Comparing Predictability of Genetic Programming and ANFIS on Drilling Performance Modeling for GFRP Composites

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@Article{Abhishek:2014:PMS,
  author =       "Kumar Abhishek and Biranchi Narayan Panda and 
                 Saurav Datta and Siba Sankar Mahapatra",
  title =        "Comparing Predictability of Genetic Programming and
                 {ANFIS} on Drilling Performance Modeling for {GFRP}
                 Composites",
  journal =      "Procedia Materials Science",
  volume =       "6",
  pages =        "544--550",
  year =         "2014",
  note =         "3rd International Conference on Materials Processing
                 and Characterisation (ICMPC 2014)",
  ISSN =         "2211-8128",
  DOI =          "doi:10.1016/j.mspro.2014.07.069",
  URL =          "http://www.sciencedirect.com/science/article/pii/S2211812814004349",
  abstract =     "Drilling of glass fibre reinforced polymer (GFRP)
                 composite material is substantially complicated from
                 the metallic materials due to its high structural
                 stiffness (of the composite) and low thermal
                 conductivity of plastics. During drilling of GFRP
                 composites, problems generally arise like fibre pull
                 out, delamination, stress concentration, swelling,
                 burr, splintering and micro cracking etc. which reduces
                 overall machining performance. Now-a-days hybrid
                 approaches have been received remarkable attention in
                 order to model machining process behaviour and to
                 optimise machining performance towards subsequent
                 improvement of both quality and productivity,
                 simultaneously. In the present research, spindle speed,
                 feed rate, plate thickness and drill bit diameter have
                 been considered as input parameters; and the machining
                 yield characteristics have been considered in terms of
                 thrust and surface roughness (output responses) of the
                 drilled composite product. The study illustrates the
                 applicability of genetic programming with the help of
                 GPTIPS as well as Adaptive Neuro Fuzzy Inference System
                 (ANFIS) towards generating prediction models for better
                 understanding of the process behavior and for improving
                 process performances in drilling of GFRP composites.",
  keywords =     "genetic algorithms, genetic programming, Glass fibre
                 reinforced polymer (GFRP), Adaptive Neuro Fuzzy
                 Inference System (ANFIS), GPTIPS.",
}

Genetic Programming entries for Kumar Abhishek Biranchi Narayan Panda Saurav Datta Siba Sankar Mahapatra

Citations