Experimental Study on the Relationship between Surface Roughness and Cutting Parameters when Face Milling High Strength Steel

2010 ◽  
Vol 139-141 ◽  
pp. 782-787
Author(s):  
Yue Ding ◽  
Wei Liu ◽  
Xi Bin Wang ◽  
Li Jing Xie ◽  
Jun Han
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
High Strength Steel ◽  
Cutting Speed ◽  
High Strength ◽  
Face Milling ◽  
Depth Of Cut ◽  
Radial Depth ◽  
The Relationship ◽  
Axial Depth

In this study, surface roughness generated by face milling of 38CrSi high-strength steel is discussed. Experiments based on 24 factorial design and Box-Behnken design method are conducted to investigate the effects of milling parameters (cutting speed, axial depth of cut and radial depth of cut and feed rate) on surface roughness, and a second-order model of surface roughness is established by using surface response methodology (RSM); Significance tests of the model are carried out by the analysis of variance (ANOVA). The results show that the most important cutting parameter is feed rate, followed by radial depth of cut, cutting speed and axial depth of cut. Moreover, it is verified that the predictive model possesses highly significance by the variance examination at a level of confidence of 99%. And the relationship between surface roughness and the important interaction terms is nonlinear.

scholarly journals Investigate the Effect of Cutting Parameters on Surface Roughness When Milling X12m Steel

2021 ◽  
pp. 258-263
Author(s):  
Do Thi Kim Lien ◽  
Nguyen Dinh Man ◽  
Phung Tran Dinh
Keyword(s):  
Experimental Study ◽  
Surface Roughness ◽  
Feed Rate ◽  
Cutting Tool ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Face Milling ◽  
Negligible Effect ◽  
Depth Of Cut ◽  
The Relationship

In this paper, an experimental study on the effect of cutting parameters on surface roughness was conducted when milling X12M steel. The cutting tool used in this study is a face milling cutter. The material that is used to make the insert is the hard alloy T15K6. The cutting parameters covered in this study include the cutting speed, the feed rate and depth of cut. The experiments are performed in the form of a rotating center composite design. The analysis shows that for both Ra and Rz: (1) the feed rate has the greatest influence on the surface roughness while the depth of cut, the cutting speed has a negligible effect on the surface roughness. (2) only the interaction between the feed rate and the depth of the cut has a significant effect on both Ra and Rz while the interaction between the cutting speed and the feed rate, the interaction between the cutting speed and the depth of cut have a negligible effect on surface roughness. A regression equation showing the relationship between Ra, Rz, and cutting parameters has also been built in this study.

scholarly journals Optimizing Cutting Conditions for Minimum Surface Roughness in Face Milling of High Strength Steel Using Carbide Inserts

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Adel Taha Abbas ◽  
Adham Ezzat Ragab ◽  
Essam Ali Al Bahkali ◽  
Ehab Adel El Danaf
Keyword(s):  
Surface Roughness ◽  
Factorial Design ◽  
Feed Rate ◽  
High Strength Steel ◽  
High Strength ◽  
Face Milling ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Quadratic Term ◽  
Machining Parameters

A full factorial design technique is used to investigate the effect of machining parameters, namely, spindle speed(N), depth of cut(ap),and table feed rate(Vf),on the obtained surface roughness (RaandRt) during face milling operation of high strength steel. A second-order regression model was built using least squares method depending on the factorial design results to approximate a mathematical relationship between the surface roughness and the studied process parameters. Analysis of variance was conducted to estimate the significance of each factor and interaction with respect to the surface roughness. ForRa, the results show that spindle speed, depth of cut, and table feed rate have a significant effect on the surface roughness in both linear and quadratic terms. There is also an interaction between depth of cut and feed rate. It also appears that feed rate has the greatest effect on the data variation followed by depth of cut. ForRt, the results show that the table feed rate is the most effective factor followed by the depth of cut, while the spindle speed had a significant small effect only in its quadratic term. The conditions of minimumRaandRtare identified through least square optimization. Moreover, multiobjective optimization for minimizingRaand maximizing metal removal rateQis conducted and the results are presented.

