Modeling of geometrical and machining parameters on temperature rise while machining Al 6351 using response surface methodology and genetic algorithm

2015 ◽  
Vol 39 (2) ◽  
pp. 487-496 ◽  
Author(s):  
M. Santhanakrishnan ◽  
P. S. Sivasakthivel ◽  
R. Sudhakaran
Keyword(s):  
Genetic Algorithm ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Temperature Rise ◽  
Machining Parameters

Study on Wire Electric Discharge Machining Based on Response Surface Methodology and Genetic Algorithm

2012 ◽  
Vol 622-623 ◽  
pp. 1280-1284 ◽  
Author(s):  
Pragya Shandilya ◽  
P.K. Jain ◽  
N.K. Jain
Keyword(s):  
Genetic Algorithm ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Process Parameters ◽  
Electric Discharge ◽  
Metal Matrix ◽  
Machining Parameters ◽  
Electric Discharge Machining ◽  
Response Parameter ◽  
Wire Electric Discharge Machining

Wire electric discharge machining (WEDM) is one of the most popular non-conventional machining processes for machining metal matrix composites (MMCs). The present research work deals the parametric optimization of the input process parameters for response parameter during WEDM of SiCp/6061 Al metal matrix composite (MMC). Response surface methodology (RSM) and genetic algorithm (GA) integrated with each other to optimize the process parameters. RSM has been used to plan and analyze the experiments. Four WEDM parameters namely servo voltage, pulse-on time, pulse-off time and wire feed rate were varied to study their effect on the quality of cut in SiCp/6061 Al MMC using cutting width (kerf) as response parameter. The relationship between kerf and machining parameters has been developed by using RSM. The mathematical model thus than developed was then employed on GA to optimized the process parameters.

Investigation of Machining Parameters in CNC Turning of EN3 Low Carbon Steel Using Genetic Algorithm and Response Surface Methodology

2014 ◽  
Vol 592-594 ◽  
pp. 883-887
Author(s):  
R. Rave Kumar ◽  
M. Mohamed Abdul Hafeez ◽  
K. Velmanirajan ◽  
K. Nantha Kumar
Keyword(s):  
Genetic Algorithm ◽  
Surface Roughness ◽  
Response Surface Methodology ◽  
Carbon Steel ◽  
Response Surface ◽  
Low Carbon Steel ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Low Carbon

Burrs are bottleneck of precision machining and automation production. Burrs are formed in every edges and faces, during the turning process, which affects the quality level of surface roughness. In this paper the experimental study of EN3 low carbon steel were carried out to minimize the surface roughness using response surface methodology and genetic algorithm. Tungsten Carbide was used as a cutting tool for this turning operation. Machined samples are examined under Scanning Electron Microscope (SEM) for burr formation. A wide variety of analysis between cutting parameters have been shown graphically. The minimization of burr was achieved and hence better surface quality was obtained by optimizing the cutting parameters like cutting speed, feed, and depth of cut, with the aid of Genetic Algorithm (GA) & Response Surface Methodology (RSM) Techniques.

Quantification of tool chatter and metal removal rate using wavelet denoising and statistical approach

2018 ◽  
Vol 49 (2) ◽  
pp. 62-81 ◽  
Author(s):  
Shailendra Kumar ◽  
Bhagat Singh
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Metal Removal ◽  
Removal Rate ◽  
Statistical Significance ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Metal Removal Rate ◽  
Chatter Index ◽  
Tool Chatter

Tool chatter is an unavoidable phenomenon encountered in machining processes. Acquired raw chatter signals are contaminated with various types of ambient noises. Signal processing is an efficient technique to explore chatter as it eliminates unwanted background noise present in the raw signal. In this study, experimentally recorded raw chatter signals have been denoised using wavelet transform in order to eliminate the unwanted noise inclusions. Moreover, effect of machining parameters such as depth of cut ( d), feed rate ( f) and spindle speed ( N) on chatter severity and metal removal rate has been ascertained experimentally. Furthermore, in order to quantify the chatter severity, a new parameter called chatter index has been evaluated considering aforesaid denoised signals. A set of 15 experimental runs have been performed using Box–Behnken design of experiment. These experimental observations have been used to develop mathematical models for chatter index and metal removal rate considering response surface methodology. In order to check the statistical significance of control parameters, analysis of variance has been performed. Furthermore, more experiments are conducted and these results are compared with the theoretical ones in order to validate the developed response surface methodology model.

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