Scopus
YÖKSİS DOI Eşleşti
SJR Q1
Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method
Expert Systems with Applications · Mayıs 2011
YÖKSİS Kayıtları
Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method
Expert Systems with Applications · 2011 SCI-Expanded
Prof. Dr. MEHMET ÇUNKAŞ →
YÖKSİS Kayıtları — ISSN Eşleşmesi
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Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method
2011 ISSN: 09574174 SCI-Expanded
Prof. Dr. MEHMET ÇUNKAŞ →
A fuzzy clustering approach for finding similar documents using a novel similarity measure
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An Adaptive Network Based Fuzzy Inference System ANFIS for the prediction of stock market return The case of the Istanbul Stock Exchange
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Predicting direction of stock price index movement using artificial neural networks and support vector machines The sample of the Istanbul Stock Exchange
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Makale Bilgileri
ISSN09574174
Yayın TarihiMayıs 2011
Cilt / Sayfa38 · 5826-5832
Scopus ID2-s2.0-79151483285
Özet
Machine parts during their useful life are significantly influenced by surface roughness quality. The machining process is more complex, and therefore, it is very hard to develop a comprehensive model involving all cutting parameters. In this study, the surface roughness is measured during turning at different cutting parameters such as speed, feed, and depth of cut. Full factorial experimental design is implemented to increase the confidence limit and reliability of the experimental data. Artificial neural networks (ANN) and multiple regression approaches are used to model the surface roughness of AISI 1040 steel. Multiple regression and neural network-based models are compared using statistical methods. It is clearly seen that the proposed models are capable of prediction of the surface roughness. The ANN model estimates the surface roughness with high accuracy compared to the multiple regression model. © 2010 Elsevier Ltd. All rights reserved.
Yazarlar (2)
1
İlhan Asiltürk
ORCID: 0000-0002-8302-6577
2
Mehmet Çunkaş
Anahtar Kelimeler
Artificial neural networks
Predictive modeling
Surface roughness
Turning operations
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
Son Atıflar
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Scimago Dergi (ISSN Eşleşmesi)
Expert Systems with Applications
Q1
SJR Skoru1,854
H-Index290
YayıncıElsevier Ltd
ÜlkeUnited Kingdom
Artificial Intelligence (Q1)
Computer Science Applications (Q1)
Engineering (miscellaneous) (Q1)
Metrikler
353
Atıf
2
Yazar
4
Anahtar Kelime