Predicting the physical properties of drawn Nylon-6 fibers using an artificial-neural-network model

Abstract

Low-oriented nylon-6 fibers were drawn in a multistage drawing process, during which the number of drawing steps and the temperature of each step were changed. The physical properties of these fibers were measured and compared with the values predicted by a multiple-linear-regression model. Moreover, six input variables and four output variables were used in an artificial neural network (ANN) to establish the logical relationships between the inputs and outputs. Attempts were also made to determine the effective parameters for each physical property and explain the observed trends. The results showed that the models based on the ANN performed well and provided stable responses in predicting combined interactions between independent variables. Keywords: drawing process, artificial neural network, modeling, physical properties Malo orientirana vlakna najlon 6 so bila vle~ena z ve~stopenjskim postopkom, pri~emer se je pri vsakem vleku spreminjala stopnja vle~enja in temperatura. Izmerjene vrednosti teh vlaken so bile primerjane z vrednostmi, napovedanimi z modelom multivariantne linearne regresije. Poleg tega je bilo v umetni nevronski mre`i (ANN) uporabljenih {est vhodnih spremenljivk in {tiri izhodne, da bi ugotovili logi~ne odvisnosti med vhodnimi in izhodnimi spremenljivkami. Posku{alo se je ugotoviti u~inkovite parametere za vsako fizikalno lastnost in razlo`iti opa`ene tendence. Rezultati so pokazali, da so modeli na osnovi ANN dobri in ponujajo stabilne odgovore pri predvidevanju kombiniranih interakcij neodvisnih spremenljivk. Klju~ne besede: postopek vle~enja, umetna nevronska mre`a, modeliranje, fizikalne lastnosti

MLR and ANN Approaches for Prediction of Synthetic/Natural Nanofibers Diameter in the Environmental and Medical Applications

“…However, these values were satisfactory because the electrospinning process and the diameter size of electrospinning nanofibers have high degrees of complexity 9 . The evidence showed in previous studies suggests similar findings [42][43][44] . Nurwaha and Wang (2013) compared the neuro-fuzzy inference systems (ANFIS) and support vector machines (SVMs), an MLR for evaluation of electrospinning nanofibers diameter.…”

“…The ANN techniques provide the advantage of modeling a nonlinear and complicated problem www.nature.com/scientificreports www.nature.com/scientificreports/ without the need to find suitable functional forms for the problem, and their neural network learning ability also equips them with high efficiency in nonlinear system modeling 43,45 . Together, these studies indicate that ANNs techniques carried out well and illustrated stable responses in predicting combined interactions between independent parameters [42][43][44] .…”

“…The ANN model showed well correlation and provided stable responses comparison to MLR. Overall, these results indicated that the ANN model would very useful for predicting combined interaction between independent variables 44 . The ANN techniques provide the advantage of modeling a nonlinear and complicated problem www.nature.com/scientificreports www.nature.com/scientificreports/ without the need to find suitable functional forms for the problem, and their neural network learning ability also equips them with high efficiency in nonlinear system modeling 43,45 .…”

Application of ANN and ANFIS in prediction of color strength of plasma-treated wool yarns dyed with a natural colorant

“…ANN has been used in several studies concerning the prediction of different properties of textile materials such as physical properties (Rahbar and Vadood (2015)). Moreover, many studies have been published on the prediction of dye and ion removal from wastewaters using ANN and ANFIS (Ghaedi and Vafaei, 2017; Souza et al , 2018).…”