Improvement of Surface Roughness in End Milling of Ti6Al4V by Coupling RSM with Genetic Algorithm

2011 ◽  
Vol 264-265 ◽  
pp. 1154-1159
Author(s):  
Anayet Ullah Patwari ◽  
A.K.M. Nurul Amin ◽  
S. Alam
Keyword(s):  
Genetic Algorithm ◽  
Surface Roughness ◽  
Titanium Alloys ◽  
End Milling ◽  
Fitness Function ◽  
Cutting Speed ◽  
Weight Ratio ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Resistance Surface

Titanium alloys are being widely used in the aerospace, biomedical and automotive industries because of their good strength-to-weight ratio and superior corrosion resistance. Surface roughness is one of the most important requirements in machining of Titanium alloys. This paper describes mathematically the effect of cutting parameters on Surface roughness in end milling of Ti6Al4V. The mathematical model for the surface roughness has been developed in terms of cutting speed, feed rate, and axial depth of cut using design of experiments and the response surface methodology (RSM). Central composite design was employed in developing the surface roughness models in relation to primary cutting parameters. The experimental results indicate that the proposed mathematical models suggested could adequately describe the performance indicators within the limits of the factors that are being investigated. The developed RSM is coupled as a fitness function with genetic algorithm to predict the optimum cutting conditions leading to the least surface roughness value. MATLAB 7.0 toolbox for GA is used to develop GA program. The predicted results are in good agreement with the experimental one and hence the model can be efficiently used to achieve the minimum surface roughness value.

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.

The Investigation of Prediction Surface Roughness from Machining Forces in End Milling Processes

2011 ◽  
Vol 486 ◽  
pp. 91-94 ◽  
Author(s):  
Jabbar Abbas ◽  
Amin Al-Habaibeh ◽  
Dai Zhong Su
Keyword(s):  
Surface Roughness ◽  
End Milling ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Irregular Waves ◽  
Depth Of Cut ◽  
End Mill ◽  
Machined Surface ◽  
Machining Forces ◽  
Mill Cutter

Surface roughness is one of the most significant parameters to determine quality of machined parts. Surface roughness is defined as a group of irregular waves in the surface, measured in micrometers (μm). Many investigations have been performed to verify the relationship between surface roughness and cutting parameters such as cutting speed, feed rate and depth of cut. To predict the surface produced by end milling, surface roughness models have been developed in this paper using the machining forces by assuming the end mill cutter as a cantilever beam rigidly or semi- rigidly supported by tool holder. An Aluminium workpiece and solid carbide end mill tools are used in this work. Model to predict surface roughness has been developed. Close relationship between machined surface roughness and roughness predicted using the measured forces signals.

Surface Roughness Optimization in End Milling Using the Multi Objective Genetic Algorithm Approach

2012 ◽  
Vol 576 ◽  
pp. 103-106 ◽  
Author(s):  
Muataz H.F. Al Hazza ◽  
Erry Yulian Triblas Adesta ◽  
Muhammad Riza ◽  
M.Y. Suprianto
Keyword(s):  
Genetic Algorithm ◽  
Surface Roughness ◽  
End Milling ◽  
Cutting Speed ◽  
Root Mean Square Roughness ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Machining Optimization

In finishing end milling, not only good accuracy but also good roughness levels must be achieved. Therefore, determining the optimum cutting levels to achieve the minimum surface roughness is important for it is economical and mechanical issues. This paper presents the optimization of machining parameters in end milling processes by integrating the genetic algorithm (GA) with the statistical approach. Two objectives have been considered, minimum arithmetic mean roughness (Ra) and minimum Root-mean-square roughness (Rq). The mathematical models for the surface roughness parameters have been developed, in terms of cutting speed, feed rate, and axial depth of cut by using Response Methodology Method (RSM). Due to complexity of this machining optimization problem, a multi objective genetic algorithm (MOGA) has been applied to resolve the problem, and the results have been analyzed.

Prediction of Surface Roughness in High Speed Machining of Inconel 718

2011 ◽  
Vol 264-265 ◽  
pp. 1193-1198
Author(s):  
Mokhtar Suhaily ◽  
A.K.M. Nurul Amin ◽  
Anayet Ullah Patwari
Keyword(s):  
Surface Roughness ◽  
Surface Finish ◽  
Inconel 718 ◽  
High Speed ◽  
End Milling ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Machining Process ◽  
Depth Of Cut ◽  
High Speed Machining

Surface finish and dimensional accuracy is one of the most important requirements in machining process. Inconel 718 has been widely used in the aerospace industries. High speed machining (HSM) is capable of producing parts that require little or no grinding/lapping operations within the required machining tolerances. In this study small diameter tools are used to achieve high rpm to facilitate the application of low values of feed and depths of cut to investigate better surface finish in high speed machining of Inconel 718. This paper describes mathematically the effect of cutting parameters on Surface roughness in high speed end milling of Inconel 718. The mathematical model for the surface roughness has been developed in terms of cutting speed, feed rate, and axial depth of cut using design of experiments and the response surface methodology (RSM). Central composite design was employed in developing the surface roughness models in relation to primary cutting parameters. Machining were performed using CNC Vertical Machining Center (VMC) with a HES510 high speed machining attachment in which using a 4mm solid carbide fluted flat end mill tool. Wyko NT1100 optical profiler was used to measure the definite machined surface for obtaining the surface roughness data. The predicted results are in good agreement with the experimental one and hence the model can be efficiently used to predict the surface roughness value with in the specified cutting conditions limit.

