Generating Milling Tool Paths for Prismatic Parts Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Barclay:2015:Procedia,
  author =       "Jack Barclay and Vimal Dhokia and Aydin Nassehi",
  title =        "Generating Milling Tool Paths for Prismatic Parts
                 Using Genetic Programming",
  journal =      "Procedia CIRP",
  volume =       "33",
  pages =        "490--495",
  year =         "2015",
  note =         "9th CIRP Conference on Intelligent Computation in
                 Manufacturing Engineering - CIRP ICME 14",
  ISSN =         "2212-8271",
  DOI =          "doi:10.1016/j.procir.2015.06.060",
  URL =          "http://www.sciencedirect.com/science/article/pii/S2212827115007039",
  abstract =     "The automatic generation of milling tool paths
                 traditionally relies on applying complex tool path
                 generation algorithms to a geometric model of the
                 desired part. For parts with unusual geometries or
                 intricate intersections between sculpted surfaces,
                 manual intervention is often required when normal tool
                 path generation methods fail to produce efficient tool
                 paths. In this paper, a simplified model of the
                 machining process is used to create a domain-specific
                 language that enables tool paths to be generated and
                 optimised through an evolutionary process - formulated,
                 in this case, as a genetic programming system. The
                 driving force behind the optimisation is a fitness
                 function that promotes tool paths whose result matches
                 the desired part geometry and favours those that reach
                 their goal in fewer steps. Consequently, the system is
                 not reliant on tool path generation algorithms, but
                 instead requires a description of the desired
                 characteristics of a good solution, which can then be
                 used to measure and evaluate the relative performance
                 of the candidate solutions that are generated. The
                 performance of the system is less sensitive to
                 different geometries of the desired part and doesn't
                 require any additional rules to deal with changes to
                 the initial stock (e.g. when rest roughing). The method
                 is initially demonstrated on a number of simple test
                 components and the genetic programming process is shown
                 to positively influence the outcome. Further tests and
                 extensions to the work are presented.",
  keywords =     "genetic algorithms, genetic programming, Computer
                 numerical control (CNC), Milling",
  notes =        "Edited by Roberto Teti",
}

Genetic Programming entries for Jack Barclay Vimal Dhokia Aydin Nassehi

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