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Journals / Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi / 2026 / Cilt: 28 Sayı: 2

Investigation of the effect of sanding parameters on surface roughness of S235 steel and PP plastic by response surface method and artificial neural networks

S235 Çelik ve PP Plastikte zımparalama parametrelerinin yüzey pürüzlülüğüne etkisinin yanıt yüzey metodu ve yapay sinir ağlarıyla incelenmesi

Yunus Emre Nehri(BALIKESIR UNIVERSITY)0000-0003-2119-9031
Melih Sarılıgil(Kemal Başaran Endüstri A.Ş.)0000-0002-1402-3049
Ali Oral(BALIKESİR ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ)0000-0002-9144-3821
Pages
573–587
Publication Date
2026-07-31
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AI Summary & Key Takeaways

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

It was concluded that sanding size was the most effective parameter on surface roughness.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, the surface roughness values obtained as a result of sanding the surfaces of S235 and polypropylene (PP) materials with different sanding parameters were investigated.
📊
Key Findings & Data:Sanding size (P60, P100, P180, and P320) and sanding time (15, 30, 45, and 60 seconds) were used as sanding parameters.
💡
Conclusion & Impact:It was concluded that sanding size was the most effective parameter on surface roughness.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Zımparalama · yüzey pürüzlülüğü · RSM

Abstract

In this study, the surface roughness values obtained as a result of sanding the surfaces of S235 and polypropylene (PP) materials with different sanding parameters were investigated. Sanding size (P60, P100, P180, and P320) and sanding time (15, 30, 45, and 60 seconds) were used as sanding parameters. The effect ratios on material roughness were determined by analysis of variance. It was concluded that sanding size was the most effective parameter on surface roughness. Mathematical models and artificial neural networks were created for prediction. Surface roughness decreased with increasing sanding size number and sanding time. In the applied parameters, it was measured that there was a decrease in surface roughness of over 80% for both materials. Optimum parameters were determined based on the criterion of minimizing the surface roughness (Ra) value. As a result of the optimisation with Response Surface Method (RSM), a 60-second sanding process with P320 sandpaper was determined as the optimum condition for achieving the minimum Ra value.

Özet

Bu çalışmada, S235 ve polipropilen (PP) malzemelerin yüzeylerinin farklı zımparalama parametreleriyle zımparalanması sonucunda elde edilen yüzey pürüzlülüğü değerleri incelenmiştir. Zımpara tane büyüklüğü (P60, P100, P180 ve P320) ile zımparalama süresi (15, 30, 45 ve 60 saniye) zımparalama parametreleri olarak kullanılmıştır. Zımpara parametrelerinin malzeme yüzey pürüzlülüğü üzerindeki etki oranları varyans analizi ile belirlenmiştir. Yüzey pürüzlülüğü üzerinde en etkili parametrenin zımpara tane büyüklüğü olduğu belirlenmiştir. Tahmin amacıyla matematiksel modeller ve yapay sinir ağları oluşturulmuştur. Zımpara tane büyüklüğüne ait numara ve zımparalama süresi arttıkça yüzey pürüzlülüğünün azaldığı görülmüştür. Uygulanan parametreler kapsamında, her iki malzeme için de yüzey pürüzlülüğünde %80’in üzerinde bir azalma ölçülmüştür. Optimum parametreler, yüzey pürüzlülüğü (Ra) değerinin minimize edilmesi kriterine göre belirlenmiştir. Yanıt Yüzey Yöntemi (RSM) ile gerçekleştirilen optimizasyon sonucunda, minimum Ra değerinin elde edilmesi için P320 zımpara ve 60 saniyelik zımparalama işlemi optimum koşul olarak belirlenmiştir.

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Cite This Article

Nehri, Y. E., Sarılıgil, M., Oral, A. (2026). Investigation of the effect of sanding parameters on surface roughness of S235 steel and PP plastic by response surface method and artificial neural networks. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi, Cilt: 28 Sayı: 2, 573–587. https://doi.org/10.25092/baunfbed.1864040

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