A hybrid M5'-genetic programming approach for ensuring greater trustworthiness of prediction ability in modelling of FDM process
Created by W.Langdon from
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- @Article{journals/jim/GargTLS14,
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title = "A hybrid {M5'-genetic programming} approach for
ensuring greater trustworthiness of prediction ability
in modelling of {FDM} process",
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author = "A. Garg and K. Tai and C. H. Lee and M. M. Savalani",
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journal = "Journal of Intelligent Manufacturing",
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year = "2014",
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number = "6",
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volume = "25",
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pages = "1349--1365",
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keywords = "genetic algorithms, genetic programming, M5,
Artificial neural network, ANN, Trustworthiness,
Support vector regression, SVM, Fused deposition
modelling, Rapid prototyping",
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ISSN = "0956-5515",
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bibdate = "2014-11-11",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/jim/jim25.html#GargTLS14",
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URL = "http://dx.doi.org/10.1007/s10845-013-0734-1",
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size = "17 pages",
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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. 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 models.",
- }
Genetic Programming entries for
Akhil Garg
Kang Tai
C H Lee
M M Savalani
Citations