MELTBLOWN MAKİNELERİNDE ÜRETİLEN DOKUSUZ KUMAŞLARIN BASINÇ VERİMLİLİĞİNİN MAKİNE ÖĞRENMESİ YÖNTEMLERİ İLE TAHMİNLENMESİ VE PERFORMANS ANALİZİ
Abstract
Meltblown teknolojisi, termoplastik polimerlerin eritilip ince nozüllerden geçirilerek yüksek hızlı sıcak hava akımı ile mikro ve nanofiber formuna dönüştürülmesi esasına dayanan bir dokusuz üretim tekniğidir. Meltblown kumaşlar; özellikle medikal tekstiller, filtrasyon sistemleri, otomotiv parçaları ve endüstriyel temizlik ürünleri gibi birçok sektörde geniş bir kullanım alanına sahiptir. Bu çalışma, meltblown kumaş üretim makinelerinde, üretim kalitesini doğrudan etkileyen basınç parametresinin makine öğrenmesi algoritmaları ile tahmin edilmesini amaçlamaktadır. Çalışmanın yenilikçi yönü, meltblown üretim sürecine ait farklı proses girdilerinin ve üretim koşullarının, makine öğrenmesi modelleri ile ilişkilendirilerek kalite iyileştirme sürecine katkı sağlamasıdır. Çalışmada, kritik üretim parametreleri dikkate alınarak, laboratuvar ortamında oluşturulan veri kümesi kullanılmıştır. Veri kümesinin sınırlı boyutunu aşmak için üç farklı veri çoğaltma tekniği uygulanmış ve böylece modelleme için yeterli sayıda örnek elde edilmiştir. Ardından, bu veri kümeleri kullanılarak çeşitli makine öğrenmesi modelleri geliştirilmiş ve bu modellerin basınç parametresini tahmin etme performansları karşılaştırılmıştır. Son olarak, modellerin iç karar mekanizmalarını daha şeffaf hale getirmek amacıyla SHAP (SHapley Additive exPlanations) ve LIME (Local Interpretable Model-agnostic Explanations) gibi açıklanabilir yapay zekâ (Explainable Artificial Intelligence) yöntemleri kullanılmıştır. Elde edilen bulgular, makine öğrenmesi temelli modellerin meltblown üretim süreçlerinde kalite parametrelerinin tahmini açısından yüksek başarı sağladığını ortaya koymaktadır.
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PREDICTION OF PRESSURE EFFICIENCY OF NONWOVEN FABRICS PRODUCED IN MELTBLOWN MACHINES USING MACHINE LEARNING METHODS AND PERFORMANCE ANALYSIS
Abstract
Meltblown technology is a nonwoven production technique based on the principle of melting thermoplastic polymers, passing them through fine nozzles, and converting them into micro and nanofiber form using a high-speed hot air stream. Meltblown fabrics have a wide range of applications in many industries, particularly medical textiles, filtration systems, automotive parts, and industrial cleaning products. This study aims to predict the pressure parameter, which directly affects production quality in meltblown fabric production machines, using machine learning algorithms. The innovative aspect of the study is that it contributes to the quality improvement process by correlating different process inputs and production conditions specific to the meltblown production process with machine learning models. The study used a dataset created in a laboratory environment, taking into account critical production parameters. To overcome the limited size of the dataset, three different data augmentation techniques were applied, thereby obtaining a sufficient number of examples for modeling. Subsequently, various machine learning models were developed using these datasets, and the performance of these models in predicting the pressure parameter was compared. Finally, SHAP and LIME were used to make the internal decision mechanisms of the models more transparent. The findings reveal that machine learning-based models achieve high success in predicting quality parameters in meltblown production processes.
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- Dhiman, H. S., Deb, D., & Guerrero, J. M. (2019). Hybrid machine intelligent SVR variants for wind forecasting and ramp events. Renewable and Sustainable Energy Reviews, 108, 369-379. https://doi.org/10.1016/j.rser.2019.04.002
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- Gökçe, M. M., & Duman, E. (2024). A deep learning-based demand forecasting system for planning electricity generation. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 27(2), 511-522.
