Power consumption and tool life models for the production process

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

@Article{Garg:2016:JCP,
  author =       "Akhil1 Garg and Jasmine Siu Lee Lam",
  title =        "Power consumption and tool life models for the
                 production process",
  journal =      "Journal of Cleaner Production",
  year =         "2016",
  volume =       "131",
  pages =        "754--764",
  ISSN =         "0959-6526",
  DOI =          "doi:10.1016/j.jclepro.2016.04.099",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0959652616303754",
  abstract =     "For achieving the multi-objective optimization of
                 product quality and power consumption of any production
                 process, the formulation of generalized models is
                 essential. Extensive research has been done on applying
                 the traditional statistical methods (analysis of
                 variance, response surface methodology, grey relational
                 analysis, Taguchi method) in formulation of these
                 models for the processes. In the present work, a
                 detailed survey on the applications of these methods in
                 modelling of power consumption for the production
                 operations specifically machining is conducted.
                 Critical issues arising from the survey are highlighted
                 and hence form the motivation of this study. Further,
                 three advanced soft computing methods, namely
                 evolutionary-based genetic programming (GP), support
                 vector regression, and multi-adaptive regression
                 splines are proposed in predictive modelling of tool
                 life and power consumption of a turning phenomenon in
                 machining. Statistical comparison based on the five
                 error metrics and hypothesis tests for the goodness of
                 the fit reveals that the GP model outperforms the other
                 two models. The hidden relationships between the
                 process parameters are unveiled from the formulated
                 models. It is found that the cutting speed parameter is
                 the most influential input for power consumption and
                 tool life in the turning phenomenon. The future scope
                 comprising of the challenges in predictive modelling of
                 production processes is highlighted in the end.",
  keywords =     "genetic algorithms, genetic programming, Power
                 consumption, Machining, Environmental, Tool life, Soft
                 computing methods",
}

Genetic Programming entries for Akhil Garg Jasmine Siu Lee Lam

Citations