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

Author(s):  
S.Y. Wang ◽  
Xing Ai ◽  
Jun Zhao
2007 ◽  
Vol 339 ◽  
pp. 189-194
Author(s):  
Su Yu Wang ◽  
Xing Ai ◽  
Jun Zhao

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.


2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
N. V. Dhandapani ◽  
V. S. Thangarasu ◽  
G. Sureshkannan

This research paper analyzes the effects of material properties on surface roughness, material removal rate, and tool wear on high speed CNC end milling process with various ferrous and nonferrous materials. The challenge of material specific decision on the process parameters of spindle speed, feed rate, depth of cut, coolant flow rate, cutting tool material, and type of coating for the cutting tool for required quality and quantity of production is addressed. Generally, decision made by the operator on floor is based on suggested values of the tool manufacturer or by trial and error method. This paper describes effect of various parameters on the surface roughness characteristics of the precision machining part. The prediction method suggested is based on various experimental analysis of parameters in different compositions of input conditions which would benefit the industry on standardization of high speed CNC end milling processes. The results show a basis for selection of parameters to get better results of surface roughness values as predicted by the case study results.


2013 ◽  
Author(s):  
◽  
Khaled A. M. Adem

This dissertation outlines research on studying the effects of machining parameters such that cutting speed, feed rate, axial depth of cut, radial depth of cut and helix angle on system dynamic stability and the surface quality of high-speed milling. With the use of structural tool modal parameters, the material cutting force coefficients and the axial depth of cut, the system can avoid the chatter phenomenon of the tool at high cutting speeds. The surface roughness finish in the milling process is determined by the machining parameters and tool structure dynamics. To perform high-speed milling, the chance of tool vibration (chatter phenomenon) which affects the cutting tool, must be minimized or eliminated. In this research, the linear and nonlinear mathematical force models including the effect of the helix angle are presented for an end-milling process. The linear force model includes cutting-edge coefficients. The cutting force coefficients are determined for an end-milling process using two methods, the average force method and the optimization technique method. The second method is developed to identify the cutting force coefficients in the milling process by forming the objective functions using the optimization technique to minimize the error between the experimental and the analytical forces. Moreover, this method produced a good force model that approximates the experimental force results, which compared with the average force method. The stability lobe diagrams are created using the analytical method to determine whether the cut is stable or unstable. In addition, simulations are performed to predict stability of the milling process. By comparing simulated and experimental results, the dynamics and stability of the milling operation can be easily identified before performing any cutting operation. The slot milling experiments show that while the system in the chatter region close to the stability limits and the axial depth of cut increased, the system changes from stable chatter to chaotic chatter. Furthermore, the nature of bifurcation in milling is investigated by performing experiments and simulations. The linear and nonlinear mathematical force models are used for simulating end-milling process. Simulated bifurcation diagrams are generated using both models and compared to experimental results. In addition, the effect of the feed rate on the location of the bifurcation point (start and end of bifurcation) is studied. By comparing simulated and experimental results, the simulation using a nonlinear force model is found more accurate in predicting the dynamics and stability of the milling operation. The applications of Taguchi and response surface methodologies (RSM) are used to minimize the surface roughness in the end milling process. Taguchi’s method for optimum selection of the milling process parameters is applied based on the signal to noise ratio and ANOVA analysis of the surface finish. A second-order model contains quadratic terms that have been created between the cutting parameters and surface roughness using response surface methodology (RSM). Surface roughness of the machined surfaces are measured and used to identify the optimum levels of the milling parameters. Based on Taguchi, ANOVA, and RSM analyses, the end milling process can be optimized to improve surface finish quality and machining productivity.


2011 ◽  
Vol 121-126 ◽  
pp. 2059-2063 ◽  
Author(s):  
Somkiat Tangjitsitcharoen ◽  
Angsumalin Senjuntichai

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.


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