Optimization of surface roughness in ball-end milling using teaching-learning-based optimization and response surface methodology

2016 ◽  
Vol 231 (14) ◽  
pp. 2596-2607 ◽  
Author(s):  
Mithilesh K Dikshit ◽  
Asit B Puri ◽  
Atanu Maity
Keyword(s):  
Surface Roughness ◽  
Process Parameters ◽  
End Milling ◽  
Cutting Speed ◽  
Cutting Process ◽  
Depth Of Cut ◽  
Radial Depth ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Axial Depth

Surface roughness is one of the most important requirements of the finished products in machining process. The determination of optimal cutting parameters is very important to minimize the surface roughness of a product. This article describes the development process of a surface roughness model in high-speed ball-end milling using response surface methodology based on design of experiment. Composite desirability function and teaching-learning-based optimization algorithm have been used for determining optimal cutting process parameters. The experiments have been planned and conducted using rotatable central composite design under dry condition. Mathematical model for surface roughness has been developed in terms of cutting speed, feed per tooth, axial depth of cut and radial depth of cut as the cutting process parameters. Analysis of variance has been performed for analysing the effect of cutting parameters on surface roughness. A second-order full quadratic model is used for mathematical modelling. The analysis of the results shows that the developed model is adequate enough and good to be accepted. Analysis of variance for the individual terms revealed that surface roughness is mostly affected by the cutting speed with a percentage contribution of 47.18% followed by axial depth of cut by 10.83%. The optimum values of cutting process parameters obtained through teaching-learning-based optimization are feed per tooth ( fz) = 0.06 mm, axial depth of cut ( Ap) = 0.74 mm, cutting speed ( Vc) = 145.8 m/min, and radial depth of cut ( Ae) = 0.38 mm. The optimum value of surface roughness at the optimum parametric setting is 1.11 µm and has been validated by confirmation experiments.

A Comprehensive Study on Surface Roughness in Machining of AISI D2 Hardened Steel

2012 ◽  
Vol 576 ◽  
pp. 60-63 ◽  
Author(s):  
N.A.H. Jasni ◽  
Mohd Amri Lajis
Keyword(s):  
Surface Roughness ◽  
High Speed ◽  
End Milling ◽  
Cutting Speed ◽  
Hardened Steel ◽  
Depth Of Cut ◽  
Feed Speed ◽  
Radial Depth ◽  
Aisi D2 ◽  
Coated Carbide

Hard milling of hardened steel has wide application in mould and die industries. However, milling induced surface finish has received little attention. An experimental investigation is conducted to comprehensively characterize the surface roughness of AISI D2 hardened steel (58-62 HRC) in end milling operation using TiAlN/AlCrN multilayer coated carbide. Surface roughness (Ra) was examined at different cutting speed (v) and radial depth of cut (dr) while the measurement was taken in feed speed, Vf and cutting speed, Vc directions. The experimental results show that the milled surface is anisotropic in nature. Surface roughness values in feed speed direction do not appear to correspond to any definite pattern in relation to cutting speed, while it increases with radial depth-of-cut within the range 0.13-0.24 µm. In cutting speed direction, surface roughness value decreases in the high speed range, while it increases in the high radial depth of cut. Radial depth of cut is the most influencing parameter in surface roughness followed by cutting speed.

Cutting Forces and Surface Roughness in High-Speed End Milling of Ti6Al4V

2013 ◽  
Vol 589-590 ◽  
pp. 76-81
Author(s):  
Fu Zeng Wang ◽  
Jun Zhao ◽  
An Hai Li ◽  
Jia Bang Zhao
Keyword(s):  
Surface Roughness ◽  
Surface Quality ◽  
Cutting Forces ◽  
High Speed ◽  
End Milling ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Wide Range ◽  
Radial Depth ◽  
Coated Carbide

In this paper, high speed milling experiments on Ti6Al4V were conducted with coated carbide inserts under a wide range of cutting conditions. The effects of cutting speed, feed rate and radial depth of cut on the cutting forces, chip morphologies as well as surface roughness were investigated. The results indicated that the cutting speed 200m/min could be considered as a critical value at which both relatively low cutting forces and good surface quality can be obtained at the same time. When the cutting speed exceeds 200m/min, the cutting forces increase rapidly and the surface quality degrades. There exist obvious correlations between cutting forces and surface roughness.

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.

Surface Integrity of End Milled Ti-6Al-4V Using the TiAlN Coated Tool

2008 ◽  
Author(s):  
Y. B. Guo ◽  
Jie Sun
Keyword(s):  
Surface Roughness ◽  
Surface Integrity ◽  
End Milling ◽  
Bulk Material ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Near Surface ◽  
Β Phase ◽  
Radial Depth ◽  
Milled Surface

End milling titanium Ti-6Al-4V has wide applications in aerospace, biomedical, and chemical industries. However, milling induced surface integrity has received little attention. In this study, a series of end milling experiment were conducted to comprehensively characterize surface integrity at various milling conditions. The experimental results have shown that the milled surface shows the anisotropic nature with a surface roughness range in 0.6 μm–1.2 μm. Surface roughness increases with feed and radial depth-of-cut (DoC), but varies with the cutting speed range. Compressive residual normal stress occurs in both cutting and feed directions, while the influences of cutting speed and feed on residual stress trend are quit different. The microstructure analysis shows that β phase becomes much smaller and severely deformed in the very near surface with the cutting speed. The milled surfaces are at least 60% harder than the bulk material in the subsurface.

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.

