كنفرانس بين المللی مهندسی صنايع و سيستم ها

صفحه اصلی / یازدهمین كنفرانس بين المللی مهندسی صنايع و سيستم ها

تنظیم بهینه متغیرهای فرایند ریسندگی و بهبود کیفیت نخ با استفاده از الگوریتم بهینه ‏سازی گرگ خاکستری: پیاده ‏سازی روی ماشین کشش

نویسندگان :

حبیب امیری سوادرودباری¹ محسن رضاحسنی² محمدجواد عبقری³

1- دانشگاه صنعتی امیرکبیر 2- دانشگاه یزد 3- دانشگاه یزد

کلمات کلیدی :

ریسندگی نخ،کیفیت نخ،ماشین کشش،بهینه ‏سازی چندهدفه،بهینه ‏سازی گرگ خاکستری،شبکه عصبی مصنوعی

چکیده :

This study employed a systematic and algorithmic approach to optimize the drawing frame variables in the spinning line and enhance the quality of polyester spun yarn. Data on the yarn’s unevenness percentage (CVm%) and the imperfection index (IPI) were collected using Response Surface Methodology (RSM) design. Subsequently, two models, RSM and Artificial Neural Network (ANN), were developed to predict yarn properties and compared in terms of their accuracy. The results demonstrated that the ANN model exhibited higher accuracy than the RSM model. For multi-objective optimization, the Gray Wolf Optimization (GWO) algorithm was applied to improve yarn quality. Following optimization, the CVm% value decreased from 12.31 to 11.86, while the IPI index remained almost unchanged at 26.40, signifying the algorithm’s effectiveness in enhancing the overall yarn quality. Finally, a comparison between the GWO and Genetic Algorithm (GA) revealed that both algorithms produced similar results in optimizing the variables; however, the execution speed of GWO was considerably faster than that of GA. These findings highlight the potential of the GWO algorithm to optimize industrial processes with low computational cost and high accuracy.