Scholarly Research Excellence
@article{(International Science Index):http://waset.org/publications/4729,
  title    = {Rough Set Based Intelligent Welding Quality Classification},
  author    = {L. Tao and  T. J. Sun and  Z. H. Li},
  country   = {},
  institution={},
  abstract  = {The knowledge base of welding defect recognition is
essentially incomplete. This characteristic determines that the recognition results do not reflect the actual situation. It also has a further influence on the classification of welding quality. This paper is
concerned with the study of a rough set based method to reduce the influence and improve the classification accuracy. At first, a rough set
model of welding quality intelligent classification has been built. Both condition and decision attributes have been specified. Later on, groups
of the representative multiple compound defects have been chosen
from the defect library and then classified correctly to form the
decision table. Finally, the redundant information of the decision table has been reducted and the optimal decision rules have been reached. By this method, we are able to reclassify the misclassified defects to
the right quality level. Compared with the ordinary ones, this method
has higher accuracy and better robustness.},
    journal   = {International Journal of Chemical, Molecular, Nuclear, Materials and Metallurgical Engineering},  volume    = {5},
  number    = {12},
  year      = {2011},
  pages     = {1128 - 1131},
  ee        = {http://waset.org/publications/4729},
  url       = {http://waset.org/Publications?p=60},
  bibsource = {http://waset.org/Publications},
  issn      = {eISSN:1307-6892},
  publisher = {World Academy of Science, Engineering and Technology},
  index     = {International Science Index 60, 2011},
}