Prediction of seam strength of cotton canvas fabric using fuzzy logic

• Explores the application of fuzzy logic in the predictive modeling of seam strength (warp and weft way) in cotton canvas fabric, considering thread linear density from (27-80 Tex) and stitch per inch (8-15) as input parameters. • The fuzzy logic model was developed using MATLAB's Mamdani fuzzy inference system, with triangular membership functions to simulate the relationship between these input and output parameters. • Results indicate that the fuzzy logic model offers high predictive accuracy, as demonstrated by coefficients of determination (R²) of 0.9841 for warp way seam strength and 0.9888 for weft way seam strength. • Provides valuable insights into the application of fuzzy logic in textile engineering, particularly in the areas of quality control and performance evaluation for textile manufacturing processes. This study investigates the application of fuzzy logic in predicting seam strength in cotton plain canvas fabric, focusing on both warp and weft directions. The precise prediction of seam strength is crucial for manufacturers to uphold quality standards, enhance production efficiency, and minimize waste. The fuzzy logic model in this study uses thread linear density and stitch per inch as input parameters and warp and weft seam strength as output variables. The modeling was conducted using MATLAB, specifically utilizing the Mamdani fuzzy inference system with triangle membership functions. The fuzzy logic model was found to be very accurate, as shown by coefficients of determination (R 2 ) of 0.9841 for the warp way and 0.9888 for the weft way, along with correlation coefficients (R) of 0.992 and 0.9944. The mean absolute percentage error (MAPE) was calculated to be 4.8719 % for the warp way and 4.7561 % for the weft way, each below 5 %, underscoring the model's reliability and robustness in seam strength prediction. This research provides findings with substantial implications for the textile industry, where the application of predictive models is on the rise to enhance production efficiency and product quality. Manufacturers can improve their ability to forecast regarding fabric properties and adjust production processes through the implementation of fuzzy logic models. This approach is consistent with current industry trends emphasizing automation and digitalization, wherein predictive models are essential for facilitating smart manufacturing and quality control.