Energy conservation in manufacturing operations: modelling the milling process by a new complexity-based evolutionary approach

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@Article{Garg:2015:JCP,
  author =       "Akhil1 Garg and Jasmine Siu Lee Lam and L. Gao",
  title =        "Energy conservation in manufacturing operations:
                 modelling the milling process by a new complexity-based
                 evolutionary approach",
  journal =      "Journal of Cleaner Production",
  volume =       "108, Part A",
  pages =        "34--45",
  year =         "2015",
  ISSN =         "0959-6526",
  DOI =          "doi:10.1016/j.jclepro.2015.06.043",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0959652615007726",
  abstract =     "From the perspective of energy conservation, the
                 notion of modelling of energy consumption as a vital
                 element of environmental sustainability in any
                 manufacturing industry remains a current and important
                 focus of study for climate change experts across the
                 globe. Among the manufacturing operations, machining is
                 widely performed. Extensive studies by peer researchers
                 reveal that the focus was on modelling and optimizing
                 the manufacturing aspects (e.g. surface roughness, tool
                 wear rate, dimensional accuracy) of the machining
                 operations by computational intelligence methods such
                 as analysis of variance, grey relational analysis,
                 Taguchi method, and artificial neural network.
                 Alternatively, an evolutionary based multi-gene genetic
                 programming approach can be applied but its effective
                 functioning depends on the complexity measure chosen in
                 its fitness function. This study proposes a new
                 complexity-based multi-gene genetic programming
                 approach based on orthogonal basis functions and
                 compares its performance to that of the standardized
                 multi-gene genetic programming in modelling of energy
                 consumption of the milling process. The hidden
                 relationships between the energy consumption and the
                 input process parameters are unveiled by conducting
                 sensitivity and parametric analysis. From these
                 relationships, an optimum set of input settings can be
                 obtained which will conserve greater amount of energy
                 from these operations. It was found that the cutting
                 speed has the highest impact on the milling process
                 followed by feed rate and depth of cut.",
  keywords =     "genetic algorithms, genetic programming, Environmental
                 sustainability, Energy conservation, Energy
                 consumption, Machining, Computational intelligence,
                 Milling process",
}

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

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