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Using regression models for predicting the product quality in a tubing extrusion process

Author

Listed:
  • Vicente García

    (Universidad Autónoma de Ciudad Juárez)

  • J. Salvador Sánchez

    (Universitat Jaume I)

  • Luis Alberto Rodríguez-Picón

    (Universidad Autónoma de Ciudad Juárez)

  • Luis Carlos Méndez-González

    (Universidad Autónoma de Ciudad Juárez)

  • Humberto de Jesús Ochoa-Domínguez

    (Universidad Autónoma de Ciudad Juárez)

Abstract

Quality in a manufacturing process implies that the performance characteristics of the product and the process itself are designed to meet specific objectives. Thus, accurate quality prediction plays a principal role in delivering high-quality products to further enhance competitiveness. In tubing extrusion, measuring of the inner and outer diameters is typically performed either manually or with ultrasonic or laser scanners. This paper shows how regression models can result useful to estimate both those physical quality indices in a tube extrusion process. A real-life data set obtained from a Mexican extrusion manufacturing company is used for the empirical analysis. Experimental results demonstrate that k nearest-neighbor and support vector regression methods (with a linear kernel and with a radial basis function) are especially suitable for predicting the inner and outer diameters of an extruded tube based on the evaluation of 15 extrusion and pulling process parameters.

Suggested Citation

  • Vicente García & J. Salvador Sánchez & Luis Alberto Rodríguez-Picón & Luis Carlos Méndez-González & Humberto de Jesús Ochoa-Domínguez, 2019. "Using regression models for predicting the product quality in a tubing extrusion process," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2535-2544, August.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:6:d:10.1007_s10845-018-1418-7
    DOI: 10.1007/s10845-018-1418-7
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    References listed on IDEAS

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    1. Hu, Chao & Jain, Gaurav & Zhang, Puqiang & Schmidt, Craig & Gomadam, Parthasarathy & Gorka, Tom, 2014. "Data-driven method based on particle swarm optimization and k-nearest neighbor regression for estimating capacity of lithium-ion battery," Applied Energy, Elsevier, vol. 129(C), pages 49-55.
    2. Biau, Gérard & Devroye, Luc & Dujmović, Vida & Krzyżak, Adam, 2012. "An affine invariant k-nearest neighbor regression estimate," Journal of Multivariate Analysis, Elsevier, vol. 112(C), pages 24-34.
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    Cited by:

    1. Hasan Tercan & Tobias Meisen, 2022. "Machine learning and deep learning based predictive quality in manufacturing: a systematic review," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 1879-1905, October.

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