1 Department of Textile Engineering, National Institute of Textile Engineering and Research (NITER), Savar, Dhaka.
2 Department of Business Administration, The People’s University of Bangladesh.
Research Article
World Journal of Advanced Research and Reviews, 2022, 16(03), 283-293
Article DOI: 10.30574/wjarr.2022.16.3.1328
Received on 31 October 2022; revised on 02 December 2022; accepted on 05 December 2022
Bursting strength is an important parameter of knit fabrics. It depends on multiple factors. This study aims to determine the best machine learning model to predict bursting strength. Besides, determining the optimum GSM and yarn count to get best bursting strength using RSM (Response Surface Methodology). We tried 9 machine learning models. Among which, the XGBoost (Extreme Gradient Boosting), Random Forest and ANN models performed best having 99%, 85% and 80% R2 value, respectively. The Pearson correlation shows the most significant factors is stitch density, whereas porosity and GSM have most meaningful correlation according to scatterplot. In the same way, RSM determined the GSM and GSM square are significant for bursting strength having p-value > 0.10 (at 90% confidence level). The RSM also depicted the optimum GSM range is from 150 to 245 and yarn count is 24/1 Ne to 31/1 Ne. Again, the RSM found that the GSM, yarn count, and stitch length can influence the bursting strength significantly.
Bursting Strength; Machine Learning; Response Surface Methodology; Random Forest; Artificial Neural Network; Extreme Gradient Boosting
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Mohammad Mohsin Ul Hoque, Toufique Ahmed, Tazina Shams and Md. Ikramul Islam. Predicting bursting strength of single jersey 100% cotton plain knitted fabrics using different machine learning models. World Journal of Advanced Research and Reviews, 2022, 16(3), 283-293. Article DOI: https://doi.org/10.30574/wjarr.2022.16.3.1328