Experimental Study of End Milling Force on Manganese Steel

2013 ◽  
Vol 718-720 ◽  
pp. 239-243
Author(s):  
Girma Seife Abebe ◽  
Ping Liu
Keyword(s):  
Cutting Force ◽  
Feed Rate ◽  
End Milling ◽  
Cutting Speed ◽  
Second Order ◽  
Manganese Steel ◽  
Depth Of Cut ◽  
Machining Deformation ◽  
Radial Depth ◽  
Axial Depth

Cutting force is a key factor influencing the machining deformation of weak rigidity work pieces. In order to reduce the machining deformation and improve the process precision and the surface quality, it is necessary to study the factors influencing the cutting force and build the regression model of cutting forces. This paper discusses the development of the first and second order models for predicting the cutting force produced in end-milling operation of modified manganese steel. The first and second order cutting force equations are developed using the response surface methodology (RSM) to study the effect of four input cutting parameters (cutting speed, feed rate, radial depth and axial depth of cut) on cutting force. The separate effect of individual input factors and the interaction between these factors are also investigated in this study. The received second order equation shows, based on the variance analysis, that the most influential input parameter was the feed rate followed by axial depth, and radial depth of cut. It was found that the interaction of feed with axial depth was extremely strong. In addition, the interactions of feed with radial depth; and feed rate with radial depth of cut were observed to be quite significant. The predictive models in this study are believed to produce values of the longitudinal component of the cutting force close to those readings recorded experimentally with a 95% confident interval.

Investigation of Chip and Surface Roughness when Milling AISI304 Stainless Steel

2017 ◽  
Vol 889 ◽  
pp. 152-158
Author(s):  
K. Kadirgama ◽  
K. Abou-El-Hossein
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Feed Rate ◽  
Prediction Models ◽  
Cutting Speed ◽  
Experimental Result ◽  
Quadratic Model ◽  
Depth Of Cut ◽  
Cutting Condition ◽  
Axial Depth

Stainless steel was used for many engineering applications. The optimum parameters needs to be identify to save the cutting tool usage and increase productivity. The purpose of this study is to develop the surface roughness mathematical model for AISI 304 stainless steel when milling using TiN (CVD) carbide tool. The milling process was done under various cutting condition which is cutting speed (1500, 2000 and 2500 rpm), feed rate (0.02, 0.03 and 0.04 mm/tooth) and axial depth (0.1, 0.2 and 0.3 mm). The first order model and quadratic model have been developed using Response Surface Method (RSM) with confident level 95%. The prediction models were comparing with the actual experimental results. It is found that quadratic model much fit the experimental result compare to linear model. In general, the results obtained from the mathematical models were in good agreement with those obtained from the machining experiments. Besides that, it is shown that the influence of cutting speed and feed rate are much higher on surface roughness compare to depth of cut. The optimum cutting speed, feed rate and axial depth is 2500 rpm, 0.0212 mm/tooth and 0.3mm respectively. Besides that, continues chip is produced at cutting speed 2500 rpm meanwhile discontinues chip produced at cutting speed 1500 rpm.

scholarly journals Analysis of tool wear and surface roughness in high-speed milling process of aluminum alloy Al6061

2021 ◽  
pp. 71-84
Author(s):  
Nhu-Tung Nguyen ◽  
Dung Hoang Tien ◽  
Nguyen Tien Tung ◽  
Nguyen Duc Luan
Keyword(s):  
Surface Roughness ◽  
Aluminum Alloy ◽  
Tool Wear ◽  
Feed Rate ◽  
High Speed ◽  
Cutting Speed ◽  
Milling Process ◽  
Face Milling ◽  
Depth Of Cut ◽  
High Speed Milling

