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Diameter prediction and optimization of hot extrusion-synthesized polypropylene filament using statistical and soft computing techniques

Author

Listed:
  • Pauline Ong

    (Universiti Tun Hussein Onn Malaysia)

  • Choon Sin Ho

    (Universiti Tun Hussein Onn Malaysia)

  • Desmond Daniel Vui Sheng Chin

    (Universiti Tun Hussein Onn Malaysia)

  • Chee Kiong Sia

    (Universiti Tun Hussein Onn Malaysia)

  • Chuan Huat Ng

    (Universiti Tun Hussein Onn Malaysia)

  • Md Saidin Wahab

    (Universiti Tun Hussein Onn Malaysia)

  • Abduladim Salem Bala

    (Universiti Tun Hussein Onn Malaysia)

Abstract

In this study, statistical and soft computing techniques were developed to investigate effect of process parameters on diameter of extruded filament made of polypropylene in hot extrusion. A multi-factors experiment was designed with process parameters of screw speed, roller speed and die temperature. According to the design matrix, twenty four experiments were conducted. The diameter of the extruded plastic filament was measured in each experiment. Subsequently, statistical analysis was used to identify significant factors on diameter of extruded filament. Predictive models of response surface methodology (RSM) and radial basis function neural network (RBFNN) were applied to predict the diameter of extruded filament. The optimal process parameters to maintain the diameter of the filament closest to the target value were identified using the cuckoo search algorithm (CSA), and particle swarm optimization (PSO). Performance analysis demonstrated the superior predictive ability of both models, in which the prediction errors of 0.0245 and 0.0029 (in terms of mean squared error) were obtained by RSM and RBFNN, respectively. Considering the optimization methods, the optimization approaches of using CSA and PSO were promising, in which average relative error of 1.28% was obtained in confirmation tests.

Suggested Citation

  • Pauline Ong & Choon Sin Ho & Desmond Daniel Vui Sheng Chin & Chee Kiong Sia & Chuan Huat Ng & Md Saidin Wahab & Abduladim Salem Bala, 2019. "Diameter prediction and optimization of hot extrusion-synthesized polypropylene filament using statistical and soft computing techniques," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1957-1972, April.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:4:d:10.1007_s10845-017-1365-8
    DOI: 10.1007/s10845-017-1365-8
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    References listed on IDEAS

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    1. Hamed Bakhtiari & Mahdi Karimi & Sina Rezazadeh, 2016. "Modeling, analysis and multi-objective optimization of twist extrusion process using predictive models and meta-heuristic approaches, based on finite element results," Journal of Intelligent Manufacturing, Springer, vol. 27(2), pages 463-473, April.
    2. Doriana M. D’Addona & A. M. M. Sharif Ullah & D. Matarazzo, 2017. "Tool-wear prediction and pattern-recognition using artificial neural network and DNA-based computing," Journal of Intelligent Manufacturing, Springer, vol. 28(6), pages 1285-1301, August.
    3. K. E. K. Vimal & S. Vinodh & A. Raja, 2017. "Optimization of process parameters of SMAW process using NN-FGRA from the sustainability view point," Journal of Intelligent Manufacturing, Springer, vol. 28(6), pages 1459-1480, August.
    4. Emel Kuram & Babur Ozcelik, 2016. "Micro-milling performance of AISI 304 stainless steel using Taguchi method and fuzzy logic modelling," Journal of Intelligent Manufacturing, Springer, vol. 27(4), pages 817-830, August.
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    Cited by:

    1. Dawei Zhao & Mikhail Ivanov & Yuanxun Wang & Dongjie Liang & Wenhao Du, 2021. "Multi-objective optimization of the resistance spot welding process using a hybrid approach," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2219-2234, December.

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