| People | Locations | Statistics |
|---|---|---|
| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Repon, Md. Reazuddin
Institute for Integrated Management of Material Fluxes and of Resources
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (12/12 displayed)
- 2024Exploring Ultrasonic‐Assisted Extraction and Eco‐Friendly Dyeing of Organic Cotton using <i>Syzygium cumini</i> Leaf Extractscitations
- 2024Effect of cotton‐polyester composite yarn on the physico‐mechanical and comfort properties of woven fabric
- 2024Effect of cotton-polyester composite yarn on the physico-mechanical and comfort properties of woven fabriccitations
- 2024Enhancing mechanical properties of natural waste-based composites for automobile and plastic industrycitations
- 2023Preparation and characterization of snake plant fiber reinforced composite: A sustainable utilization of biowastecitations
- 2023Preparation and characterization of snake plant fiber reinforced composite: A sustainable utilization of biowaste
- 2023Mapping the progress in natural fiber reinforced composites: Preparation, mechanical properties, and applicationscitations
- 2022A Prognostic Based Fuzzy Logic Method to Speculate Yarn Quality Ratio in Jute Spinning Industrycitations
- 2022Carbon-based polymer nanocomposites for electronic textiles (e-textiles)citations
- 2022Effect of Stretching on Thermal Behaviour of Electro-Conductive Weft-Knitted Composite Fabricscitations
- 2021Development of multi-layered weft-knitted fabrics for thermal insulationcitations
- 2017Ecological risk assessment and health safety speculation during color fastness properties enhancement of natural dyed cotton through metallic mordantscitations
Places of action
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article
A Prognostic Based Fuzzy Logic Method to Speculate Yarn Quality Ratio in Jute Spinning Industry
Abstract
<jats:p>Jute is a bio-degradable, agro-renewable, and widely available lingo cellulosic fiber having high tensile strength and initial modulus, moisture regain, good sound, and heat insulation properties. For these unique properties and eco-friendly nature of jute fibers, jute-based products are now widely used in many sectors such as packaging, home textiles, agro textiles, build textiles, and so forth. The diversified applications of jute products create an excellent opportunity to mitigate the negative environmental effect of petroleum-based products. For producing the best quality jute products, the main prerequisite is to ensure the jute yarn quality that can be defined by the load at break (L.B), strain at break (S.B), tenacity at break (T.B), and tensile modulus (T.M). However, good quality yarn production by considering these parameters is quite difficult because these parameters follow a non-linear relationship. Therefore, it is essential to build up a model that can cover this entire inconsistent pattern and forecast the yarn quality accurately. That is why, in this study, a laboratory-based research work was performed to develop a fuzzy model to predict the quality of jute yarn considering L.B, S.B, T.B, and T.M as input parameters. For this purpose, 173 tex (5 lb/spindle) and 241 tex (7 lb/spindle) were produced, and then L.B, S.B, T.B and T.M values were measured. Using this measured value, a fuzzy model was developed to determine the optimum L.B, S.B, T.B, and T.M to produce the best quality jute yarn. In our proposed fuzzy model, for 173 tex and 241 tex yarn count, the mean relative error was found to be 1.46% (Triangular membership) and 1.48% (Gaussian membership), respectively, and the correlation coefficient was 0.93 for both triangular and gaussian membership function. This result validated the effectiveness of the proposed fuzzy model for an industrial application. The developed fuzzy model may help a spinner to produce the best quality jute yarn.</jats:p>