Title of article :
Adaptive neuro-fuzzy inference system modeling of cryogenically treated AISI M2 HSS turning tool for estimation of flank wear
Author/Authors :
Gill، نويسنده , , Simranpreet Singh and Singh، نويسنده , , Rupinder and Singh، نويسنده , , Jagdev and Singh، نويسنده , , Harpreet، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2012
Pages :
10
From page :
4171
To page :
4180
Abstract :
This study deals with modeling the flank wear of cryogenically treated AISI M2 high speed steel (HSS) tool by means of adaptive neuro-fuzzy inference system (ANFIS) approach. Cryogenic treatment has recently been found to be an innovative technique to improve wear resistance of AISI M2 HSS tools but precise modelling approach which also incorporates the cryogenic soaking temperature to simulate the tool flank wear is still not reported in any open literature. In order to obtain data for developing the ANFIS model, turning of hot rolled annealed steel stock (C-45) by cryogenically treated tools treated at various cryogenic soaking temperatures was performed in steady state conditions while varying the cutting speed and cutting time. The model combined modeling function of fuzzy inference with the learning ability of artificial neural network; and a set of rules has been generated directly from experimental data. It was determined that the predictions usually agreed well with the experimental data with correlation coefficients of 0.994 and mean errors of 2.47%. The proposed model can also be used for estimating tool flank wear on-line but the accuracy of the model depends upon the proper training and selection of data points.
Keywords :
ANFIS , Tool flank wear , cryogenic treatment , Fuzzy Logic , NEURAL NETWORKS , Soaking temperature
Journal title :
Expert Systems with Applications
Serial Year :
2012
Journal title :
Expert Systems with Applications
Record number :
2351430
Link To Document :
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