Please use this identifier to cite or link to this item: http://hdl.handle.net/11452/22642
Title: Surface roughness model in machining hardened steel with cubic boron nitride cutting tool
Authors: Şahin, Yusuf
Uludağ Üniversitesi/Teknik Bilimler Meslek Yüksekokulu/Makine Programı.
0000-0002-9129-8935
Motorcu, Ali Rıza
G-5458-2017
6507857056
Keywords: Turning
CBN tools
Surface roughness
Response surface method
Regression
Prediction
Prediction
Wear
Machinability
Parameters
Vibrations
Design
Cubic boron nitride
Cutting tools
Machining
Surface roughness
Cutting speed
Feed rate
Response surface method
Steel
Materials science
Metallurgy & metallurgical engineering
Issue Date: 2008
Publisher: Elsevier Science
Citation: Şahin, Y. ve Motorcu A. R. (2008). ''Surface roughness model in machining hardened steel with cubic boron nitride cutting tool''. International Journal of Refractory Metals & Hard Materials, 26(2), 84-90.
Abstract: The surface roughness model was developed in terms of main cutting parameters such as cutting speed, feed rate and depth of cut, using response surface methodology. Machining tests were carried out in turning AISI 1050 hardened steels by cubic boron nitride (CBN) cutting tools under different conditions. The model predicting equations for surface roughness of R-a, R-z, and R-max were developed using an experimental data when machining steels. The results indicate that the feed rate was found out to be dominant factor on the surface roughness, but it decreased with decreasing cutting speed, feed rate, depth of cut for these tools. In addition, average surface finish of Ra value produced by CBN cutting tool was about 0.823 mu m when machining hardened steels. However, the Ra value decreased about 0.55 mu m in terms of trial conditions. Moreover, a good agreement between the predicted and experimental surface roughness was observed within reasonable limit.
URI: https://www.sciencedirect.com/science/article/pii/S0263436807000261
https://doi.org/10.1016/j.ijrmhm.2007.02.005
http://hdl.handle.net/11452/22642
ISSN: 0263-4368
Appears in Collections:Scopus
Web of Science

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