4. Conclusions
The conclusions made on the basis of results obtained
from parametric analysis of surface roughness studies in
turning using Artificial Neural Network have proved that
ANN is a powerful tool and used for accurate prediction
better than other techniques. Many of the researches have
used MATLAB software for prediction of ANN.
References
[1] A.V.N.L. Sharma, P. Satyanarayana Raju, A.
Gopichand, K. V. Subbaiah, Optimization of cutting
parameters on mild steel with HSS & cemented carbide
tipped tools using ANN, IJRET, 1(3), 2012
[2] Adnan jameel, Mohamah Minhat, Md. Nizam, Using
genetic algorithm to optimize machining parameters in
turning operation: A Review, International journal of
scientific and research publications, 3(5), 2013
[3] Anna Zawada- Tomkiewicz, Estimation of surface
roughness parameter based on machined surface
image, Metrology and measurement, 17(3), 2010, 493-
504
[4] B. Sidda Reddy, J. Suresh Kumar, K. Vijaya Kumar
Reddy, prediction of surface roughness in turning
using adaptive neuro-fuzzy inference system, jorden
journal of mechanical and industrial engineering, 3(4),
2009
[5] B. Y. Lee, Y. S. Tarng, H. R. Lii, An investigation of
modeling of the machining database in turning
operations, Journal of materials processing technology,
105, 2000, 1-6
[6] B. Y. Lee, Y.S. Tarng, Surface roughness inspection
by computer vision in turning operations, International
journal of machine tools & manufacture, 2001
[7] Dejan Tanikic, Miodrag Manic, Goran Radenkovic,
Dragan Mancic, Metal cutting process parameters
modeling: an artificial intelligence approach”, Journal
of scientific & industrial research, 68, 2009, 530-539
[8] V. Diwakar Reddy, G. Krishnaiah, A. Hemanth
Kumar, S. K. Priya, ANN based predication of surface
roughness in turning, International conference on
trends in mechanical and industrial engineering
(ICTMIE2011) Bangkok, 2011
[9] D. Karayel, Prediction and control of surface
roughness in CNC lathe using artificial neural network,
journal of materials processing technology, 209, 2009,
3125–3137
[10] Ilhan Asiltürk, Mehmet Çunkas, Selçuk, Modeling and
prediction of surface roughness in turning operations
using artificial neural network and multiple regression
method, Expert Systems with Applications, 38, 2011,
5826–5832
[11] J. Paulo Davim, V. N. Gaitonde, S. R. Karnik,
Investigations into the effect of cutting conditions on
surface roughness in turning of free machining steel
by ANN models, Journal of materials processing
technology, 2007
[12] P. G. Benardos, G.-C. Vosniakos, Optimizing feed
forward
artificial
neural
network
architecture”Engineering Applications of Artificial
Intelligence, 20, 2007, 365–382
[13] R. Karuppasamy, A. K. Shaik Dawood, G.
Karuppusami, Reduction the Surface Roughness of
Pneumatic Cylinder Rod in Turning Process Using
Genetic
Algrithm,
IRACST-Engineering
and
Technology: An international JOURNAL (ESTIJ),
2(4), 2012
[14] Ramon Quiza Sardinas, Marcelino Rivas Santana,
Eleno Alfonso Brindis, Genetic algorithm-based multi-
objective optimization of cutting parameters in turning
processes, Engineering Application of Artificial
Intelligence, 2005
[15] Ranganath M S, Vipin, R S Mishra, Application of
ANN for Prediction of Surface Roughness in Turning
Process: A Review, International Journal of Advance
Research and Innovation, 3, 2013, 229-233
[16] Ranganath M S, Vipin, R S Mishra, Neural Network
Process Modelling for Turning of Aluminium (6061)
using Cemented Carbide Inserts, International Journal
of Advance Research and Innovation, 3, 2013, 211-219
[17] Ruey-Jing Lian, Bai-Fu Lin, Jyun-Han Huang, A grey
prediction fuzzy controller for constant cutting force in
turning, International journal of machine tools &
manufacture, 45, 2005, 1047-1056
Volume 2, Issue 3 (2014) 676-683
ISSN 2347 - 3258
International Journal of Advance Research and Innovation
683
IJARI
[18] Ruturaj Kulkarni, Hazim A. El Mounayri, Simulate
turning process using ANN, predict optimum control
factors to achieve minimum surface roughness
[19] S. V. Bhaskara Reddy, M. S. Shunmugam, T. T.
Narendran, Optimal sub- division of the depth of cut to
achieve minimum production cost in multi-pass turning
using a genetic algorithm, journal of material
processing technology, 79, 1998, 101-108
[20] Sita rama raju k, Rajesh S, Rama Murty Raju P,
Predication of surface roughness in turning process
using soft computing techniques, Int. journal of applied
sciences and engineering research, 1(2), 2012
[21] Tugrul ozel, A. Esteves Correia, J. Paulo Davim,
Neural network process modeling for turning of steel
Dostları ilə paylaş: |