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.

Power Consumption Optimization in CNC Turning Process Using Multi Objective Genetic Algorithm

2012 ◽  
Vol 576 ◽  
pp. 95-98 ◽  
Author(s):  
Muataz H.F. Al Hazza ◽  
Erry Yulian Triblas Adesta ◽  
Muhammad Riza ◽  
M.Y. Suprianto
Keyword(s):  
Genetic Algorithm ◽  
Power Consumption ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Consumption Cost ◽  
Cnc Turning Process ◽  
Machining Optimization

Power consumption cost is one of the main integral parts of the total machining cost, but it has not given the proper attention when minimizing the machining cost. In this paper, the optimal machining parameters for continuous machining are determined with respect to the minimum power consumption cost with maintaining the surface roughness in the range of acceptance. The constraints considered in this research are cutting speed, feed rate, depth of cut and rake angle. Due to complexity of this machining optimization problem, a multi objective genetic algorithm (MOGA) was applied to resolve the problem, and the results have been analyzed.

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.

scholarly journals Optimization of machining parameters in face milling using multi-objective Taguchi technique

2018 ◽  
Vol 12 (2) ◽  
pp. 104-108 ◽  
Author(s):  
Yusuf Fedai ◽  
Hediye Kirli Akin
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Taguchi Technique ◽  
Average Roughness ◽  
Multi Objective ◽  
Control Factors ◽  
Average Maximum

In this research, the effect of machining parameters on the various surface roughness characteristics (arithmetic average roughness (Ra), root mean square average roughness (Rq) and average maximum height of the profile (Rz)) in the milling of AISI 4140 steel were experimentally investigated. Depth of cut, feed rate, cutting speed and the number of insert were considered as control factors; Ra, Rz and Rq were considered as response factors. Experiments were designed considering Taguchi L9 orthogonal array. Multi signal-to-noise ratio was calculated for the response variables simultaneously. Analysis of variance was conducted to detect the significance of control factors on responses. Moreover, the percent contributions of the control factors on the surface roughness were obtained to be the number of insert (71.89 %), feed (19.74 %), cutting speed (5.08%) and depth of cut (3.29 %). Minimum surface roughness values for Ra, Rz and Rq were obtained at 325 m/min cutting speed, 0.08 mm/rev feed rate, 1 number of insert and 1 mm depth of cut by using multi-objective Taguchi technique.

scholarly journals Multi-objective optimization and analysis for laser beam cutting of stainless steel (SS304) using hybrid statistical tools GA-RSM

2021 ◽  
Vol 1201 (1) ◽  
pp. 012030
Author(s):  
A D Tura ◽  
H B Mamo ◽  
D G Desisa
Keyword(s):  
Genetic Algorithm ◽  
Surface Roughness ◽  
Stainless Steel ◽  
Laser Beam ◽  
Response Surface ◽  
Focal Point ◽  
Cutting Speed ◽  
Machining Parameters ◽  
Multi Objective ◽  
The Impact

Abstract A laser beam machine is a non-traditional manufacturing technique that uses thermal energy to cut nearly all types of materials. The quality of laser cutting is significantly affected by process parameters. The purpose of this study is to use a genetic algorithm (GA) in conjunction with response surface approaches to improve surface roughness in laser beam cutting CO2 with a continuous wave of SS 304 stainless steel. The effects of the machining parameters, such as cutting speed, nitrogen gas pressure, and focal point location, were investigated quantitatively and optimized. The tests were carried out using the Taguchi L9 orthogonal mesh approach. Analysis of variance, main effect plots, and 3D surface plots were used to evaluate the impact of cutting settings on surface roughness. A multi-objective genetic algorithm in MATLAB was used to achieve a minimum surface roughness of 0.93746 μm, with the input parameters being 2028.712 mm/m cutting speed, 11.389 bar nitrogen pressure, and a focal point position of - 2.499 mm. The optimum results of each method were compared, as the results the response surface approach is less promising than the genetic algorithm method.

Surface Roughness Prediction in End Milling of Machinable Glass Ceramic and Optimization by Response Surface Methodology

2011 ◽  
Vol 189-193 ◽  
pp. 1376-1381
Author(s):  
Moola Mohan Reddy ◽  
Alexander Gorin ◽  
Khaled A. Abou El Hossein
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Glass Ceramic ◽  
End Milling ◽  
Removal Rate ◽  
Cutting Speed ◽  
Lower Surface ◽  
Depth Of Cut ◽  
Machining Parameters

This paper presents the prediction of a statistically analyzed model for the surface roughness,R_a of end-milled Machinable glass ceramic (MGC). Response Surface Methodology (RSM) is used to construct the models based on 3-factorial Box-Behnken Design (BBD). It is found that cutting speed is the most significant factor contributing to the surface roughness value followed by the depth of cut and feed rate. The surface roughness value decreases for higher cutting speed along with lower feed and depth of cut. Additionally, the process optimization has also been done in terms of material removal rate (MRR) to the model’s response. Ideal combinations of machining parameters are then suggested for common goal to achieve lower surface roughness value and higher MRR.

