Study of effect of nanofluid concentration on response characteristics of machining process for cleaner production

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@Article{Garg:2016:JCPb,
  author =       "Akhil Garg and Shrutidhara Sarma and B. N. Panda and 
                 Jian Zhang and L. Gao",
  title =        "Study of effect of nanofluid concentration on response
                 characteristics of machining process for cleaner
                 production",
  journal =      "Journal of Cleaner Production",
  year =         "2016",
  volume =       "135",
  pages =        "476--489",
  month =        "1 " # nov,
  ISSN =         "0959-6526",
  DOI =          "doi:10.1016/j.jclepro.2016.06.122",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0959652616307995",
  abstract =     "With the ever-increasing concern for reducing
                 environmental pollution and waste minimization,
                 {"}green manufacturing{"} has been successful to draw
                 sufficient amount of attention towards it. Minimum
                 Quantity Lubrication (MQL) is one such technique that
                 has revolutionized the manufacturing industry by not
                 only reducing the amount of working fluid dramatically
                 but also enhancing cutting tool life and reducing
                 material costs. Past studies have reported the use of
                 experiments in MQL based manufacturing but limited
                 computational modeling for optimizing the process
                 parameters Based on the past experimental procedure of
                 machining process (micro-drilling), a computational
                 framework such as Adaptive Neuro Fuzzy Inference System
                 (ANFIS) and Genetic Programming (GP) in quantification
                 of three response characteristics (torque, thrust
                 forces and material removal rate (MRR) is proposed. The
                 performance analysis based on the cross-validation,
                 error metrics, curve fitting and hypothesis tests
                 reveals that among the two models, the GP models have
                 performed better. 2-D and 3-D surface analysis were
                 performed to validate the robustness of the models.
                 Among the three response characteristics, It was found
                 that the nanofluid concentration influences torque the
                 most, which is an important aspect for power
                 consumption. At nanofluid concentration values of 1.4
                 and 4, the minimum values of torque and thrust forces
                 is achieved respectively. When drill diameter is
                 minimum and the spindle speed is maximum, the values of
                 torque, thrust forces and MRR is the lowest. Thus, the
                 feed rate, nanofluid concentration and drill diameter
                 are most critical for obtaining higher MRR and lower
                 values of torque and thrust force, thus enabling
                 cleaner production and environment.",
  keywords =     "genetic algorithms, genetic programming, Minimum
                 quality lubrication, Green manufacturing,
                 Micro-drilling process, Torque, Drill diameter",
}

Genetic Programming entries for Akhil Garg Shrutidhara Sarma Biranchi Narayan Panda Jian Zhang Liang Gao

Citations