Modeling and optimization of surface roughness in single point incremental forming process

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@Article{Kurra:2015:JMRT,
  author =       "Suresh Kurra and Nasih Hifzur Rahman and 
                 Srinivasa Prakash Regalla and Amit Kumar Gupta",
  title =        "Modeling and optimization of surface roughness in
                 single point incremental forming process",
  journal =      "Journal of Materials Research and Technology",
  year =         "2015",
  volume =       "4",
  number =       "3",
  month =        jul # "-" # sep,
  pages =        "304--313",
  keywords =     "genetic algorithms, genetic programming, Incremental
                 forming, Surface roughness, Artificial neural networks,
                 ANN, Support vector regression, SVM",
  ISSN =         "2238-7854",
  DOI =          "doi:10.1016/j.jmrt.2015.01.003",
  URL =          "http://www.sciencedirect.com/science/article/pii/S2238785415000071",
  size =         "10 pages",
  abstract =     "Single point incremental forming (SPIF) is a novel and
                 potential process for sheet metal prototyping and low
                 volume production applications. This article is focuses
                 on the development of predictive models for surface
                 roughness estimation in SPIF process. Surface roughness
                 in SPIF has been modelled using three different
                 techniques namely, Artificial Neural Networks (ANN),
                 Support Vector Regression (SVR) and Genetic Programming
                 (GP). In the development of these predictive models,
                 tool diameter, step depth, wall angle, feed rate and
                 lubricant type have been considered as model variables.
                 Arithmetic mean surface roughness (Ra) and maximum peak
                 to valley height (Rz) are used as response variables to
                 assess the surface roughness of incrementally formed
                 parts. The data required to generate, compare and
                 evaluate the proposed models have been obtained from
                 SPIF experiments performed on Computer Numerical
                 Control (CNC) milling machine using Box-Behnken design.
                 The developed models are having satisfactory goodness
                 of fit in predicting the surface roughness. Further,
                 the GP model has been used for optimisation of Ra and
                 Rz using genetic algorithm. The optimum process
                 parameters for minimum surface roughness in SPIF have
                 been obtained and validated with the experiments and
                 found highly satisfactory results within 10percent
                 error.",
  notes =        "Department of Mechanical Engineering, Birla Institute
                 of Technology and Science, Pilani, Hyderabad Campus,
                 Hyderabad, AP, India",
}

Genetic Programming entries for Kurra Suresh Nasih Hifzur Rahman Srinivasa Prakash Regalla Amit Kumar Gupta

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