Prediction of Surface Roughness Using Back-Propagation Neural Network in End Milling Ti-6Al-4V Alloy

2011 ◽  
Vol 325 ◽  
pp. 418-423 ◽  
Author(s):  
Song Zhang ◽  
Jian Feng Li
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
End Milling ◽  
Back Propagation ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Back Propagation Neural Network ◽  
Depth Of Cut ◽  
Radial Depth ◽  
Bpnn Model

Surface roughness plays a significant role in machining industry for proper planning of process system and optimizing the cutting conditions. In this paper, a back-propagation neural network (BPNN) model has been developed for the prediction of surface roughness in end milling process. A large number of milling experiments were conducted on Ti-6Al-4V alloy using the uncoated carbide tools. Four cutting parameters including cutting speed, feed per tooth, radial depth of cut, and axial depth of cut are used as the inputs to develop the BPNN model, while surface roughness corresponding to these combinations of different cutting parameters is the output of the neural network model. The performance of the trained BPNN model has been verified with the experimental results, and it is found that the BPNN predicted and the experimental values are very close to each other.

scholarly journals Optimal selection of parameters in CNC end milling of Al 7075 T6 alloy by TBLO

2019 ◽  
Vol 11 (4) ◽  
Author(s):  
Fauzia Siddiqui ◽  
Paramjit Thakur
Keyword(s):  
Regression Model ◽  
Process Parameters ◽  
End Milling ◽  
High Surface ◽  
Depth Of Cut ◽  
Optimum Process ◽  
Al 7075 ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Cnc End Milling

Al 7075 T6 is one of the highest strength aluminum alloys in 7000 series family which is used in highly stressed structural parts of aircrafts. The high surface roughness lowers the fatigue resistance and also affects the quality of the parts. Hence, this work deals with the application of teaching learning based optimization to minimize the roughness in the CNC end milling process. Here, taguchi L9 orthogonal array is used as experimental design. The depth of cut, feed and speed are used as control factors with three levels each and roughness as the response. The regression model was developed to find the effect of process parameters on response. The regression model was used by Teaching Learning Based Optimization (TLBO) algorithm and optimum process parameters were obtained. The optimal process parameters obtained by TLBO gave 60% reduction in roughness as compared to that given by initial setting of parameters used for machining of this material.

Optimal Cutting Parameters for Desired Surface Roughness in End Milling Inconel 718

2010 ◽  
Vol 126-128 ◽  
pp. 911-916 ◽  
Author(s):  
Yuan Wei Wang ◽  
Song Zhang ◽  
Jian Feng Li ◽  
Tong Chao Ding
Keyword(s):  
Surface Roughness ◽  
Inconel 718 ◽  
End Milling ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Radial Depth ◽  
Exponential Regression Model ◽  
Optimal Cutting Parameters ◽  
Coated Carbide

In this paper, Taguchi method was applied to design the cutting experiments when end milling Inconel 718 with the TiAlN-TiN coated carbide inserts. The signal-to-noise (S/N) ratio are employed to study the effects of cutting parameters (cutting speed, feed per tooth, radial depth of cut, and axial depth of cut) on surface roughness, and the optimal combination of the cutting parameters for the desired surface roughness is obtained. An exponential regression model for the surface roughness is formulated based on the experimental results. Finally, the verification tests show that surface roughness generated by the optimal cutting parameters is really the minimum value, and there is a good agreement between the predictive results and experimental measurements.

Effect Analysis and ANN Prediction of Surface Roughness in End Milling AISI H13 Steel

2014 ◽  
Vol 800-801 ◽  
pp. 590-595
Author(s):  
Qing Zhang ◽  
Song Zhang ◽  
Jia Man ◽  
Bin Zhao
Keyword(s):  
Surface Roughness ◽  
End Milling ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Experimental Results ◽  
Depth Of Cut ◽  
H13 Steel ◽  
Aisi H13 Steel ◽  
Radial Depth ◽  
Aisi H13

Surface roughness has a significant effect on the performance of machined components. In the present study, a total of 49 end milling experiments on AISI H13 steel are conducted. Based on the experimental results, the signal-to-noise (S/N) ratio is employed to study the effects of cutting parameters (axial depth of cut, cutting speed, feed per tooth and radial depth of cut) on surface roughness. An ANN predicting model for surface roughness versus cutting parameters is developed based on the experimental results. The testing results show that the proposed model can be used as a satisfactory prediction for surface roughness.

scholarly journals Surface Roughness Modeling with Edge Radius and End Milling Parameters on Al 7075 Alloy Using Taguchi and Regression Methods

ACTA IMEKO ◽  
2014 ◽  
Vol 3 (4) ◽  
pp. 46 ◽  
Author(s):  
Muhammet Numan Durakbasa ◽  
Anil Akdogan ◽  
Ali Serdar Vanli ◽  
Asli Günay
Keyword(s):  
Surface Roughness ◽  
Process Parameters ◽  
End Milling ◽  
Cutting Speed ◽  
Tool Geometry ◽  
Depth Of Cut ◽  
Regression Methods ◽  
Edge Radius ◽  
Al 7075 ◽  
7075 Alloy

Tool geometry and edge radius are not only crucial for workpiece surface characteristics determination but they also have direct impacts on tool lifetime. They are created in manufacturing and deformations occur in machining processes. With this regard, precise determination of the geometry is of vital importance. This study focused on process parameters like cutting speed, feed rate, depth of cut, tool geometry, and different coatings, to indicate the effects on surface roughness of the machined product on the basis of two and three-dimensional precise measurements. This paper studies the optimization of process parameters and different coating materials with combined different tool radii to obtain the maximum surface quality for the end milling process of Al 7075 alloy by Taguchi and Regression methods. The results revealed optimum process parameters with the proper coating type. The calculated mathematical model predicts average surface roughness value against edge radius wear and processing parameters.

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