Yarn tenacity modeling using artificial neural networks and development of a decision support system based on genetic algorithms

Yarn tenacity is one of the most important properties in yarn production. This paper focuses on modeling of the yarn tenacity as well as optimally determining the amounts of effective inputs to produce the desired yarn tenacity. The artificial neural network is used as a suitable structure for tenacity modeling of cotton yarn with 30 Number English. The empirical data was initially collected for cotton yarns. Then, the structure of the neural network was determined and its parameters were adjusted by the back propagation method. The efficiency and accuracy of the neural model was measured based on the error value and coefficient determination. The obtained experimental results show that the neural model could predicate the tenacity with less than 3.5% error. Afterwards, utilizing genetic algorithms, a new method is proposed for optimal determination of input values in the yarn production to reach the desired tenacity. We conducted several experiments for different ranges with various production cost functions. The proposed approach could find the best input values to reach the desired tenacity considering the production costs.