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


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2008-01

AUTHORS

S. Kumanan, C. P. Jesuthanam, R. Ashok Kumar

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. More... »

PAGES

778-788

References to SciGraph publications

  • 2001-05. A Fuzzy-Net-Based Multilevel In-Process Surface Roughness Recognition System in Milling Operations in THE INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • 1999-03. In-Process Surface Roughness Recognition (ISRR) System in End-Milling Operations in THE INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • 2002-09. Development of Empirical Models for Surface Roughness Prediction in Finish Turning in THE INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00170-006-0755-4

    DOI

    http://dx.doi.org/10.1007/s00170-006-0755-4

    DIMENSIONS

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