Application of Free Pattern Search on the Surface Roughness Prediction in End Milling

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  title =        "Application of Free Pattern Search on the Surface
                 Roughness Prediction in End Milling",
  author =       "Long Wen and Liang Gao and Xinyu Li and 
                 Guohui Zhang and Yang Yang",
  pages =        "765--770",
  booktitle =    "Proceedings of the 2012 IEEE Congress on Evolutionary
  year =         "2012",
  editor =       "Xiaodong Li",
  month =        "10-15 " # jun,
  DOI =          "doi:10.1109/CEC.2012.6256605",
  address =      "Brisbane, Australia",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, genetic programming, Data mining,
                 Classification, clustering, data analysis and data
  abstract =     "Surface roughness has a great influence on the product
                 properties. Predicting the surface roughness is an
                 important work for modern manufacturing industry. In
                 this paper, a novel prediction method called Free
                 Pattern Search (FPS) is proposed to explicitly
                 construct the surface roughness prediction model. FPS
                 takes the advantage of the expression tree in gene
                 expression programming (GEP) to encode the solution and
                 to expresses a non-determinative tree using a fixed
                 length individual. FPS is inspired by Pattern Search
                 (PS) and hybrid a scatter manipulator to keep the
                 diversity of the population. Three machining
                 parameters, the spindle speed, feed rate and the depth
                 of cut are used as the independent input variables when
                 prediction the surface roughness in end milling.
                 Experiments are conducted to verify the performance of
                 FPS and FPS obtains good results compared with other
                 algorithm. The predictive model found by FPS agrees
                 with the experimental result. The variable relations
                 are also showed in the predictive model, and the
                 results shows that they are fit to the experiments
  notes =        "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the
                 EPS and the IET.",

Genetic Programming entries for Long Wen Liang Gao Xinyu Li Guohui Zhang Yang Yang