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Explainable machine learning for statistical prediction of polymer fiber properties using process parameters

Yıl 2025, , 1021 - 1048, 24.06.2025

Kevser Kübra Kırboğa , Büşra Boz , Ferda Mindivan

https://doi.org/10.15672/hujms.1608393

Öz

The modeling and optimization of electrospinning parameters are essential for controlling the fiber diameter and material properties. This study uses machine learning to examine the effects of multiple electrospinning parameters on fiber diameter. Ten regression models were evaluated, with hyperparameter optimization performed using grid search cross-validation and Bayesian optimization with multiple fold configurations. The Random Forest model demonstrated superior performance (root mean square error = 129.308, coefficient of determination = 0.542, mean absolute error = 104.014, mean absolute percentage error = 0.371). Further improvement was achieved through Bayesian optimization (root mean square error = 127.400, coefficient of determination = 0.555, mean absolute percentage error = 0.360). Extreme Gradient Boosting and Gradient Boosting also showed high accuracy, while linear models performed poorly. The Shapley Additive Explanations analysis identified rotational speed as the most influential parameter (value = 0.473), followed by flow rate (0.36), porosity (0.32) and needle diameter (0.27), all positively affecting fiber diameter. In contrast, voltage (-0.24), temperature (-0.19), towing (-0.14), and humidity (-0.13) showed negative impacts. Experimentally, Polycaprolactone (Molecular number = 80,000) nanofibers were manufactured at three rotation speeds (150, 450 and 750 revolutions per minute), resulting in fiber diameters of 100.09, 154.0, and 175.45 nanometers, respectively. These findings reveal complex interactions between the electrospinning parameters and the fiber morphology, demonstrating the potential of machine learning to optimize nanofiber production.

Anahtar Kelimeler

Electrospinning explainable machine learning machine learning polycaprolactone prediction shap

Kaynakça

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Yıl 2025, , 1021 - 1048, 24.06.2025

Kevser Kübra Kırboğa , Büşra Boz , Ferda Mindivan

https://doi.org/10.15672/hujms.1608393

Öz

Kaynakça

  • [1] J. A Ilemobayo, O. Durodola, O. Alade, O. J Awotunde, A. T Olanrewaju, O. Falana, A. Ogungbire, A. Osinuga, D. Ogunbiyi, A. Ifeanyi, I. E Odezuligbo and O. E Edu, Hyperparameter tuning in machine learning: a comprehensive review, Journal of Engineering Research and Reports 26 (6), 388-395, 2024.
  • [2] A. Al-Abduljabbar and I. Farooq, Electrospun polymer nanofibers: processing, properties, and applications, Polymers 15 (1), 2023.
  • [3] N. Alharbi, A. Daraei, H. Lee and M. Guthold, The effect of molecular weight and fiber diameter on the mechanical properties of single, electrospun pcl nanofibers, Materials Today Communications 35, 105773, 2023.
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  • [5] Z. Asvar, E. Mirzaei, N. Azarpira, B. Geramizadeh and M. Fadaie, Evaluation of electrospinning parameters on the tensile strength and suture retention strength of polycaprolactone nanofibrous scaffolds through surface response methodology, J Mech Behav Biomed Mater 75, 369-378, 2017.
  • [6] M. Bartnikowski, T.R. Dargaville, S. Ivanovski and D.W. Hutmacher, Degradation mechanisms of polycaprolactone in the context of chemistry, geometry and environment, Progress in Polymer Science 96, 1-20, 2019.
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  • [13] M. Chen, H. Michaud and S. Bhowmick, Controlled vacuum seeding as a means of generating uniform cellular distribution in electrospun polycaprolactone (pcl) scaffolds, J Biomech Eng 131 (7), 074521, 2009.
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Toplam 91 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular İstatistiksel Veri Bilimi, Nicel Karar Yöntemleri
Bölüm İstatistik
Yazarlar

Kevser Kübra Kırboğa 0000-0002-2917-8860

Büşra Boz 0009-0004-3923-7539

Ferda Mindivan 0000-0002-6046-2456

Erken Görünüm Tarihi 25 Mayıs 2025
Yayımlanma Tarihi 24 Haziran 2025
Gönderilme Tarihi 27 Aralık 2024
Kabul Tarihi 17 Nisan 2025
Yayımlandığı Sayı Yıl 2025

Kaynak Göster

APA Kırboğa, K. K., Boz, B., & Mindivan, F. (2025). Explainable machine learning for statistical prediction of polymer fiber properties using process parameters. Hacettepe Journal of Mathematics and Statistics, 54(3), 1021-1048. https://doi.org/10.15672/hujms.1608393
AMA Kırboğa KK, Boz B, Mindivan F. Explainable machine learning for statistical prediction of polymer fiber properties using process parameters. Hacettepe Journal of Mathematics and Statistics. Haziran 2025;54(3):1021-1048. doi:10.15672/hujms.1608393
Chicago Kırboğa, Kevser Kübra, Büşra Boz, ve Ferda Mindivan. “Explainable Machine Learning for Statistical Prediction of Polymer Fiber Properties Using Process Parameters”. Hacettepe Journal of Mathematics and Statistics 54, sy. 3 (Haziran 2025): 1021-48. https://doi.org/10.15672/hujms.1608393.
EndNote Kırboğa KK, Boz B, Mindivan F (01 Haziran 2025) Explainable machine learning for statistical prediction of polymer fiber properties using process parameters. Hacettepe Journal of Mathematics and Statistics 54 3 1021–1048.
IEEE K. K. Kırboğa, B. Boz, ve F. Mindivan, “Explainable machine learning for statistical prediction of polymer fiber properties using process parameters”, Hacettepe Journal of Mathematics and Statistics, c. 54, sy. 3, ss. 1021–1048, 2025, doi: 10.15672/hujms.1608393.
ISNAD Kırboğa, Kevser Kübra vd. “Explainable Machine Learning for Statistical Prediction of Polymer Fiber Properties Using Process Parameters”. Hacettepe Journal of Mathematics and Statistics 54/3 (Haziran 2025), 1021-1048. https://doi.org/10.15672/hujms.1608393.
JAMA Kırboğa KK, Boz B, Mindivan F. Explainable machine learning for statistical prediction of polymer fiber properties using process parameters. Hacettepe Journal of Mathematics and Statistics. 2025;54:1021–1048.
MLA Kırboğa, Kevser Kübra vd. “Explainable Machine Learning for Statistical Prediction of Polymer Fiber Properties Using Process Parameters”. Hacettepe Journal of Mathematics and Statistics, c. 54, sy. 3, 2025, ss. 1021-48, doi:10.15672/hujms.1608393.
Vancouver Kırboğa KK, Boz B, Mindivan F. Explainable machine learning for statistical prediction of polymer fiber properties using process parameters. Hacettepe Journal of Mathematics and Statistics. 2025;54(3):1021-48.

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