Measurement of environmental aspect of 3-D printing process using soft computing methods

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

@Article{Garg:2015:Measurement,
  author =       "Akhil1 Garg and Jasmine Siu Lee Lam",
  title =        "Measurement of environmental aspect of {3-D} printing
                 process using soft computing methods",
  journal =      "Measurement",
  volume =       "75",
  pages =        "210--217",
  year =         "2015",
  ISSN =         "0263-2241",
  DOI =          "doi:10.1016/j.measurement.2015.04.016",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0263224115002195",
  abstract =     "For improving the environmental performance of the
                 manufacturing industry across the globe, 3-D printing
                 technology should be increasingly adopted as a
                 manufacturing procedure. It is because this technology
                 uses the polymer PLA (Polyactic acid) as a material,
                 which is biodegradable, and saves fuel and reduces
                 waste when fabricating prototypes. In addition, the
                 technology can be located near to industries and
                 fabricates raw material itself, resulting in reduction
                 of transport costs and carbon emission. However, due to
                 its high production cost, 3-D printing technology is
                 not yet being adopted globally. One way of reducing the
                 production cost and improving environmental performance
                 is to formulate models that can be used to operate 3-D
                 printing technology in an efficient way. Therefore,
                 this paper aims to deploy the soft computing methods
                 such as genetic programming (GP), support vector
                 regression and artificial neural network in formulating
                 the laser power-based-open porosity models. These
                 methods are applied on the selective laser sintering (a
                 3-D printing process) process data. It is found that GP
                 evolves the best model that is able to predict open
                 porosity satisfactorily based on given values of laser
                 power. The laser power-based-open porosity model
                 formulated can assist decision makers in operating the
                 SLS process in an effective and efficient way, thus
                 increasing its viability for being adopted as a
                 manufacturing procedure and paving the way for a
                 sustainable environment across the globe.",
  keywords =     "genetic algorithms, genetic programming, Selective
                 laser sintering, Soft computing methods, Open porosity
                 prediction, 3-D printing, Environmental aspect",
}

Genetic Programming entries for Akhil Garg Jasmine Siu Lee Lam

Citations