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Journals / Çukurova Üniversitesi Mühendislik-Mimarlik Fakültesi Dergisi / 2008 / Cilt: 23 - Sayı: 2

Galetaj işleminin geri yayılmalı ve genel regresyonlu yapay sinir ağlarıyla kıyaslamalı modeli

Comparative modeling in low plasticity burnishing (LPB) process using back-propagation (BPN) and General Regression Networks (GRNN)

UĞUR EŞME
İREM ERSÖZ KAYA
Funda KAHRAMAN
Aysun SAĞBAŞ
AYÇA ÇAKMAK PEHLİVANLI
TURGAY İBRİKÇİ
Pages
1–13
DOI
—
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AI Summary & Key Takeaways

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

The experimental results showed that predicted results obtained by BPN were closest to the actual results.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Low plasticity burnishing (LPB), a plastic deformation process, is becoming more popular as a finishing process: thus, how to select the burnishing parameters to reduce the surface roughness and to increase the surface quality is especially crucial.
📊
Key Findings & Data:The ANN models of surface roughness parameters is developed with the burnishing conditions such as burnishing force, number of tool passes, feed rate and burnishing speed.
💡
Conclusion & Impact:The experimental results showed that predicted results obtained by BPN were closest to the actual results.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

Low plasticity burnishing (LPB), a plastic deformation process, is becoming more popular as a finishing process: thus, how to select the burnishing parameters to reduce the surface roughness and to increase the surface quality is especially crucial. This paper reports the use of back-propagation (BPN) and General Regression Neural Networks (GRNN) techniques to model the burnishing process. The ANN models of surface roughness parameters is developed with the burnishing conditions such as burnishing force, number of tool passes, feed rate and burnishing speed. The experimental results showed that predicted results obtained by BPN were closest to the actual results.

Özet

Galetaj ile soğuk yüzey isleme yüzey kalitesini arttırdığından günümüzde popular bir soğuk isleme prosesi haline gelmistir. Bu yüzden galetaj parametrelerinin doğru ve net bir sekilde seçilmedi gerekmektedir. Bu çalısmada geri yayılmalı ve genel regresyonlu yapay sinir ağları kullanılarak galetaj parametrelerinin (baskı kuvveti, paso sayısı, ilerleme hızı ve galetaj hızı) yüzey pürüzlülüğü üzerindeki etkileri kıyaslamalı bir model ile açıklanmaya çalısılmıstır. Sonuç olarak geri yayılmalı yapay sinir eğları ile tahmin edilen sonuçların gerçek sonuçlara çok yakın olduğu görülmüstür.

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Cite This Article

EŞME, U., KAYA, İ. E., KAHRAMAN, F., SAĞBAŞ, A., PEHLİVANLI, A. Ç., İBRİKÇİ, T. (2008). Galetaj işleminin geri yayılmalı ve genel regresyonlu yapay sinir ağlarıyla kıyaslamalı modeli. Çukurova Üniversitesi Mühendislik-Mimarlik Fakültesi Dergisi, Cilt: 23 - Sayı: 2, 1–13. https://ajindex.com/articles/350740

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