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Journals / Politeknik Dergisi / 2026 / Cilt: 29 Sayı: 5

Benchmarking a Knowledge-Based Fuzzy with Data-Driven Machine Learning Models for Predicting Kerf Width in Laser Cutting

Lazer Kesimde Kesme Genişliğini Tahmin Etmek için Bilgi Tabanlı Bulanık Mantık Yönteminin Veri Odaklı Makine Öğrenmesi Modelleriyle Karşılaştırılması

Doğukan Zaif(MVD Makine Sanayi A. Ş.)0009-0004-4547-6341
Muhammed Arif Şen(KONYA TECHNICAL UNIVERSITY)0000-0002-6081-2102
Pages
1–13
Publication Date
2026-10-06
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

According to the results, the KW-MFL model reduced RMSE-MAE in kerf width estimation by 27.95%-27.60% to SVR and by 21.87%-19.05% to GPR, respectively.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study aims to compare the generalisation capabilities of data-driven common ML models with those of a knowledge-based fuzzy logic system under relatively small data constraints.
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Key Findings & Data:An experimental dataset was obtained for a 4 mm-thick S235JR sheet metal in which the cutting kerf width was measured across four different levels for laser cutting power, gas pressure, and focal length.
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Conclusion & Impact:According to the results, the KW-MFL model reduced RMSE-MAE in kerf width estimation by 27.95%-27.60% to SVR and by 21.87%-19.05% to GPR, respectively.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Lazer kesim · Kesme Genişliği · Tahmin

Abstract

This study aims to compare the generalisation capabilities of data-driven common ML models with those of a knowledge-based fuzzy logic system under relatively small data constraints. An experimental dataset was obtained for a 4 mm-thick S235JR sheet metal in which the cutting kerf width was measured across four different levels for laser cutting power, gas pressure, and focal length. A conventional ANOVA analysis was performed with a total of 64 data points. The gas pressure was found to have the highest contribution to kerf width at 38.71%. To estimate the kerf width in precision laser cutting, using the same 50 data for all models, the Kerf Width-Mamdani Fuzzy Logic (KW-MFL) model was constructed, and SVR, GPR which are data-driven ML methods, were trained. Each model validation was performed using 14 unseen datasets to rigorously assess its performance. According to the results, the KW-MFL model reduced RMSE-MAE in kerf width estimation by 27.95%-27.60% to SVR and by 21.87%-19.05% to GPR, respectively. The results revealed that data-driven models struggled with data scarcity and not exhibit satisfactory performance. The KW-MFL successfully compensated for uncertainties by integrating into its expert rule base, achieving the highest representational ability with the lowest prediction errors.

Özet

Bu çalışma, nispeten küçük veri kısıtlamaları altında, veri odaklı yaygın makine öğrenimi modellerinin genelleme yeteneklerini, bilgi tabanlı bulanık mantık sisteminin yetenekleriyle karşılaştırmayı amaçlamaktadır. 4 mm kalınlığında S235JR sac metal için deneysel bir veri seti elde edildi ve bu sette lazer kesim gücü, gaz basıncı ve odak uzaklığı için dört farklı seviyede kesim aralığı genişliği ölçüldü. Toplam 64 veri noktasıyla geleneksel bir ANOVA analizi gerçekleştirildi. Gaz basıncının kesim aralığı genişliğine en yüksek katkıyı (%38,71) sağladığı bulundu. Hassas lazer kesiminde kesim aralığı genişliğini tahmin etmek için, tüm modeller için aynı 50 veri kullanılarak, Kesim Aralığı Genişliği-Mamdani Bulanık Mantık (KW-MFL) modeli oluşturuldu ve veri odaklı makine öğrenimi yöntemleri olan SVR ve GPR eğitildi. Her modelin performansını titizlikle değerlendirmek için 14 farklı veri seti kullanılarak model doğrulaması yapıldı. Sonuçlara göre, KW-MFL modeli, kesme genişliği tahmininde RMSE-MAE değerlerimi SVR'ye kıyasla %27,95-%27,60 ve GPR'ye kıyasla %21,87-%19,05 oranında azalttı. Sonuçlar, veri odaklı modellerin veri yetersizliğiyle mücadele ettiğini ve tatmin edici performans sergileyemediğini ortaya koydu. KW-MFL, uzman kural tabanına entegre olarak belirsizlikleri başarıyla telafi etti ve en düşük tahmin hatalarıyla en yüksek temsil yeteneğine ulaştı.

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Cite This Article

Zaif, D., Şen, M. A. (2026). Benchmarking a Knowledge-Based Fuzzy with Data-Driven Machine Learning Models for Predicting Kerf Width in Laser Cutting. Politeknik Dergisi, Cilt: 29 Sayı: 5, 1–13. https://doi.org/10.2339/politeknik.1965192

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