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- İlhan, A., Tümse, S., Bilgili, M., Yıldırım, A., & Şahin, B. (2025). Lstm And Anfis Machine Learning Algorithms In Estimating The Sea Water Temperature In Türkiye At Various Sea Locations. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(1), 322-333.
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- Loh, W.-Y. (2011). Classification and regression trees. WIREs Data Mining and Knowledge Discovery, 1(1), 14-23. https://doi.org/10.1002/widm.8
- Mosca, E., Szigeti, F., Tragianni, S., Gallagher, D., & Groh, G. (2022). SHAP-Based Explanation Methods: A Review for NLP Interpretability. İçinde N. Calzolari, C.-R. Huang, H. Kim, J. Pustejovsky, L. Wanner, K.-S. Choi, P.-M. Ryu, H.-H. Chen, L. Donatelli, H. Ji, S. Kurohashi, P. Paggio, N. Xue, S. Kim, Y. Hahm, Z. He, T. K. Lee, E. Santus, F. Bond, & S.-H. Na (Ed.), Proceedings of the 29th International Conference on Computational Linguistics (ss. 4593-4603). International Committee on Computational Linguistics. https://aclanthology.org/2022.coling-1.406/
- Natekin, A., & Knoll, A. (2013). Gradient boosting machines, a tutorial. Frontiers in Neurorobotics, 7. https://doi.org/10.3389/fnbot.2013.00021
- Patidar, N., Mishra, S., Jain, R., Prajapati, D., Solanki, A., Suthar, R., Patel, K., & Patel, H. (2024). Transparency in AI Decision Making: A Survey of Explainable AI Methods and Applications (SSRN Scholarly Paper No. 4766176). Social Science Research Network. https://papers.ssrn.com/abstract=4766176
- Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on Image Data Augmentation for Deep Learning. Journal of Big Data, 6(1), 60. https://doi.org/10.1186/s40537-019-0197-0
- Su, X., Yan, X., & Tsai, C.-L. (2012). Linear regression. WIREs Computational Statistics, 4(3), 275-294. https://doi.org/10.1002/wics.1198
- Thalpage, N. (2023). Unlocking the Black Box: Explainable Artificial Intelligence (XAI) for Trust and Transparency in AI Systems. Journal of Digital Art & Humanities, 4(1), 31-36. https://doi.org/10.33847/2712-8148.4.1_4
- Vimbi, V., Shaffi, N., & Mahmud, M. (2024). Interpreting artificial intelligence models: A systematic review on the application of LIME and SHAP in Alzheimer’s disease detection. Brain Informatics, 11(1), 10. https://doi.org/10.1186/s40708-024-00222-1
- Waegel, D. (2013). A Survey of Bootstrapping Techniques in Natural Language Processing. Department of Computer Science, University of Delaware, Literature Survey Reports.
- Wang, Y., Zhang, L., Wen, Z., Chen, C., Cao, X., & Doh Dinga, C. (2023). Optimization of the sustainable production pathways under multiple industries and objectives: A study of China’s three energy- and emission-intensive industries. Renewable and Sustainable Energy Reviews, 182, 113399. https://doi.org/10.1016/j.rser.2023.113399
- Wiens, M., Verone-Boyle, A., Henscheid, N., Podichetty, J. T., & Burton, J. (2025). A Tutorial and Use Case Example of the eXtreme Gradient Boosting (XGBoost) Artificial Intelligence Algorithm for Drug Development Applications. Clinical and Translational Science, 18(3), e70172. https://doi.org/10.1111/cts.70172
- Zhang, Z. (2016). Introduction to machine learning: K-nearest neighbors. Annals of Translational Medicine, 4(11), 218. https://doi.org/10.21037/atm.2016.03.37
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Artificial Intelligence (Other) |
| Journal Section | Research Article |
| Authors | |
| Publication Date | December 3, 2025 |
| Submission Date | July 23, 2025 |
| Acceptance Date | September 19, 2025 |
| Published in Issue | Year 2025 Volume: 28 Issue: 4 |
