Surface roughness prediction by extreme learning machine constructed with abrasive water jet

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@Article{Cojbasic:2016:PE,
  author =       "Zarko Cojbasic and Dalibor Petkovic and 
                 Shahaboddin Shamshirband and Chong Wen Tong and Sudheer Ch and 
                 Predrag Jankovic and Nedeljko Ducic and 
                 Jelena Baralic",
  title =        "Surface roughness prediction by extreme learning
                 machine constructed with abrasive water jet",
  journal =      "Precision Engineering",
  volume =       "43",
  pages =        "86--92",
  year =         "2016",
  ISSN =         "0141-6359",
  DOI =          "doi:10.1016/j.precisioneng.2015.06.013",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0141635915001154",
  abstract =     "In this study, the novel method based on extreme
                 learning machine (ELM) is adapted to estimate roughness
                 of surface machined with abrasive water jet. Roughness
                 of surface is one of the main attributes of quality of
                 products derived from water jet processing, and
                 directly depends on the cutting parameters, such as
                 thickness of the workpiece, abrasive flow rate, cutting
                 speed and others. In this study, in order to provide
                 data on influence of parameters on surface roughness,
                 extensive experiments were carried out for different
                 cutting regimes. Measured data were used to model the
                 process by using ELM model. Estimation and prediction
                 results of ELM model were compared with genetic
                 programming (GP) and artificial neural networks (ANNs)
                 models. The experimental results show that an
                 improvement in predictive accuracy and capability of
                 generalization can be achieved by the ELM approach in
                 comparison with GP and ANN. Moreover, achieved results
                 indicate that developed ELM models can be used with
                 confidence for further work on formulating novel model
                 predictive strategy for roughness of the surface
                 machined with abrasive water jet. In conclusion, it is
                 conclusively found that application of ELM is
                 particularly promising as an alternative method to
                 estimate the roughness of the surface machined with
                 abrasive water jet.",
  keywords =     "genetic algorithms, genetic programming, Abrasive
                 water jet, Cutting, Surface roughness, Estimation,
                 Extreme learning machine (ELM)",
}

Genetic Programming entries for Zarko Cojbasic Dalibor Petkovic Shahaboddin Shamshirband Chong Wen Tong Sudheer Ch Predrag Jankovic Nedeljko Ducic Jelena Baralic

Citations