Use of R.S.M. for Design of Experiments and Optimization of Parameters in Milling Operations
Abstract
domain. If these experiments are planned experiments then,
the data can be converted into the empirical models, which
can be used for finding out the performance of the system
under different situations. It helps in planning and execution
of activities with optimized use of available resources. The
design of experiments is usually carried out by using methods
like full factorial design, fractional factorial design, Taguchi
design and Response surface design. In this paper, we have
reviewed the recent literature for the use of response surface
methodology (RSM) for milling operations. The aim of this
work was to study and present the recent literature in this
field. Initially, the use of RSM for modeling and analysis of
surface roughness is presented, followed by cutting
temperature, cutting force and other parameters. RSM is
found to be a very useful and powerful tool for design of
experiments and optimization of process parameters in milling
operations. It was used by many researchers for modeling and
analysis of surface roughness vibration amplitude, tool wear,
cutting temperatures, cutting forces and burr dimensions.
Hybrid analysis combining RSM with evolutionary algorithms
like genetic algorithms and simulated annealing was also
successfully employed.
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D. J. Peck H. S. Zadeh, J. P. Windham and A. E. Yagle,
"A comparative analysis of several transformations for
enhancement and segmentation of magnetic resonance
image scene sequences," IEEE Trans. Med.Imaging, vol.
, no. 3, pp. 302–318, September 1992.
P. G. Benardos and G. Vosniakos, "Predicting surface
roughness in machining: a review," vol. 43, pp. 833–
, 2003.
G. Mahesh, S. Muthu, and S. R. Devadasan, "Prediction
of surface roughness of end milling operation using
genetic algorithm," Int. J. Adv. Manuf. Technol., vol. 77,
no. 1–4, pp. 369–381, 2014.
M. S. M. S. R. Sudhakaran, "Modeling and Analysis of
Surface Roughness of AL7075-T6 in End Milling
Process Using Response Surface Methodology," pp.
–7313, 2014.
R. Kamguem, A. Djebara, and V. Songmene,
"Investigation on surface finish and metallic particle
emission during machining of aluminum alloys using
response surface methodology and desirability
functions," pp. 1283–1298, 2013.
A. K. Sehgal, "SURFACE ROUGHNESS
OPTIMIZATION BY RESPOSE SURFACE
METHODOLOGY AND PARTICLE SWARM
OPTIMIZATION," vol. 5, no. 07, pp. 1382–1393, 2013.
S. Jeyakumar, K. Marimuthu, and T. Ramachandran,
"Prediction of cutting force , tool wear and surface
roughness of Al6061 / SiC composite for end milling
operations using RSM †," vol. 27, no. 9, pp. 2813–2822,
B. C. Routara, A. Bandyopadhyay, and P. Sahoo,
"Roughness modeling and optimization in CNC end
milling using response surface method: effect of
workpiece material variation," pp. 1166–1180, 2009.
T. Erzurumlu, "Materials & Design Comparison of
response surface model with neural network in
determining the surface quality of moulded parts," vol.
, pp. 459–465, 2007.
N. S. Kumar and R. P. Venkateswara, "Selection of
optimum tool geometry and cutting conditions using a
surface roughness prediction model for end milling," pp.
–1210, 2005.
N. S. K. Reddy and P. V. Rao, "A GENETIC
ALGORITHMIC APPROACH FOR OPTIMIZATION
OF SURFACE ROUGHNESS PREDICTION MODEL
IN DRY MILLING," vol. 0344, no. March, 2016.
H. Öktem, T. Erzurumlu, and H. Kurtaran, "Application
of response surface methodology in the optimization of
cutting conditions for surface roughness," J. Mater.
Process. Technol., vol. 170, no. 1–2, pp. 11–16, 2005.
M.-Y. Wang and H.-Y. Chang, "Experimental study of
surface roughness in slot end milling AL2014-T6," Int. J.
Mach. Tools Manuf., vol. 44, no. 1, pp. 51–57, 2004.
A. Mansour and H. Abdalla, "Surface roughness model
for end milling: a semi-free cutting carbon
casehardening steel ( EN32 ) in dry condition," vol. 124,
M. S. P. S. S. R. Sudhakaran, "Modeling of geometrical
and machining parameters on temperature rise while
machining Al 6351 using response surface methodology
and genetic algorithm," J. Brazilian Soc. Mech. Sci.
Eng., 2015.
P. S. Sivasakthivel and R. Sudhakaran, "Optimization of
machining parameters on temperature rise in end milling
of Al 6063 using response surface methodology and
genetic algorithm," Int. J. Adv. Manuf. Technol., vol. 67,
no. 9–12, pp. 2313–2323, 2013.
T. Sri and C. Saraswathi, "Multi-objective Optimization
of Hard Milling Process using Evolutionary Computation
Techniques Multi-objective Optimization of Hard
Milling Process using Evolutionary Computation
Techniques," no. November, 2015.
B. Patel, H. Nayak, K. Araniya, and G. Champaneri,
"Parametric Optimization of Temperature During CNC
End Milling of Mild Steel Using RSM," vol. 3, no. 1, pp.
–73, 2014.
K. Kadirgama and M. M. Noor, "Finite Element Analysis
and Statistical Method to Determine Temperature
Distribution on Cutting Tool in End-Milling," Eur. J. Sci.
Res., vol. 30, no. 3, pp. 451–463, 2009.
A. Tamilarasan and K. Marimuthu, "Multi-response
optimization of hard milling process: RSM coupled
with grey relational analysis," vol. 5, no. 6, pp. 4903–
, 2014.
M. Kumar, A. Baran, and A. Maity, "Experimental Study
of Cutting Forces in Ball End Milling of Al2014-T6
Using Response Surface Methodology," Procedia Mater.
Sci., vol. 6, no. Icmpc, pp. 612–622, 2014.
M. Subramanian, M. Sakthivel, K. Sooryaprakash, and
R. Sudhakaran, "Optimization of end mill tool geometry
parameters for Al7075-T6 machining operations based
on vibration amplitude by response surface
methodology," Measurement, vol. 46, no. 10, pp. 4005–
, 2013.
P. S. Sivasakthivel, V. Velmurugan, and R. Sudhakaran,
"Prediction of vibration amplitude from machining
parameters by response surface methodology in end
milling," pp. 453–461, 2011.
K. Fuh and H. Chang, "An accuracy model for the
peripheral milling of aluminum alloys using response
surface design," vol. 72, pp. 42–47, 1997.
S. Parsa and K. Mehrzad, "Optimization of Micromilling
Parameters Regarding Burr Size Minimization
via RSM and Simulated Annealing Algorithm," Trans.
Indian Inst. Met., vol. 68, pp. 897–910, 2015.
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