Optimization of Surface Roughness in End Milling of Titanium Alloy Ti-6Al-4V under the Influence of Magnetic Field from Permanents Magnets

2012 ◽  
Vol 576 ◽  
pp. 51-55 ◽  
Author(s):  
Syidatul Akma Sulaiman ◽  
A.K.M. Nurul Amin ◽  
M.D. Arif
Keyword(s):  
Magnetic Field ◽  
Surface Roughness ◽  
Titanium Alloy ◽  
Permanent Magnets ◽  
End Milling ◽  
Desirability Function ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Central Composite

This paper presents the effect of cutting parameters on surface roughness in end milling of Titanium alloy Ti-6Al-4V under the influence of magnetic field from permanent magnets. Response Surface Methodology (RSM) with a small central composite design was used in developing the relationship between cutting speed, feed, and depth of cut, with surface roughness. In this experiment, three factors and five levels of central composite with 0.16817 alpha value was used as an approach to predict the surface roughness, in end milling of titanium alloy, with reasonable accuracy. The Design-Expert 6.0 software was applied to develop the surface roughness equation for the predictive model. The adequacy of the surface roughness model was validated to 95% by using ANOVA analysis. Finally, desirability function approach was used to determine the optimum possible surface roughness given the capabilities of the end machine.

Surface Roughness Model when Machining Aluminum Nitride Ceramic with Two Flute Square End Micro Grain Solid Carbide End Mill

2013 ◽  
Vol 747 ◽  
pp. 282-286 ◽  
Author(s):  
Moola Mohan Reddy ◽  
Alexander Gorin ◽  
K.A. Abou-El-Hossein ◽  
D. Sujan
Keyword(s):  
Surface Roughness ◽  
Aluminum Nitride ◽  
Feed Rate ◽  
End Milling ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
End Mill ◽  
Roughness Model ◽  
Solid Carbide

This research presents the performance of Aluminum Nitride ceramic in end milling using two flute square end micro grain solid carbide end mill under dry cutting. Surface finish is one of the important requirements in the machining process. This paper describes mathematically the effect of cutting parameters on surface roughness in end milling process. The quadratic model for the surface roughness has been developed in terms of cutting speed, feed rate, and axial depth of cut using the response surface methodology (RSM). Design of experiments approach was employed in developing the surface roughness model in relation to cutting parameters. The predicted results are in good agreement with the experimental results within the specified range of cutting conditions. Experimental results showed surface roughness increases with increase in the cutting speed, feed rate, and the axial depth of cut.

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.

Fuzzy Modeling of Surface Roughness Parameters in Machining Ti-6Al-4V Alloy

2015 ◽  
Vol 766-767 ◽  
pp. 681-686 ◽  
Author(s):  
J. Nithyanandam ◽  
K. Palanikumar ◽  
Sushil Laldas
Keyword(s):  
Surface Roughness ◽  
Titanium Alloys ◽  
Chemical Reactivity ◽  
Cutting Temperature ◽  
Cutting Speed ◽  
Weight Ratio ◽  
Fuzzy Modeling ◽  
Depth Of Cut ◽  
Work Piece ◽  
Nose Radius

Titanium alloys are attractive materials used in different engineering applications, due to its excellent combination of properties such as high strength to weight ratio, good corrosion resistance and high temperature applicability. They are also being used increasingly in chemical process, automotive, biomedical and nuclear plant. When machining of Titanium alloys with traditional tools the tool wear rate high. Because of high chemical reactivity and low modulus of elasticity resulting high cutting temperature and strong adhesion between the tool and work piece materials. The poly crystalline diamond (PCD) cutting tool is used for the turning experiment. The turning parameters for the experimental work are cutting speed, feed, nose radius, and depth of cut. From the results, analysis of the influences of the individual parameters on the surface roughness have been carried out. Fuzzy modeling technique is effectively used to predict the surface roughness in the machining of titanium alloy.

Surface Roughness Optimization in Machining of Titanium Alloy (Ti-6Al-4V)

2014 ◽  
Vol 984-985 ◽  
pp. 42-47
Author(s):  
J. Nithyanandam ◽  
Sushil Laldas ◽  
K. Palanikumar
Keyword(s):  
Surface Roughness ◽  
Titanium Alloy ◽  
Thermal Properties ◽  
Cutting Speed ◽  
Weight Ratio ◽  
High Strength ◽  
Cutting Parameters ◽  
Ratio Method ◽  
Depth Of Cut ◽  
Nose Radius

Titanium is one of the important kinds of material used in different engineering fields. They have very good properties like high strength to weight ratio, superior corrosion resistance and thermal properties. They are very attractive materials and has application aerospace, biomedical and automotive field. they are classified to be “difficult-to-Machine materials” as they posses poor thermal properties, poor machinability, etc.The prime important is with the study of machining characteristics and the optimization of the cutting parameters. In this paper Titanium alloy (Ti-6Al-4V) is taken, the dry turning experiments are carried out in semi-automatic lathe using poly crystalline diamond (PCD) cutting tool insert. The taguchi’s design of L27orthogonal array is done by four machining factors namely cutting speed, feed, nose radius and depth of cut at three levels. The optimal machining conditions are arrived by Signal-Noise ratio method with respect to surface roughness (Ra). The analysis of variance (ANOVA) and the percentage of contribution of feed, cutting speed, nose radius and depth of cut for better surface roughness is validated using S/N ratio. In this result indicated that the feed is a vital parameter followed by cutting speed, the nose radius and then by depth of cut. The worn out surface of the insert is examined by using scanning electron microscope (SEM).

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