Multiple Regression Model for Surface Roughness Using Full Factorial Design

被引：0

作者：

Dahbi, Samya ^{[1
]}

El Moussami, Haj ^{[1
]}

Ezzine, Latifa ^{[2
]}

机构：

[1] Moulay Ismail Univ, ENSAM Sch, Team Mech & Integrated Engn, POB 15290, Al Mansour 50000, Meknes, Morocco

[2] Moulay Ismail Univ, EST Sch, Team Modeling Control Syst & Telecommun, Toulal 50000, Meknes, Morocco

来源：

2015 INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND SYSTEMS MANAGEMENT (IESM) | 2015年

关键词：

Full Factorial Design; Multiple Regression; ANOVA analysis; surface roughness; cutting parameters; FINISH; STEEL; PREDICTION; HARDNESS;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

This paper describes the modeling of surface roughness in turning of AISI 1042 Steel at four cutting parameters: cutting speed, feed rate, depth of cut and tool nose radius. Full factorial design is implemented to investigate the effects of interactions of these parameters on surface roughness. By using the multiple regression method, we developed a model with high correlation coefficient of 99.55 % and error of 0.07. Moreover, a good agreement was observed between estimated and experimental surface roughness in this model. The effects of cutting parameters and their interactions on surface roughness were investigated by using the analysis of variance.

引用

页码：439 / 444

页数：6

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