NDLI: Application of multiple regression and adaptive neuro fuzzy inference system for the prediction of surface roughness

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Application of multiple regression and adaptive neuro fuzzy inference system for the prediction of surface roughness

Content Provider	Semantic Scholar
Author	Kumanan, S. Jesuthanam, C. P. Kumar, R. Ashok
Copyright Year	2008
Abstract	A manufacturing system is oriented towards higher production rate, quality, and reduced cost and time to make a product. Surface roughness is an index for determining the quality of machined products and is influenced by the cutting parameters. Surface roughness prediction in machining is being attempted with many methodologies, yet there is a need to develop robust, autonomous and accurate predictive system. This work proposes the application of two different hybrid intelligent techniques, adaptive neuro fuzzy inference system (ANFIS) and radial basis function neural network- fuzzy logic (RBFNN-FL) for the prediction of surface roughness in end milling. An experimental data set is obtained with speed, feed, depth of cut and vibration as input parameters and surface roughness as output parameter. The input-output data set is used for training and validation of the proposed techniques. After validation they are forwarded for the prediction of surface roughness. Both the hybrid techniques are found to be superior over their respective individual intelligent techniques in terms of computational speed and accuracy for the prediction of surface roughness.
Starting Page	778
Ending Page	788
Page Count	11
File Format	PDF HTM / HTML
DOI	10.1007/s00170-006-0755-4
Volume Number	35
Alternate Webpage(s)	https://page-one.springer.com/pdf/preview/10.1007/s00170-006-0755-4
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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