Prediction of pilling grade of alkali-treated regenerated cellulosic fabric using fuzzy inference system

Muthusamy, Lekha Priya; Periyasamy, Aravin Prince; Govindan, Nalankilli

doi:10.1080/00405000.2021.2012909

Cited by 5 publications

(3 citation statements)

References 30 publications

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“…In most cases, pilling is produced by the staple fibers in the fabric coming apart at the surface because of the fabric being subjected to mechanical stress. The formation of fuzz is the first step in the pilling process, which can be identified by the gradual separation of fibers from a surface because of the mechanical action of the washing and wearing cycles [ 38 , 39 ]. The fiber frictions, their cross-sectional shape, thickness, stiffness, yarn hairiness, and abrasion resistance all contribute to the formation of fuzz.…”

Section: Sustainable Way To Reduce the Microfibers Generationsmentioning

confidence: 99%

“…These microfiber emissions can subsequently cause fuzz (see Figure 3) to form on the surface of the garment. During the process of pill formation, cellulosic fibers such as cotton and lyocell fibers initiate the fuzz formations which are followed to develop the fibrillations [39][40][41][42] and synthetic fibers such as polyester offer a way that is functionally equivalent to one another. The predicted mechanism for the development of microfibers is represented in Figure 3.…”

Section: Introductionmentioning

confidence: 99%

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Environmentally Friendly Approach to the Reduction of Microplastics during Domestic Washing: Prospects for Machine Vision in Microplastics Reduction

Periyasamy

2023

Toxics

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The increase in the global population is directly responsible for the acceleration in the production as well as the consumption of textile products. The use of textiles and garment materials is one of the primary reasons for the microfibers generation and it is anticipated to grow increasingly. Textile microfibers have been found in marine sediments and organisms, posing a real threat to the environment as it is invisible pollution caused by the textile industry. To protect against the damaging effects that microplastics can have, the formulation of mitigation strategies is urgently required. Therefore, the primary focus of this review manuscript is on finding an environmentally friendly long-term solution to the problem of microfiber emissions caused by the domestic washing process, as well as gaining an understanding of the various properties of textiles and how they influence this problem. In addition, it discussed the effect that mechanical and chemical finishes have on microfiber emissions and identified research gaps in order to direct future research objectives in the area of chemical finishing processes. In addition to that, it included a variety of preventative and minimizing strategies for reduction. Last but not least, an emphasis was placed on the potential and foreseeable applications of machine vision (i.e., quantification, data storage, and data sharing) to reduce the amount of microfibers emitted by residential washing machines.

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Section: Sustainable Way To Reduce the Microfibers Generationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Environmentally Friendly Approach to the Reduction of Microplastics during Domestic Washing: Prospects for Machine Vision in Microplastics Reduction

Periyasamy

2023

Toxics

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“…ANN are applied in various fields like as water resources engineering, traffic engineering 13 , detection of structural damage 14 , concrete strength prediction 15 , in textiles, yarn strength 16 , 17 , fabric pilling properties 18 , mechanical properties 19 , color strength prediction 20 , fabric finishing 21 , microplastic emission during domestic washing 22 .…”

Section: Introductionmentioning

confidence: 99%

Neural network model applied to electromagnetic shielding effectiveness of ultra-light Ni/Cu coated polyester fibrous materials

Periyasamy

Muthusamy

Militký

2022

Sci Rep

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The purpose of effective electromagnetic interference (EMI) shielding is to prevent EMI from smartphone, wireless, and utilization of other electronic devices. The electrical conductivity of materials strongly influences on the EMI shielding properties. In this work, mainly focus to predict the EMI shielding effectiveness on the ultralight weight fibrous materials by artificial neural network (ANN). Prior to the ANN modelling, the ultra-lightweight fibrous materials were electroplated with different concentration of Ni/Cu and then coated with different silanes. This work utilizes the algorithm to provide accurate quantitative values of EMI shielding effectiveness (EM SE). To compare its performance, the experimental and the predicted EM SE values were validated by root-mean-square error (RMSE), mean absolute percentage error (MAPE) values and correlation coefficient ‘r’. The proposed ANN results accurately predict the experimental data with correlation coefficients of 0.991 and 0.997. Further due to its simplicity, reliability as well as its efficient computational capability the proposed ANN model permits relatively fast, cost effective and objective estimates to be made of serving in this industry.

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Evaluation and optimization of pretreatment process for lyocell knitted fabric dyeing with reactive dyestuff

Akter,

Repon

et al. 2024

Cellulose

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Prediction of pilling grade of alkali-treated regenerated cellulosic fabric using fuzzy inference system

Cited by 5 publications

References 30 publications

Environmentally Friendly Approach to the Reduction of Microplastics during Domestic Washing: Prospects for Machine Vision in Microplastics Reduction

Environmentally Friendly Approach to the Reduction of Microplastics during Domestic Washing: Prospects for Machine Vision in Microplastics Reduction

Neural network model applied to electromagnetic shielding effectiveness of ultra-light Ni/Cu coated polyester fibrous materials

Evaluation and optimization of pretreatment process for lyocell knitted fabric dyeing with reactive dyestuff

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