Generating Milling Tool Paths for Prismatic Parts Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7917

@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 G M Barclay Vimal Dhokia Aydin Nassehi

Citations