A Prognostic Based Fuzzy Logic Method to Speculate Yarn Quality Ratio in Jute Spinning Industry

Tamal Krishna Paul, Tazin Ibna Jalil, Md. Shohan Parvez, M. Repon, Ismail Hossain, Md. Abdul Alim, T. Islam, M. Jalil
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引用次数: 2

Abstract

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.
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基于预测的模糊逻辑方法推测黄麻纺纱质量比
黄麻是一种生物可降解、农业可再生、广泛使用的lingo纤维素纤维,具有高抗拉强度和初始模量、回潮性、良好的隔音和隔热性能。由于黄麻纤维的这些独特性能和环保特性,黄麻基产品现在被广泛应用于包装、家用纺织品、农用纺织品、建筑纺织品等许多领域。黄麻产品的多样化应用创造了一个极好的机会来减轻石油基产品对环境的负面影响。要生产出最优质的黄麻产品,主要前提是保证黄麻纱线的质量,这可以由断裂载荷(L.B)、断裂应变(S.B)、断裂韧性(T.B)和拉伸模量(T.M)来定义。然而,由于这些参数遵循非线性关系,因此在考虑这些参数的情况下生产优质纱线是相当困难的。因此,建立一个能够覆盖整个不一致花型的模型,准确预测成纱质量是十分必要的。因此,本研究以实验室为基础,以L.B、S.B、T.B和T.M为输入参数,建立模糊模型来预测黄麻纱线的质量。为此,分别生产173tex(5磅/锭)和241tex(7磅/锭),然后测量lb、S.B、T.B和T.M值。利用这一测量值,建立了模糊模型,以确定最佳的粗、粗、粗、粗、粗,从而生产出最佳质量的黄麻纱线。在我们提出的模糊模型中,173和241支纱的平均相对误差分别为1.46%(三角隶属度)和1.48%(高斯隶属度),三角隶属度和高斯隶属度函数的相关系数均为0.93。这一结果验证了所提出的模糊模型在工业应用中的有效性。所建立的模糊模型可以帮助纺纱厂生产出质量最好的黄麻纱线。
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