PREDICTION OF COTTON YARN END BREAKAGE IN ROTOR SPINNING MACHINE USING THE ARTIFICIAL NEURAL NETWORK APPROACH

Authors

  • Syarif Iskandar Politeknik STTT Bandung
  • Valentinus Galih Vidia Putra Politeknik STTT Bandung
  • Hermansyah Hermansyah a:1:{s:5:"en_US";s:23:"Politeknik STTT Bandung";}

DOI:

https://doi.org/10.59052/edufisika.v7i1.19543

Keywords:

End breakage, Artificial neural networks (ANN)

Abstract

The purpose of this study is to predict the total end breakage per machine in 40 hrs of cotton yarn in a rotor spinning machine based on yarn count (yarn count), rotor speed (rotor speed), opening roller speed (opening roller speed) and residual trash content in draw frame sliver. . This study uses an artificial neural network (ANN) method in predicting a desired output. Furthermore, the artificial neural network is modeled with several model variations. From several modeling and testing carried out, starting from varying the number of nodes, the amount of alpha, the number of hidden layers, the number of iterations, it can be obtained that the results of using an artificial neural network with 1 hidden layer, 3 nodes, alpha of 0.3 with 50,000 iterations have more optimal results compared to the others because the resulting output is close to the target with an R-squared value of 0.984968. This shows that there is a large or close correlation between the actual variables and the variables in the artificial neural network. The novelty of this study is the use of ANN for the first time in predicting the total end breakage per machine in 40 hrs of cotton yarn in a rotor spinning machine. This method can facilitate top management and especially the Quality Control section in making decisions to set the parameters of the rotor machine in order to minimize the occurrence of yarn end breakage per machine in 40 hrs.

 

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References

Adiansyah, M. (2016). Pemodelan Dan Optimasi Proses Biofiksasi Karbondioksida Pada Biogas Menggunakan Java Moss (Taxiphylum Barbieri) Dengan Response Surface Methodology. Malang: Repository UB.

Ahmad, A. (2017). Mengenal Artificial Intelligence, Machine Learning, Neural Network, dan Deep Learning. Jurnal Teknologi Indonesia, 1-4.

Albert. (2009). Studi Penerapan Response Surface Methodology (RSM) dalam Proses Pembuatan Botol untuk Peningkatan Produktivitas Produk Botol di CV Bobofood. Medan: Universitas Sumatera Utara.

Das, A., & Ishtiaque, S. (2004). End Breakage in Rotor Spinning: Effect of Different Variables on Cotton Yarn End Breakage. AUTEX Research Journal, Vol. 4, No. 2, 52-59.

Disa, S. (2015). Penerapan Metode Regresi Linear dalam Pembuatan Perangkat Lunak Simulasi Target Penjualan. Jurnal Inspiration Vol. 5, No. 2, 82-89.

Fei, J., & Cheng, L. (2018). Adaptive Sliding Mode Control of Dynamic Systems Using Double Loop Recurrent Neural Network Structure. in IEEE Transactions on Neural Networks and Learning Systems, Vol. 29, No. 4, 1275 - 1286.

Gujarati, N. D. (2004). Basic Econometrics. New York: McGraw-Hill Companies, Incorporation. .

Hernawati, R., Putra, V., & Fauzi, I. (2015). Predicting the Actual Strength of Open-End Spun Yarn Using Mechanical Model. Applied Mechanics and Materials Vol. 780, 69-77.

Jones, R. (2002). Design and Analysis of Experiments (fifth edition), Douglas Montgomery, John Wiley and Sons. Quality and Reliability Engineering International Vol. 18 Issue 2, 163.

Maran, J., Sivakumar, V., Thirugnanasambandham, K., & Sridhar, R. (2013). Artificial Neural Network And Response Surface Methodology Modeling In Mass Transfer Parameters Predictions During Osmotic Dehydration Of Carica Papaya L. Alexandria Engineering Journal Vol. 52, Issue 3, 507-516.

Mita, A., & Basuki, N. (2019). Penerapan Metode Artificial Neural Network dalam Peramalan Kunjungan Ibu Hamil (K4). Jurnal Biometrika dan Kependudukan Vol. 8, No. 1, 11-20.

Pinar, N., & Babaarslan, O. (2003). Determining an Optimum Opening Roller Speed for Spinning Polyester/ Waste Blend Rotor Yarns . Textile Research Journal Vol. 73, 907-911.

Puspitaningrum, D. (2006). Pengantar Jaringan Saraf Tiruan. Yogyakarta: Andi.

Putra, V., & Wijoyono, A. (2017, November 16). Pemodelan Untuk Menentukan Hubungan Twist Terhadap Nomor Benang Nm pada Mesin Rotor Open-End Spinning Menggunakan Metode Lagrange dan Komputasi Numerik (Pendekatan Fisika). Dipetik Maret 09, 2022, dari Zenodo: https://zenodo.org/record/1438947#.YqsHuXZBzrc

Putra, V., Rosyid, M., & Maruto, G. (2016). A Simulation Model of Twist Influenced by Fibre Movement inside Yarn on Solenoid Coordinate. Global Journal of Pure and Applied Mathematics Vol. 12, No.1, 405-412.

Siang, J. (2005). Jaringan Syaraf Tiruan dan Pemrogramannya Menggunakan Matlab. Yogyakarta: ANDI.

Trommer, G. (1995). Rotor Spinning : Development, Process, Yarn, Machine Spinning Components, Technological Standard Values / Günter Trommer. Frankfurt/Main, Germany: Deutscher Fachverlag.

Wuryandari, M., & Afrianto, I. (2012). Perbandingan Metode Jaringan Syaraf Tiruan Perbandingan Metode Jaringan Syaraf Tiruan Perbandingan Metode Jaringan Syaraf Tiruan. Jurnal Komputer dan Informatika (KOMPUTA) Vol. 1, No. 1, 45-51.

Published

2022-06-30

How to Cite

Iskandar, S., Putra, V. G. V., & Hermansyah, H. (2022). PREDICTION OF COTTON YARN END BREAKAGE IN ROTOR SPINNING MACHINE USING THE ARTIFICIAL NEURAL NETWORK APPROACH. EduFisika: Jurnal Pendidikan Fisika, 7(1), 72-87. https://doi.org/10.59052/edufisika.v7i1.19543