Review of empirical modelling techniques for modelling of turning process

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@Article{Garg:2013:IJMIC,

• author = "Akhil Garg and Yogesh Bhalerao and Kang Tai",
• title = "Review of empirical modelling techniques for modelling of turning process",
• journal = "International Journal of Modelling, Identification and Control, Vol. 20, No. 2, 2013",
• year = "2013",
• month = aug # "~31",
• volume = "20",
• number = "2",
• pages = "121--129",
• keywords = "genetic algorithms, genetic programming, empirical modelling, turning, artificial neural networks, ANNs, review, regression analysis, fuzzy logic, support vector machines, SVM",
• ISSN = "1746-6180",
• publisher = "Inderscience Publishers",
• bibsource = "OAI-PMH server at www.inderscience.com",
• language = "eng",
• URL = "http://www.inderscience.com/link.php?id=56184",
• DOI = "DOI:10.1504/IJMIC.2013.056184",
• 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

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