Application of Free Pattern Search on the Surface Roughness Prediction in End Milling
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{Wen:2012:CEC,
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title = "Application of Free Pattern Search on the Surface
Roughness Prediction in End Milling",
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author = "Long Wen and Liang Gao and Xinyu Li and
Guohui Zhang and Yang Yang",
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pages = "765--770",
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booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary
Computation",
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year = "2012",
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editor = "Xiaodong Li",
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month = "10-15 " # jun,
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DOI = "doi:10.1109/CEC.2012.6256605",
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address = "Brisbane, Australia",
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ISBN = "0-7803-8515-2",
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keywords = "genetic algorithms, genetic programming, Data mining,
Classification, clustering, data analysis and data
mining",
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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
well.",
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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
Citations