Development of Models to Predict Tensile Strength of Cotton Woven Fabrics

Malik, Zulfiqar Ali; Malik, Mumtaz Hasan; Hussain, Tanveer; Ahmed, Farooq

doi:10.1177/155892501100600407

Cited by 12 publications

(12 citation statements)

References 17 publications

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“…The tear strength is usually a measure of the force (tensile stress) required to propagate a tear and is often used to give a direct assessment of the serviceability of the fabric (Teli et al, 2008). Tear strength can be tested in both warp and weft directions and it is considered one of the most important performance attributes of woven textiles (Malik et al, 2011). Most of the tear strength prediction studies employ linear models, which are rather rigid and thus fail when nonlinear relationships exist among the data attributes.…”

Section: Data Mining Applied To Fabricsmentioning

confidence: 99%

“…In (Kotb, 2009), linear regression models were used to predict the fabric tearing force based on 9 identified input features, concluding that tearing force is largely affected by the type and number of weft yarns, weft density, ground structure, and ground yarns, while the shape of the pile and the change in pile designation have minor effects. In another study, the linear regression was also used to predict the fabric tear strength in warp and weft direction for woven wool fabrics, obtaining a Pearson correlation between the actual and the predicted strength for warp and weft of 0.976 and 0.975, respectively (Malik et al, 2011). The same linear regression model was used in (Eltayib et al, 2016) to predict the relationship between fabric tear strength and other independent variables, such as yarn tensile strength, yarn count and fabric linear density.…”

Section: Data Mining Applied To Fabricsmentioning

confidence: 99%

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Predicting the Tear Strength of Woven Fabrics Via Automated Machine Learning: An Application of the CRISP-DM Methodology

Ribeiro

Pilastri²,

Moura

et al. 2020

Proceedings of the 22nd International Conference on Enterprise Information Systems

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Textile and clothing is an important world industry that is currently being transformed by the adoption of the Industry 4.0 concept. In this paper, we use Data Mining (DM) technology and the CRoss-Industry Standard Process for DM (CRISP-DM) methodology to model the textile testing process, which assures that products are safe and comply with regulations and client needs. Real-world data were collected from a Portuguese textile company, which has the goal to reduce the number of attempts they take in order to produce a woven fabric. Thus, predicting the outcome of a given test is beneficial to the company because it can reduce the number of physical samples that are needed to be produced when designing new fabrics. In particular, we target two important textile regression tasks: the tear strength in warp and weft directions. To better focus on feature engineering and data transformations, we adopt an Automated Machine Learning (AutoML) during the modeling stage of the CRISP-DM. Several iterations of the CRISP-DM methodology were employed, using different data preprocessing procedures (e.g., removal of outliers). The best predictive models were achieved after 2 (for warp) and 3 (for weft) CRISP-DM iterations.

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Section: Data Mining Applied To Fabricsmentioning

confidence: 99%

Section: Data Mining Applied To Fabricsmentioning

confidence: 99%

Predicting the Tear Strength of Woven Fabrics Via Automated Machine Learning: An Application of the CRISP-DM Methodology

Ribeiro

Pilastri²,

Moura

et al. 2020

Proceedings of the 22nd International Conference on Enterprise Information Systems

View full text Add to dashboard Cite

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“…Almetwally et al proposed a regression method to Compare Mechanical Properties of fabrics woven from compact and ring spun yarns [13]. Malik et al developed models to predict the tensile strength of cotton woven fabrics [14]. Kotb presented a deepened understanding of plain-woven fabrics using regression analysis [15].…”

Section: Introductionmentioning

confidence: 99%

Predicting the Tensile Properties of UV Degraded Nylon66/Polyester Woven Fabric Using Regression and Artificial Neural Network Models

Moezzi

Ghane

Semnani

2015

Journal of Engineered Fibers and Fabrics

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UV radiation is the major source of radiation in environmental conditions and affects many characteristics of exposed fabrics. Physical properties of polymeric material and fabric deteriorate when exposed to environmental conditions. The study of the changes in physical properties has been a major issue for researchers. Color absorption of degraded fabrics varies with exposure time. The main aim of this work was to predict the physical properties of UV degraded woven fabrics at different levels of exposure time. Samples of plain-woven fabrics were selected. The fabric consisted of nylon 66 as weft yarns and polyester as warp yarns. A UV light source was used to induce controlled degradation at different exposure times. The samples were dyed in identical conditions and the color values for all samples were measured using a spectrophotometer. The samples were then tested with tensile testing machine and stress-strain curves were obtained. Six parameters were considered in stress-strain curves. Exposure time caused the differences in the color values of the samples. This was used to evaluate the tensile behavior. Regression and artificial neural network methods were used to correlate each of the six parameters of tensile properties with color values. To validate the methods, experimental samples were tested with the tensile testing machine and the results were compared with the predicted values. The results show a good agreement between the experimental and predicted models.

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“…The internal geometry of a woven fabric is an important factor determining fabric mechanical behavior; deformability, draping, wrinkling and buckling are some of them. In addition to the comprehensive works on geometrical and mechanical analysis of textile materials [2][3][4] there are numerous recent works found in literatures such as those performed in [5][6] concerning many challenging aspects of woven structures geometrical-mechanical modeling even in relatively simple plain-weave fabrics.…”

Section: Introductionmentioning

confidence: 99%

Mechanical Behavior of Regular Twill Weave Structures; Part I: 3D Meso-Scale Geometrical Modelling

Avanaki

Jeddi

2015

Journal of Engineered Fibers and Fabrics

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The mechanical behavior of fabrics with different configurations has been investigated in many works by various approaches including the force equilibrium or energy methods. However, representing a suitable geometrical model for fabric structure is prerequisite to apply these approaches. This paper introduces an analytic 3D meso-scale geometrical modelling of regular twill weaves, in terms of a few structural parameters in 2D biaxial orthogonal woven fabrics. The model is proposed for the fabrics in their fully relaxed state considering their inherent skewness. For this purpose, a three dimensional structure is used to show this phenomenon. In this model, the yarns cross-section is assumed to be circular and the yarns path is assumed to be straight line (saw-tooth) in the unit cell which leads to the results that are in reasonable agreement with experimental data. These assumptions will be helpful in finding a close form solution for the mechanical behavior of woven structures. The proposed model has been verified by comparing its output with some experimental data for its areal mass and thickness in 2/2 twill fabric as a case study. By applying this model, the geometrical structural parameters such as skewness and weave angles as well as their total consumed yarns can be predicted theoretically. This model is a framework which will be used for estimating the initial deformation behavior of regular twill woven structures under uniaxial tensile loads in our forthcoming works.

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Development of Models to Predict Tensile Strength of Cotton Woven Fabrics

Cited by 12 publications

References 17 publications

Predicting the Tear Strength of Woven Fabrics Via Automated Machine Learning: An Application of the CRISP-DM Methodology

Predicting the Tear Strength of Woven Fabrics Via Automated Machine Learning: An Application of the CRISP-DM Methodology

Predicting the Tensile Properties of UV Degraded Nylon66/Polyester Woven Fabric Using Regression and Artificial Neural Network Models

Mechanical Behavior of Regular Twill Weave Structures; Part I: 3D Meso-Scale Geometrical Modelling

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