Manufacturing Technology Today

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Prediction of Surface Roughness in Turning Using RBFNN-FL Technique


Affiliations
1 Department of Production Engineering, National Institute of Technology, Tiruchirapalli-620015, India
     

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Surface roughness is an important aspect in evaluating the quality of a machined product and it is influenced by the machining conditions. Conventional surface roughness measuring instruments are of contact type, offline and post processing in nature. Presently there is an increase in the demand for the use of intelligent techniques in manufacturing applications. In this paper a hybrid intelligent technique is presented by combining Radial Basis Function Neural Network (RBFNN) and Fuzzy Logic (FL) for the prediction of surface roughness in turning operations to achieve effective automation. A comparison is made between the use of RBFNN and hybrid RBFNN-FL technique.
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  • Prediction of Surface Roughness in Turning Using RBFNN-FL Technique

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Authors

C. P. Jesuthanam
Department of Production Engineering, National Institute of Technology, Tiruchirapalli-620015, India
S. Kumanan
Department of Production Engineering, National Institute of Technology, Tiruchirapalli-620015, India

Abstract


Surface roughness is an important aspect in evaluating the quality of a machined product and it is influenced by the machining conditions. Conventional surface roughness measuring instruments are of contact type, offline and post processing in nature. Presently there is an increase in the demand for the use of intelligent techniques in manufacturing applications. In this paper a hybrid intelligent technique is presented by combining Radial Basis Function Neural Network (RBFNN) and Fuzzy Logic (FL) for the prediction of surface roughness in turning operations to achieve effective automation. A comparison is made between the use of RBFNN and hybrid RBFNN-FL technique.