Prediction of Surface Roughness for AISI 304 Steel with Solid Carbide Tools in End Milling Process Using Regression and ANN Models

2014 ◽  
Vol 39 (11) ◽  
pp. 8065-8075 ◽  
Author(s):  
S. Kalidass ◽  
P. Palanisamy
Keyword(s):  
Surface Roughness ◽  
End Milling ◽  
Milling Process ◽  
Aisi 304 ◽  
Aisi 304 Steel ◽  
Solid Carbide ◽  
Ann Models ◽  
End Milling Process

Optimization and Prediction of Cutting Force and Surface Roughness in End Milling Process of AISI 304 Stainless Steel

2015 ◽  
Vol 813-814 ◽  
pp. 362-367 ◽  
Author(s):  
Darshan A. Patel ◽  
Jitendra M. Mistry ◽  
Vrushit P. Kapatel ◽  
Dhaval R. Joshi
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Feed Rate ◽  
End Milling ◽  
Milling Process ◽  
304 Stainless Steel ◽  
Depth Of Cut ◽  
Aisi 304 Stainless Steel ◽  
Aisi 304 ◽  
End Milling Process

The end milling process is most commonly used where the large amount material can be removed to produce almost final shape of component. The present work deals with the experimental study and optimization the machining parameter of AISI 304 stainless steel. The effects of spindle speed, feed rate and depth of cut have been studied on the cutting force and surface roughness using Taguchi’s 27 orthogonal arrays. Regression analyses were used to develop the model of response parameters. The analysis of the result shows, the surface roughness and the cutting force is increased with feed rate and depth of cut but decreased with increased the cutting speed. The ANOVA indicate the feed rate was the most dominate parameter on surface roughness and cutting force than speed and depth of cut.

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.

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