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

被引:10
|
作者
Varma, N. Sudheer Kumar [1 ]
Rajesh, S. [1 ]
Raju, K. Sita Rama [1 ]
Raju, V. V. Murali Krishnam [1 ]
机构
[1] SRKR Engn Coll, Dept Mech Engn, Bhimavaram 534204, Andhra Prades, India
关键词
Surface roughness; Material removal rate; Neural network; ANFIS; Grinding; MODEL;
D O I
10.1016/j.matpr.2017.07.154
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Surface roughness and metal removal rate are the prominent output responses that influence the quality and cost of production. Moreover, in this present investigation neural network modeling and Adaptive-neuro fuzzy inference system (ANFIS) are employed to predict the output responses with respect to a variety of cutting parameters in cylindrical grinding process. A full factorial experimental design is conducted on AISI 1040 steel; considering work speed, depth of cut and feed rate as the cutting parameters. In addition 8 sets of experiments are performed to validate the computational methods. Our comparative study on identifying a better prediction methodology illustrated ANFIS as a better technique with 91% accuracy, while depth of cut as the most influential parameter effecting output responses. (C) 2017 Published by Elsevier Ltd.
引用
收藏
页码:8134 / 8141
页数:8
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