of AI in the manufacturing-based industries, especially the garments industry. Put simply, this review serves as a bridge between the realms of AI, mathematics, and textile engineering, providing a clear understanding of how artificial-neural-network-based models will be shaping the future of seam strength prediction in the denim manufacturing landscape. This type of evolution, based on ANN, will support and enhance the accuracy and efficiency of seam strength predictions by allowing models to discern intricate patterns and relationships within vast and diverse datasets.

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Section: Ai For Garment Propertiessupporting

confidence: 54%

Artificial-Neural-Network-Based Predicted Model for Seam Strength of Five-Pocket Denim Jeans: A Review

Zulfiqar,

Manzoor,

Ijaz

et al. 2024

Textiles

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show abstract

“…Furthermore, based on the internal and external information that flows in the network, the ANN is an adaptive system and changes its structure during the learning phase. 34 Selecting significant HVs for THV prediction. In order to decide the most significant HVs that affect the THV to the maximum possible, Pearson correlation was computed and a correlation matrix was established between the HVs and the THV obtained during subjective assessment.…”

Section: Methodsmentioning

confidence: 99%

Prediction of the tactile comfort of fabrics from functional finishing parameters using fuzzy logic and artificial neural network models

Tadesse

Loghin

Pîslaru

et al. 2019

Textile Research Journal

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This paper aims to predict the hand values (HVs) and total hand values (THVs) of functional fabrics by applying the fuzzy logic model (FLM) and artificial neural network (ANN) model. Functional fabrics were evaluated by trained panels employing subjective evaluation scenarios. Firstly, the FLM was applied to predict the HV from finishing parameters; then, the FLM and ANN model were applied to predict the THV from the HV. The estimation of the FLM on the HV was efficient, as demonstrated by the root mean square error (RMSE) and relative mean percentage error (RMPE); low values were recorded, except those bipolar descriptors whose values are within the lowermost extreme values on the fuzzy model. However, the prediction performance of the FLM and ANN model on THV was effective, where RMSE values of ∼0.21 and ∼0.13 were obtained, respectively; both values were within the variations of the experiment. The RMPE values for both models were less than 10%, indicating that both models are robust, effective, and could be utilized in predicting the THVs of the functional fabrics with very good accuracy. These findings can be judiciously utilized for the selection of suitable engineering specifications and finishing parameters of functional fabrics to attain defined tactile comfort properties, as both models were validated using real data obtained by the subjective evaluation of functional fabrics.

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“…ANN is a method used in forecasting in many subjects in textiles. It has been used in the estimation of body dimensions, fabric permeability, body types, thermal resistance of fabrics, needle temperature depending on machine speed, fabric elongation, convective and radiant heat transfer indexes of fabrics and many other issues until today [8][9][10][11][12][13][14]. In these studies, it was concluded that the ANN predicted expected outputs were sufficiently successful.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network-based Prediction Technique for Waterproofness of Seams Obtained by Using Fusible Threads

Karabay

Şenol

Öztürk

et al. 2022

Fibres &Amp; Textiles in Eastern Europe

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The aim of this study was to estimate waterproofness values of seams composed of the combination of fusible threads and antiwick sewing threads through artificial neural networks (ANN). Fusible threads were used to obtain waterproof seams for the first time. Therefore, estimating the value of the waterproofness variable with the help of models created from test values can contribute to accelerating the progress of further studies. Hence, ten different samples were prepared for two fabrics, and the waterproofness values of the seams obtained were tested using a Textest FX 3000 Hydrostatic Head Tester III. For the prediction of waterproofness values of the seams, the Levenberg-Marquardt backpropagation algorithm was used for artificial neural network pattern models with sigmoid and positive linear transfer functions. Finally, the ANN model was successful in estimating the waterproofness of the seams. The highest correlation coefficient was R = 0.95081 which indicated that the prediction made by the neural network model proved to be reliable.

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Application of artificial neural network (ANN) for prediction of fabrics’ extensibility

Cited by 7 publications

References 11 publications

Artificial-Neural-Network-Based Predicted Model for Seam Strength of Five-Pocket Denim Jeans: A Review

Artificial-Neural-Network-Based Predicted Model for Seam Strength of Five-Pocket Denim Jeans: A Review

Prediction of the tactile comfort of fabrics from functional finishing parameters using fuzzy logic and artificial neural network models

Artificial Neural Network-based Prediction Technique for Waterproofness of Seams Obtained by Using Fusible Threads

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