Review of empirical modelling techniques for modelling of turning process
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- @Article{Garg:2013:IJMIC,
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author = "Akhil Garg and Yogesh Bhalerao and Kang Tai",
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title = "Review of empirical modelling techniques for modelling
of turning process",
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journal = "International Journal of Modelling, Identification and
Control, Vol. 20, No. 2, 2013",
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year = "2013",
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month = aug # "~31",
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volume = "20",
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number = "2",
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pages = "121--129",
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keywords = "genetic algorithms, genetic programming, empirical
modelling, turning, artificial neural networks, ANNs,
review, regression analysis, fuzzy logic, support
vector machines, SVM",
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ISSN = "1746-6180",
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publisher = "Inderscience Publishers",
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bibsource = "OAI-PMH server at www.inderscience.com",
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language = "eng",
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URL = "http://www.inderscience.com/link.php?id=56184",
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DOI = "DOI:10.1504/IJMIC.2013.056184",
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abstract = "The most widely and well known machining process used
is turning. The turning process possesses higher
complexity and uncertainty and therefore several
empirical modelling techniques such as artificial
neural networks, regression analysis, fuzzy logic and
support vector machines have been used for predicting
the performance of the process. This paper reviews the
applications of empirical modelling techniques in
modelling of turning process and unearths the vital
issues related to it.",
- }
Genetic Programming entries for
Akhil Garg
Yogesh Bhalerao
Kang Tai
Citations