Comparative Evaluation of Optimization Algorithms at Training of Genetic Programming for Tensile Strength Prediction of FDM Processed Part

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Panda:2014:PMS,
  author =       "Biranchi Narayan Panda and 
                 M. V. A. Raju Bahubalendruni and Bibhuti Bhusan Biswal",
  title =        "Comparative Evaluation of Optimization Algorithms at
                 Training of Genetic Programming for Tensile Strength
                 Prediction of {FDM} Processed Part",
  journal =      "Procedia Materials Science",
  volume =       "5",
  pages =        "2250--2257",
  year =         "2014",
  note =         "International Conference on Advances in Manufacturing
                 and Materials Engineering, ICAMME 2014",
  ISSN =         "2211-8128",
  DOI =          "doi:10.1016/j.mspro.2014.07.441",
  URL =          "http://www.sciencedirect.com/science/article/pii/S2211812814008062",
  abstract =     "Fused deposition modelling (FDM) is a fast growing
                 rapid prototyping (RP) technology due to its ability to
                 build functional parts having complex geometrical
                 shapes in reasonable build time. The literature reveals
                 that the properties of RP built parts such as surface
                 roughness, strength, dimensional accuracy, build cost,
                 etc are related to and can be improved by the
                 appropriate settings of the input process parameters.
                 However, it is difficult to obtain an adequate strength
                 for some applications due to the characteristics of the
                 process. This paper, proposes particle swarm
                 optimisation (PSO) technique to suggest theoretical
                 combination of parameter settings to achieve good
                 strength simultaneously for all responses. Genetic
                 Programming (GP) is used to approximate the
                 relationship between process parameters (Layer
                 thickness, Raster angle, Raster width and Air gap) and
                 the Tensile strength that can withstand the part. The
                 performance of the proposed method is compared with
                 Differential Evolution (DE) algorithm in terms of
                 prediction accuracy and convergence characteristics.
                 Results show the potential of the both algorithm used
                 for, but DEA gives better result than PSO.",
  keywords =     "genetic algorithms, genetic programming, Fused
                 Depostion Modelling (FDM), Differential Evolution
                 Algorithm (DEA), PSO.",
}

Genetic Programming entries for Biranchi Narayan Panda M V A Raju Bahubalendruni Bibhuti Bhusan Biswal

Citations