In this study, the influence of cutting parameters and machining time on the tool wear and surface roughness was investigated in high-speed milling process of Al6061 using face carbide inserts. Taguchi experimental matrix (L9) was chosen to design and conduct the experimental research with three input parameters (feed rate, cutting speed, and axial depth of cut). Tool wear (VB) and surface roughness (Ra) after different machining strokes (after 10, 30, and 50 machining strokes) were selected as the output parameters. In almost cases of high-speed face milling process, the most significant factor that influenced on the tool wear was cutting speed (84.94 % after 10 machining strokes, 52.13 % after 30 machining strokes, and 68.58 % after 50 machining strokes), and the most significant factors that influenced on the surface roughness were depth of cut and feed rate (70.54 % after 10 machining strokes, 43.28 % after 30 machining strokes, and 30.97 % after 50 machining strokes for depth of cut. And 22.01 % after 10 machining strokes, 44.39 % after 30 machining strokes, and 66.58 % after 50 machining strokes for feed rate). Linear regression was the most suitable regression of VB and Ra with the determination coefficients (R2) from 88.00 % to 91.99 % for VB, and from 90.24 % to 96.84 % for Ra. These regression models were successfully verified by comparison between predicted and measured results of VB and Ra. Besides, the relationship of VB, Ra, and different machining strokes was also investigated and evaluated. Tool wear, surface roughness models, and their relationship that were found in this study can be used to improve the surface quality and reduce the tool wear in the high-speed face milling of aluminum alloy Al6061

Effect of Cutting Parameters on Minimum Quantity Lubrication Machining of High Strength Steel

2009 ◽  
Vol 626-627 ◽  
pp. 387-392 ◽  
Author(s):  
L.T. Yan ◽  
Song Mei Yuan ◽  
Qiang Liu
Keyword(s):  
Chip Formation ◽  
Feed Rate ◽  
High Strength Steel ◽  
Cutting Speed ◽  
Minimum Quantity Lubrication ◽  
High Strength ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Work Piece ◽  
Minimum Quantity

The cutting performance (tool wear, surface roughness of machined work-piece and chip formation)of wet, dry and Minimum Quantity Lubrication (MQL) machining when milling of high strength steel (PCrNi2Mo) using cemented carbide tools under different (cutting speed, depth of cut, feed rate) was analyzed. The experimental results showed that as the cutting speed, depth of cut and feed rate changed, MQL conditions provided the lowest flank wear and the highest surface quality. Chip formation produced under MQL conditions become more favorable in terms of color and shape. The results obtained prove the potential of using MQL technique in the milling process of high strength steel (PCrNi2Mo) for high cutting speed, feed rate and depth of cut.

scholarly journals Determination of Optimum Machining Condition for Surface Roughness Using Historical Data

2018 ◽  
Vol 7 (4.30) ◽  
pp. 73
Author(s):  
Mohd Shahfizal Mohd Ruslan ◽  
Haniff Abdul Rahman ◽  
Jaharah Abdul Ghani ◽  
Che Hassan Che Haron ◽  
Mohd Shahir Kassim ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Magnesium Alloy ◽  
Optimum Condition ◽  
Feed Rate ◽  
High Speed ◽  
Historical Data ◽  
Cutting Speed ◽  
Weight Ratio ◽  
Depth Of Cut ◽  
Radial Depth

Magnesium alloy is one of the lightest materials with a high strength to weight ratio and excellent machinability, which makes it attractive and suitable for various industrial applications such as automotive and aerospace components. For these particular industrial components, the end products require a mirror-like finish. This article details a statistical analysis about the effect of milling parameters on the surface roughness of Magnesium alloy AZ91D in the dry milling process. The historical data approach in the response surface methodology (RSM) was utilized to determine the cause and effect relationship between the input variables and output response. The effect of milling parameter studied was cutting speed (900 – 1400 m/min), feed rate (0.03 - 0.09 mm/tooth), and radial depth of cut (0.2 - 0.3 mm). The results confirmed that the interaction between feed rate and cutting speed is the primary factor controlling the surface evolution. The responses of various factors were plotted using a two-dimensional interaction graph and the cubic empirical model was developed at 95% confidence level. The optimum condition for achieving the minimum surface roughness was a cutting speed of 977 m/min, a feed rate of 0.02 mm/tooth, and an axial depth of cut of 0.29 mm. With this optimum condition, a surface arithmetic roughness of 0.054 μm is expected. This study confirmed that by milling AZ91D at high speed cutting, it is possible to eliminate the polishing process to achieve a super mirror-like finishing.

scholarly journals A Survey on the Effects of Cutting Parameters on Surface Roughness When Milling 40Cr Steel Using End Mill Cutter

2020 ◽  
Vol 5 (1) ◽  
pp. 33-36
Author(s):  
Nguyen Hong Son
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Regression Equations ◽  
Face Mill ◽  
Mill Cutter ◽  
The Relationship ◽  
40Cr Steel

