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.

Optimization of Process Parameters in Plasma Arc Cutting Applying Genetic Algorithm and Fuzzy Logic

2018 ◽  
pp. 123-139
Author(s):  
Nehal Dash ◽  
Apurba Kumar Roy ◽  
Sanghamitra Debta ◽  
Kaushik Kumar
Keyword(s):  
Genetic Algorithm ◽  
Surface Roughness ◽  
Fuzzy Logic ◽  
Process Parameters ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Plasma Arc ◽  
Plasma Arc Cutting ◽  
Optimization Of Process Parameters

Plasma Arc Cutting (PAC) process is a widely used machining process in several fabrication, construction and repair work applications. Considering gas pressure, arc current and torch height as the inputs and among all possible outputs, in the present work Material Removal Rate and Surface Roughness would be considered as factors that determines the quality, machining time and machining cost. In order to reduce the number of experiments Design of Experiments (DOE) would be carried out. In later stages applications of Genetic Algorithm (GA) and Fuzzy Logic would be used for Optimization of process parameters in Plasma Arc Cutting (PAC). The output obtained would be minimized and maximized for Surface Roughness and Material Removal Rate respectively using Genetic Algorithm (GA) and Fuzzy Logic.

scholarly journals Elastic back-scattering patterns via particle surface roughness and orientation from single trapped airborne aerosol particles

2017 ◽  
Vol 187 ◽  
pp. 224-231 ◽  
Author(s):  
Richard Fu ◽  
Chuji Wang ◽  
Olga Muñoz ◽  
Gorden Videen ◽  
Joshua L. Santarpia ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Aerosol Particles ◽  
Particle Surface ◽  
Back Scattering

Nondestructive Characterization of GaN Films Grown at Low and High Temperatures

1999 ◽  
Vol 591 ◽  
Author(s):  
C.H. Yana ◽  
H.W. Yao ◽  
J.M. Van Hove ◽  
A.M. Wowchak ◽  
P.P. Chow ◽  
...  
Keyword(s):  
Surface Roughness ◽  
High Temperatures ◽  
Sapphire Substrates ◽  
Wurtzite Crystal Structure ◽  
Phonon Spectra ◽  
Scattering Geometry ◽  
Back Scattering ◽  
Nondestructive Characterization

ABSTRACTGaN films grown on GaAs and sapphire substrates by molecular beam epitaxy (MBE) and metalorganic vapor phase epitaxy (MOVPE) at both low and high temperatures (LT and HT) were characterized by Raman scattering and variable angle spectroscopic ellipsometry (VASE). Optical phonon spectra of GaN films are obtained through back-scattering geometry. Crystal quality of these films was qualitatively examined using phonon line-width. Phonon spectra showed that the HT GaN has wurtzite crystal structure, while LT GaN and GaN/GaAs have cubic-like structures. Thickness nonuniformity and defect-related absorption can be characterized by pseudo dielectric functions directly. Surface roughness also can be determined by using an effective-medium approximation (EMA) over-layer in a VASE analysis. Anisotropic optical constants of GaN, both ordinary and extraordinary, were obtained in the spectral range of 0.75 to 6.5 eV with the consideration of surface roughness, through the small and large angles of incidence, respectively. The film thickness of the GaN was accurately determined via the analysis as well.

scholarly journals Surface Roughness Optimization of Polyamide-6/Nanoclay Nanocomposites Using Artificial Neural Network: Genetic Algorithm Approach

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Mehdi Moghri ◽  
Milos Madic ◽  
Mostafa Omidi ◽  
Masoud Farahnakian
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Predictive Model ◽  
Nonlinear Function ◽  
Polyamide 6 ◽  
Machining Performance ◽  
Milling Parameters ◽  
Artificial Neural

During the past decade, polymer nanocomposites attracted considerable investment in research and development worldwide. One of the key factors that affect the quality of polymer nanocomposite products in machining is surface roughness. To obtain high quality products and reduce machining costs it is very important to determine the optimal machining conditions so as to achieve enhanced machining performance. The objective of this paper is to develop a predictive model using a combined design of experiments and artificial intelligence approach for optimization of surface roughness in milling of polyamide-6 (PA-6) nanocomposites. A surface roughness predictive model was developed in terms of milling parameters (spindle speed and feed rate) and nanoclay (NC) content using artificial neural network (ANN). As the present study deals with relatively small number of data obtained from full factorial design, application of genetic algorithm (GA) for ANN training is thought to be an appropriate approach for the purpose of developing accurate and robust ANN model. In the optimization phase, a GA is considered in conjunction with the explicit nonlinear function derived from the ANN to determine the optimal milling parameters for minimization of surface roughness for each PA-6 nanocomposite.

Research on Surface Roughness Prediction Model Based on Genetic Algorithm for Optical Ultra-Precision Machining

2007 ◽  
Vol 10-12 ◽  
pp. 369-373
Author(s):  
Jian Jun Du ◽  
Chi Fai Cheung ◽  
Suet To ◽  
Z.Y. Liu
Keyword(s):  
Numerical Simulation ◽  
Genetic Algorithm ◽  
Surface Roughness ◽  
Linear Regression ◽  
Precision Machining ◽  
Mathematics Model ◽  
Simulation Test ◽  
New Model ◽  
Roughness Prediction ◽  
Ultra Precision

In this paper a dynamic non-linear mathematics model is proposed to predict the surface roughness in optical ultra-precision machining, which can be automatically built by evoling computer program of genetic algorithm. The new model can improve the fitting and predicting accuracy, compared with the traditional linear regression model. The numerical simulation test proves the effectiveness and accuracy of new model.

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