2023-03-062023-03-062017Yıldırım, K. vd. (2017). ''Comparing the prediction capabilities of artificial neural network (ANN) and nonlinear regression models in pet-poy yarn characteristics and optimization of yarn production conditions''. Journal of Engineered Fibers and Fabrics, 12(3), 7-16.1558-9250https://doi.org/10.1177/15589250170120https://journals.sagepub.com/doi/10.1177/155892501701200302http://hdl.handle.net/11452/31347In the manufacture of yarn, predicting the effect of changing production conditions is vital to reducing defects in the end product. This study compares, for the first time, non-linear regression and artificial neural network (ANN) models in predicting 10 yarn properties shaped by the influence of winding speed, quenching air temperature and/or quenching air speed during production. A multilayer perceptron ANN model was created by training 81 patterns using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. The hyperbolic tangent, or TanH, activation function and logistic activation functions were used for the hidden and output layers respectively. Results showed that the ANN approach exhibited a greater prediction capability over the non-linear regression method. ANN simultaneously predicted all of the 10 final properties of a yarn; tensile strength, tensile strain, draw force, crystallinity ratio, dye uptake based on the colour strengths (K/S), brightness, boiling shrinkage and yarn evenness, more accurately than the non-linear regression model (R-2 = 0.97 vs. R-2 = 0.92). These results lend support to the idea that the ANN analysis combined with optimization can be used successfully to prevent production defects by fine tuning the production environment.eninfo:eu-repo/semantics/closedAccessMaterials scienceParametersAlgorithmsMathematical modelsNeural networksOptimizationYarnDefectsForecastsManufactureQuenchingChemical activationDefectsForecastingHyperbolic functionsLinear regressionManufactureNeural networksNonlinear programmingTensile strainWoolArtificial neural network modelsNon-linear regressionNon-linear regression methodNonlinear regression modelsPrediction capabilityProduction environmentsRegression analysisArticle0004173604000022-s2.0-85028661191716123Materials science, textilesYarns; Cotton Fibers; Weft