Effect of SiCp Reinforcement on Machinability of A356 Alloy Metal Matrix Composites

2016 ◽  
Vol 852 ◽  
pp. 142-148
Author(s):  
K. Jayakumar
Keyword(s):  
Surface Roughness ◽  
Metal Matrix Composites ◽  
Metal Matrix ◽  
End Milling ◽  
Cutting Speed ◽  
A356 Alloy ◽  
Experimental Result ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Matrix Composites

Machining of Aluminum Metal Matrix Composites (AMMCs) is a challenge for manufacturing industries due to their heterogeneous constituents which vary from soft matrix to hard reinforcements and their interfaces. To overcome the difficulties in machining of MMCs, researchers are continuously working to find the optimum process or machining parameters. In this work, End milling studies were carried out in A356 alloy powder-SiC particles (1 μm) in 0, 5, 10, 15 volume % reinforced AMMCs synthesised by vacuum hot pressing (VHP) route.The influence of machining parameters such as cutting speed, feed and depth of cut on the prepared composites in terms of surface roughness (Ra) and material removal rate (MRR) are measured from experimental study. Experiments were conducted as per Taguchi L16 orthogonal array with 4 factors and 4 levels.From the experimental result, it was identified that surface roughness varied from 0.214 μm to 4.115 μm and MRR varied from minimum of 1.11 cm3/min to maximum of 9.65 cm3/min. It is also observed that, MRR increased with increase in machining parameters and reinforcement quantity. Similarly, surface roughness decreased for increase of cutting speed, SiC particle (SiCp) reinforcement and increased for increase in feed and depth of cut. The optimum condition were observed in higher speed, lower feed and higher depth of cut on MMC with higher SiC content (15%) for getting higher machinability.

Processing and End Milling Behavioural Study of A356-SiCp Composite

2012 ◽  
Vol 710 ◽  
pp. 338-343 ◽  
Author(s):  
K. Jayakumar ◽  
Jose Mathew ◽  
M.A. Joseph ◽  
R. Suresh Kumar ◽  
P. Chakravarthy
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
End Milling ◽  
Cutting Speed ◽  
Machining Process ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Pressing Method ◽  
Reinforced Composite ◽  
Effect Of Particle Size

Machining process such as milling receives less attention in the study of machinability of composites due to its interrupted cutting and the complexity of the process. In the present study, A356 aluminium alloy powder reinforced with 10 volume % SiC particles of various sizes (1,12.5 and 25 µm) were synthesized by vacuum hot pressing method and the effect of particle size on the composites were analysed for its mechanical properties and machinability. End milling of these composites were carried out and the surface roughness and resultant cutting force were analysed with the change of machining parameters and varying SiC particle sizes. The minimum cutting force and surface roughness were obtained for a finer particle (1 µm) reinforced composite with higher cutting speed, low feed and depth of cut.

Multiobjective Optimization of End Milling HCHCr Using AlCrN PVD Coated Cutters With MOGA and RSMO

2014 ◽  
Author(s):  
Shantisagar K. Biradar ◽  
Geeta S. Lathkar
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Multiobjective Optimization ◽  
End Milling ◽  
Dominant Role ◽  
Optimization Technique ◽  
Cutting Speed ◽  
Optimization Techniques ◽  
Depth Of Cut ◽  
Machining Parameters

Here the End milling is studied for optimization of responses such as surface roughness and tool wear while machining HCHCr. These two conflicting responses decide the quality of process; therefore the multiobjective optimization technique is used. The Response Surface Optimizer (RSMO) and Multiobjective Genetic Algorithm (MOGA) were used as the multiobjective optimization techniques. The PVD coating of 2.5 micron AlCrN was used on four flute HSS End milling cutter. Input machining parameters were cutting speed, feed rate, depth of cut and percentage concentration of the solid lubricant MoS2 mixed with SAE-20 base oil. The experimentation was carried out using two level full factorial design concept while ANOVA technique has been used to verify the adequacy of mathematical model. It was found that the cutting speed (V) is having most dominant role on surface roughness and tool wear. The sensitivity analysis was carried out for studying sensitivity of input parameters for the responses.

Prediction and Investigation of Surface Response in High Speed End Milling of Ti-6Al-4V and Optimization by Genetic Algorithm

2009 ◽  
Vol 83-86 ◽  
pp. 1009-1015 ◽  
Author(s):  
S. Alam ◽  
A.K.M. Nurul Amin ◽  
Anayet Ullah Patwari ◽  
Mohamed Konneh
Keyword(s):  
Experimental Data ◽  
Genetic Algorithm ◽  
Surface Roughness ◽  
High Speed ◽  
End Milling ◽  
Cutting Speed ◽  
Cutting Conditions ◽  
Depth Of Cut ◽  
Flat End Milling ◽  
Five Axis

In this study, statistical models were developed using the capabilities of Response Surface Methodology (RSM) to predict the surface roughness in high-speed flat end milling of Ti-6Al-4V under dry cutting conditions. Machining was performed on a five-axis NC milling machine with a high speed attachment, using spindle speed, feed rate, and depth of cut as machining variables. The adequacy of the model was tested at 95% confidence interval. Meanwhile, a time trend was observed in residual values between model predictions and experimental data, reflecting little deviations in surface roughness prediction. A very good performance of the RSM model, in terms of agreement with experimental data, was achieved. It is observed that cutting speed has the most significant influence on surface roughness followed by feed and depth of cut. The model can be used for the analysis and prediction of the complex relationship between cutting conditions and the surface roughness in flat end milling of Ti-6Al-4V materials. The developed quadratic prediction model on surface roughness was coupled with the genetic algorithm to optimize the cutting parameters for the minimum surface roughness.

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