A comparative prediction for tensile properties of ternary blended open-end rotor yarns using regression and neural network models

Erbil Yilmaz; Babaarslan Osman; Ilhan Ilhami

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A comparative prediction for tensile properties of ternary blended open-end rotor yarns using regression and neural network models

机译：基于回归和神经网络模型的三元混纺开口纱的拉伸性能比较预测

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摘要

This study focused on predicting tensile properties of PES/CV/PAN blended Open-End Rotor yarns. The effective factors were fiber blend ratios (six stages from 0 to 100%), linear density (three count levels), mixing method (carding machine and drawframe), and number of passages in drawframe (one and two times) as production parameters. We performed a stepwise multiple linear regression (MLR) analysis and established an artificial neural network (ANN) model that trained with backpropagation rule as Levenberg-Marquardt. Then, we conducted a comparative analysis for both models in terms of prediction performance. As a result, ANN has given a slightly better prediction values than MLR for breaking strength but significantly better prediction values for breaking elongation.

机译：这项研究的重点是预测PES / CV / PAN混纺开口纱的拉伸性能。有效的因素是纤维混合比（从0到100％的六个阶段），线密度（三个计数水平），混合方法（梳理机和并条机）以及并条机的通过次数（一和两次）作为生产参数。我们进行了逐步多元线性回归（MLR）分析，并建立了以反向传播规则训练的人工神经网络（ANN）模型，如Levenberg-Marquardt。然后，我们对两种模型的预测性能进行了比较分析。结果，ANN给出的断裂强度预测值比MLR略好，但断裂伸长率的预测值却明显好于MLR。

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来源
《The Journal of the Textile Institute》 |2018年第4期|560-568|共9页
作者
Erbil Yilmaz; Babaarslan Osman; Ilhan Ilhami;
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作者单位

Cukurova Univ, Text Engn Dept, Adana, Turkey;

Cukurova Univ, Text Engn Dept, Adana, Turkey;

Cukurova Univ, Text Engn Dept, Adana, Turkey;

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正文语种 eng
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关键词
Ternary blends; open-end yarn; prediction; multiple linear regression; artificial neural networks;

机译：三元混纺;开口纱;预测;多元线性回归;人工神经网络;

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A comparative prediction for tensile properties of ternary blended open-end rotor yarns using regression and neural network models

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