Modeling multiple-response environmental and manufacturing characteristics of EDM process

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@Article{Garg:2016:JCPa,
  author =       "Akhil1 Garg and Jasmine Siu Lee Lam and L. Gao",
  title =        "Modeling multiple-response environmental and
                 manufacturing characteristics of {EDM} process",
  journal =      "Journal of Cleaner Production",
  year =         "2016",
  volume =       "137",
  pages =        "1588--1601",
  month =        "20 " # nov,
  ISSN =         "0959-6526",
  DOI =          "doi:10.1016/j.jclepro.2016.04.070",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0959652616303389",
  abstract =     "Among the machining operations, Electrical discharge
                 machining (EDM) process is widely used in production
                 industries because of its ability to machine the
                 materials of any hardness. However, the machining of
                 advanced materials including ceramics, composites, and
                 super-alloys requiring the precise surface finish and
                 dimensional accuracy also increases the energy
                 consumption and cost simultaneously. As such, both
                 environmental and economic performances are
                 compromised. Also, EDM process is itself considered
                 hazardous because of the large toxic liquid and solid
                 wastes and gases produced due to reaction products
                 developed from highly energized dielectric media placed
                 between tool and workpiece. Thus, an appropriate
                 balance between manufacturing and environmental aspects
                 is highly desirable for ensuring higher productivity
                 and environmental sustainability of the process. In
                 this context, the present work proposes two variants of
                 optimization approach of genetic programming (GP) in
                 modelling the multi-response characteristics, i.e. two
                 environmental aspects (thermal energy consumption and
                 dielectric consumption) and one manufacturing aspect
                 (relative tool to wear ratio) of the EDM process. These
                 variants are proposed by introducing two model
                 selection criteria from statistical learning theory to
                 be used as fitness functions in the framework of GP.
                 The performance of the proposed GP models is evaluated
                 against the experimental data based on five statistical
                 error metrics and the two hypothesis tests. Further,
                 the relationships between manufacturing, environmental
                 aspects and the input process parameters are unveiled,
                 which can be used by industry users to optimize the
                 process economically and environmentally. It was found
                 that the input peak current has the highest impact on
                 the environmental aspects of the EDM process.",
  keywords =     "genetic algorithms, genetic programming, Electrical
                 discharge machining (EDM), Machining, Environmental,
                 Energy consumption, Relative tool to wear ratio",
}

Genetic Programming entries for Akhil Garg Jasmine Siu Lee Lam Liang Gao

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