A normal boundary intersection approach to multiresponse robust optimization of the surface roughness in end milling process with combined arrays

2014 ◽  
Vol 38 (3) ◽  
pp. 628-638 ◽  
Author(s):  
T.G. Brito ◽  
A.P. Paiva ◽  
J.R. Ferreira ◽  
J.H.F. Gomes ◽  
P.P. Balestrassi
Keyword(s):  
Surface Roughness ◽  
Robust Optimization ◽  
End Milling ◽  
Milling Process ◽  
Normal Boundary Intersection ◽  
Normal Boundary ◽  
End Milling Process

Intelligent Monitoring and Prediction of Surface Roughness in Ball-End Milling Process

2011 ◽  
Vol 121-126 ◽  
pp. 2059-2063 ◽  
Author(s):  
Somkiat Tangjitsitcharoen ◽  
Angsumalin Senjuntichai
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Prediction Accuracy ◽  
End Milling ◽  
Milling Process ◽  
Ball End Milling ◽  
Roughness Model ◽  
Force Ratio ◽  
Tool Diameter ◽  
End Milling Process

In order to realize the intelligent machines, the practical model is proposed to predict the in-process surface roughness during the ball-end milling process by utilizing the cutting force ratio. The ratio of cutting force is proposed to be generalized and non-scaled to estimate the surface roughness regardless of the cutting conditions. The proposed in-process surface roughness model is developed based on the experimentally obtained data by employing the exponential function with five factors of the spindle speed, the feed rate, the tool diameter, the depth of cut, and the cutting force ratio. The prediction accuracy and the prediction interval of the in-process surface roughness model at 95% confident level are calculated and proposed to predict the distribution of individually predicted points in which the in-process predicted surface roughness will fall. All those parameters have their own characteristics to the arithmetic surface roughness and the surface roughness. It is proved by the cutting tests that the proposed and developed in-process surface roughness model can be used to predict the in-process surface roughness by utilizing the cutting force ratio with the highly acceptable prediction accuracy.

Predictive Model of Surface Roughness in High-Speed End Milling Process by Factorial Design of Experiments

2007 ◽  
Vol 339 ◽  
pp. 189-194
Author(s):  
Su Yu Wang ◽  
Xing Ai ◽  
Jun Zhao
Keyword(s):  
Surface Roughness ◽  
Design Of Experiments ◽  
Factorial Design ◽  
High Speed ◽  
End Milling ◽  
Milling Process ◽  
Regression Coefficients ◽  
Milling Parameters ◽  
Factorial Design Of Experiments ◽  
End Milling Process

Predictive models are presented for the surface roughness in high-speed end milling of 0.45%C steel and P20 die-mould steel based on statistical test and multiple-regression analysis. The data for establishing model is derived from experiments conducted on a high-speed machining centre by factorial design of experiments. The significances of the regression equation and regression coefficients are tested in this paper. The effects of milling parameters on surface roughness are investigated by analyzing the experimental curves.

Experimental Study on Micro End-Milling Process of Ti-6AL-4V Using Nanofluid Minimum Quantity Lubrication (MQL)

2014 ◽  
Author(s):  
Dae Hoon Kim ◽  
Pil-Ho Lee ◽  
Jung Sub Kim ◽  
Hyungpil Moon ◽  
Sang Won Lee
Keyword(s):  
Surface Roughness ◽  
Titanium Alloy ◽  
End Milling ◽  
Minimum Quantity Lubrication ◽  
Milling Process ◽  
Minimum Quantity ◽  
Nanofluid Minimum Quantity Lubrication ◽  
Micro End Milling ◽  
Milling Forces ◽  
End Milling Process

This paper investigates the characteristics of micro end-milling process of titanium alloy (Ti-6AL-4V) using nanofluid minimum quantity lubrication (MQL). A series of micro end-milling experiments are conducted in the meso-scale machine tool system, and milling forces, burr formations, surface roughness, and tool wear are observed and analyzed according to varying feed per tooth and lubrication conditions. The experimental results show that MQL and nanofluid MQL with nanodiamond particles can be effective to reduce milling forces, burrs and surface roughness during micro end-milling of titanium alloy. In particular, it is demonstrated that smaller size of nanodiamond particles — 35 nm — can be more effective to decrease burrs and surface roughness in the case of nanofluid MQL micro end-milling.

Optimization of Process Parameters for Cutting Force for Aluminium 7010 in CNC End Milling Process

2018 ◽  
Vol 10 (6) ◽  
Author(s):  
M. Kishanth ◽  
P. Rajkamal ◽  
D. Karthikeyan ◽  
K. Anand
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Process Parameters ◽  
End Milling ◽  
Milling Process ◽  
Machining Process ◽  
Depth Of Cut ◽  
Optimization Of Process Parameters ◽  
Cnc End Milling ◽  
End Milling Process

In this paper CNC end milling process have been optimized in cutting force and surface roughness based on the three process parameters (i.e.) speed, feed rate and depth of cut. Since the end milling process is used for abrading the wear caused is very high, in order to reduce the wear caused by high cutting force and to decrease the surface roughness, the optimization is much needed for this process. Especially for materials like aluminium 7010, this kind of study is important for further improvement in machining process and also it will improve the stability of the machine.

Predicting Surface Roughness with Respect to Process Parameters Using Regression Analysis Models in End Milling

2012 ◽  
Vol 576 ◽  
pp. 99-102 ◽  
Author(s):  
Erry Yulian Triblas Adesta ◽  
Muataz H.F. Al Hazza ◽  
M.Y. Suprianto ◽  
Muhammad Riza
Keyword(s):  
Surface Roughness ◽  
End Milling ◽  
Cutting Speed ◽  
Milling Process ◽  
Functional Attributes ◽  
Aisi H13 ◽  
Analysis Models ◽  
End Milling Process ◽  
Required Quality ◽  
Central Composite

Surface roughness affects the functional attributes of finished parts. Therefore, predicting the finish surface is important to select the cutting levels in order to reach the required quality. In this research an experimental investigation was conducted to predict the surface roughness in the finish end milling process with higher cutting speed. Twenty sets of data for finish end milling on AISI H13 at hardness of 48 HRC have been collected based on five-level of Central Composite Design (CCD). All the experiments done by using indexable tool holder Sandvick Coromill R490 and the insert was PVD coated TiAlN carbide. The experimental work performed to predict four different roughness parameters; arithmetic mean roughness (Ra), total roughness (Rt), mean depth of roughness (Rz) and the root mean square (Rq).

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