A hybrid M5'-genetic programming approach for ensuring greater trustworthiness of prediction ability in modelling of FDM process

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  title =        "A hybrid {M5'-genetic programming} approach for
                 ensuring greater trustworthiness of prediction ability
                 in modelling of {FDM} process",
  author =       "A. Garg and K. Tai and C. H. Lee and M. M. Savalani",
  journal =      "J. Intelligent Manufacturing",
  year =         "2014",
  number =       "6",
  volume =       "25",
  pages =        "1349--1365",
  keywords =     "genetic algorithms, genetic programming",
  bibdate =      "2014-11-11",
  bibsource =    "DBLP,
  URL =          "http://dx.doi.org/10.1007/s10845-013-0734-1",
  size =         "17 pages",
  abstract =     "Recent years have seen various rapid prototyping (RP)
                 processes such as fused deposition modelling (FDM) and
                 three-dimensional printing being used for fabricating
                 prototypes, leading to shorter product development
                 times and less human intervention. The literature
                 reveals that the properties of RP built parts such as
                 surface roughness, strength, dimensional accuracy,
                 build cost, etc are related to and can be improved by
                 the appropriate settings of the input process
                 parameters. Researchers have formulated physics-based
                 models and applied empirical modelling techniques such
                 as regression analysis and artificial neural network
                 for the modelling of RP processes. Physics-based models
                 require in-depth understanding of the processes which
                 is a formidable task due to their complexity. The issue
                 of improving trustworthiness of the prediction ability
                 of empirical models on test (unseen) samples is paid
                 little attention. In the present work, a hybrid
                 M5'-genetic programming (M5'-GP) approach is proposed
                 for empirical modelling of the FDM process with an
                 attempt to resolve this issue of ensuring
                 trustworthiness. This methodology is based on the error
                 compensation achieved using a GP model in parallel with
                 a M5' model. The performance of the proposed hybrid
                 model is compared to those of support vector regression
                 (SVR) and adaptive neuro fuzzy inference system (ANFIS)
                 model and it is found that the M5'-GP model has the
                 goodness of fit better than those of the SVR and ANFIS

Genetic Programming entries for Akhil Garg Kang Tai C H Lee M M Savalani