In this paper, the empirical research on the effects of cutting parameters on surface roughness when milling 40Cr steel with face mill is conducted. The cutting parameters in this study include the cutting speed, feed rate and depth of cut. The tests are conducted in the form of Central Composite Design. The analysis shows that, for both Ra and Rz: (1) the feed rate has the greatest effect on surface roughness, followed by the degree of influence of the depth of cut, cutting speed with insignificant effects on surface roughness. (2) only the interaction between the feed rate and depth of cut have a significant effect on both Ra and Rz; the interaction between the cutting speed and feed rate, the interaction between the cutting speed and depth of cut have negligible effects on the surface roughness. In addition, the regression equations showing the relationship between Ra, Rz and cutting parameters is developed in this study.

Sensitivity Analysis and Optimization of Machining Parameters Based on Surface Roughness Prediction for AA6061

2014 ◽  
Vol 598 ◽  
pp. 181-188
Author(s):  
Elssawi Yahya ◽  
Guo Fu Ding ◽  
Sheng Feng Qin
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Cutting Speed ◽  
Original Data ◽  
Depth Of Cut ◽  
Work Piece ◽  
Surface Model ◽  
Machining Parameters ◽  
Machining System ◽  
The Relationship

Surface roughness is strongly affected by machining parameters. In the past few decades, many researchers have established the relationship between the surface roughness and machining parameters, but less attention has been paid to tool shape and geometry. In addition, the number of tool flutes was ignored, which affects in vibrations and machining system. Therefore, this study first-time includes the tool flutes in addition to cutting speed, depth of cut and feed rate as independent variables. Firstly, a set of machining experiments were conducted using AA6061 as a work piece material to provide original data. Response Surface Model (RSM) adopted to establish the relationship model of surface roughness and machining parameters using Minitab 16. Based on analysis of variance (ANOVA), the results show cutter flutes has higher significant followed by feed rate, depth of cut and cutting speed which has less significant. Finally, machining parameters were optimized to desired surface roughness, and optimization prediction error has limited values between-0.02 and 0.02μm.

The Study of Proper Conditions in Face Milling Palmyra Palm Wood and Coconut Wood Using Design of Experiment

2012 ◽  
Vol 488-489 ◽  
pp. 847-855
Author(s):  
S. Rawangwong ◽  
J. Chatthong ◽  
J. Rodjananugoon ◽  
W. Boonchouytan ◽  
R. Burapa
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Cutting Speed ◽  
Face Milling ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Depth Of Cut ◽  
Absolute Percentage Error ◽  
Margin Of Error ◽  
Main Factors

The purpose of this research was to investigate the effects of main factors on the surface roughness in face milling process palmyra palm wood and coconut wood by computer numerical controlled milling machine and using shell end mill cutting tools 6 edges. The main factors including speed, feed rate, depth of cut and angle of cut were investigated for the optimum surface roughness. The result of preliminary trial showed that the depth of cut and the angle of the cut had no effect on surface roughness. It was found from the experiment that the factors affecting surface roughness were feed and speed, with tendency for reduction of roughness value at a lower feed rate and greater cutting speed. Therefore, in the facing process for palmyra palm wood it was possible to determine a face milling condition by means of the equation Ra = 0.954 + 20.4 Feed + 0.00126 Speed. This equation was employed at a limited speed of 800-1200 rpm, and the feed rate of 0.03-0.05 mm/tooth. The result from the experiment of the mean absolute percentage error of the equation of surface roughness is 6.10% which is less than the margin of error, and is acceptable. For coconut wood it was found from the experiment that the factor affecting surface roughness was feed rate and cutting speed, with tendency for reduction of roughness value at lower feed rate and greater cutting speed. Therefore, in the face milling coconut wood it was possible determine a facing condition by means of the equation Ra = 4.72 - 0.000864 Speed + 0.00443 Feed. Leading this equation goes to use is in limitation cutting speed 1000-2000 rpm at feed rate 100-300 mm/min. The result from the experiment of mean absolute percentage error of the equation of surface roughness is 4.64% which is less than the margin of error, and is acceptable. As a result, the selection of optimal machining parameters can be greatly benefited to the Coconut wood furniture manufacturing industry in terms of productivity improvement.

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