Prakash Khude
@mlvtec.in
Assistant Professor, Department of Textile Technology
MLV Textile and Engineering College, Bhilwara
RESEARCH INTERESTS
Textile Technology, Weaving, Spinning, Knitting, Antibacterial Textile, Technical Textile
Scopus Publications
Scopus Publications
Prakash Khude, Abhijit Majumdar, Bhupendra Singh Butola, and Rajib Bhattacharyya
Springer Science and Business Media LLC
Prakash Khude, Abhijit Majumdar, and Bhupendra Singh Butola
Springer Science and Business Media LLC
Antibacterial activity of knitted fabrics has been modelled and predicted by using two soft computing approaches, namely artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS). Four parameters, namely proportion of polyester–silver nanocomposite fibres in yarn, yarn count (diameter), machine gauge and type of fabric (100% polyester or 50:50 polyester–cotton), were used as input parameters for predicting antibacterial activity of knitted fabrics. For each of the input parameters, two fuzzy sets (low and high) were considered to reduce the complexity of ANFIS model. The sixteen linguistic fuzzy rules trained by ANFIS were able to explain the relationship between input parameters and antibacterial activity. A comparison between ANN and ANFIS models has also been presented. Both the models predicted the antibacterial activity of knitted fabrics with very good prediction accuracy in the training and testing data sets with coefficient of determination greater than 0.92 and mean absolute prediction error less than 5%. The robustness of the prediction results against data partitioning between training and testing sets has also been investigated. It is found that prediction accuracy of both the models was quite robust with ANFIS showing better performance with lesser number of training data.
Prakash Khude, Abhijit Majumdar, and Bhupendra Singh Butola
Springer Science and Business Media LLC
Leveraging the antibacterial properties of polyester-cotton knitted fabrics has been attempted in this research by admixture of small proportion of polyester-silver nanocomposite fibres. Polyester-cotton (50:50) yarns were spun by mixing 10, 20 and 30 % (wt.%) polyester-silver nanocomposite fibres with normal polyester fibres so that overall proportion of polyester fibre becomes 50 %. The proportion of cotton fibre was constant (50 %) in all the yarns. Three parameters, namely blend proportion (wt.%) of nanocomposite fibres, yarn count and knitting machine gauge were varied, each at three levels, for producing 27 knitted fabrics. Polyester-cotton knitted fabrics prepared from polyester-silver nanocomposite fibres showed equally good antibacterial activity (65-99 %) against both S. aureus and E. coli bacteria. Antibacterial properties were enhanced with the increase in the proportion of polyester-silver nanocomposite fibres, yarn coarseness and increased compactness of knitted fabrics. Yarn count and blend proportion of nanocomposite fibre were found to have very dominant influence in determining the antibacterial properties of knitted fabrics.