Surface roughness estimation for turning operation based on different regression models using vibration signals
Yükleniyor...
Dosyalar
Tarih
2018
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
On machined parts, major indication of surface quality is surface roughness and also surface quality is one of the most specifiedcustomer requirements. In the turning process, the importance of machining parameter choice is enhancing, as it controls the requiredsurface quality. To obtain the better surface quality, the most essential control parameters are tool overhang and tool geometry in turningoperations. The goal of this study was to develop an empirical multiple regression models for prediction of surface roughness (Ra) fromthe input variables in finishing turning of 42CrMo4 steel. The main input parameters of this model are tool overhang and tool geometrysuch as tool nose radius, approaching angle, and rake angle in negative direction. Regression analysis with linear, quadratic andexponential data transformation is applied so as to find the best suitable model. The best results according to comparison of modelsconsidering determination coefficient (R 2 ) are achieved with quadratic regression model. In addition, tool nose radius was determined asthe most effective parameter on turning by variance analysis (ANOVA). Cutting experiments and statistical analysis demonstrate that themodel developed in this work produces smaller errors than those from some of the existing models and have a satisfactory goodness in allthree models construction and verification.
Açıklama
Anahtar Kelimeler
Bilgisayar Bilimleri, Yapay Zeka, Cutting tool geometry, Tool-holder overhang
Kaynak
International Journal of Intelligent Systems and Applications in Engineering
WoS Q Değeri
Scopus Q Değeri
Cilt
6
Sayı
4
Künye
Neşeli, S., Yalçın, G., Yaldız, S. (2018). Surface Roughness Estimation for Turning Operation Based on Different Regression Models Using Vibration Signals. International Journal of Intelligent Systems and Applications in Engineering, 6(4), 282-288.
