Neural Network and Fuzzy Logic based prediction of Surface Roughness and MRR in Cylindrical Grinding Process

Author:

Sudheer Kumar Varma N.,Rajesh S.,Sita Rama Raju K.,Murali Krishnam Raju V.V.

Publisher

Elsevier BV

Subject

General Medicine

Reference17 articles.

1. Kirankumar, Ramakantrao and S.B.Ubale. Optimization of cylindrical grinding process parameters for AISI 1040 steel using taguchi method, International Journal of Mechanical Engineering and Technology, 2012; 3 (1): 226–234.

2. Li john P George, K Varughese Job and I M Chandran. Study on surface roughness and its prediction in cylindrical grinding process based on Taguchi method of optimization, International Journal of Scientific and Research Publications, 2013; 3(5): 5–11.

3. Radrigo, Carlosbianchi, Eduardo Catai, Rodringo, Deaguiar and Paulo Roberto. Analysis of the different forms of application and types of cutting fluid used in plunge cylindrical grinding using conventional and superabrasive CBN grinding wheels;Monici;International journal of machine tools & manufacture,2006

4. Investigation of grinding characteristic using nanofluid minimum quantity lubrication;Mao;International Journal of Precision Engineering and Manufacturing,2012

5. Deepak Pal, Ajay Bangar, Rajan Sharma and Ashish Yadav. Optimization of grinding parameters for minimum surface roughness by Taguchi parametric optimization technique. International Journal of Mechanical and Industrial Engineering, 2012; 1(3): ISSN No. 2231–6477.

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