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Prediction of burr formation during face milling using a hybrid GMDH network model with optimized cutting conditions

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Abstract

In this paper, a combined hybrid group method for data handling and optimization approach is introduced to predict burr types formed during face milling. The hybrid group method for data handling (hybrid GMDH) network was constructed for realizing predictive models for the machining of aluminum alloy, and differential evolution was selected for the optimization of burr formation problem resulting in finding optimal parameter for minimizing burr formation. Burr type was included as a parameter resulting in a classification scheme in which the burr type becomes the group label and it is therefore possible in the future to classify a machining process into any of these burr types. The resulting hybrid GMDH output was in agreement with experimental results, thereby validating the proposed scheme for modeling and prediction of burr formation in milling operations.

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Authors and Affiliations

  1. School of Engineering and Physics, University of the South Pacific, Private Bag, Suva, Fiji

    G. C. Onwubolu

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  1. G. C. Onwubolu
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Onwubolu, G.C. Prediction of burr formation during face milling using a hybrid GMDH network model with optimized cutting conditions. Int J Adv Manuf Technol 44, 1083–1093 (2009). https://doi.org/10.1007/s00170-008-1909-3

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  • DOI: https://doi.org/10.1007/s00170-008-1909-3

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