A hybrid M5'-genetic programming approach for ensuring greater trustworthiness of prediction ability in modelling of FDM process
Created by W.Langdon from
gp-bibliography.bib Revision:1.6567
- @Article{journals/jim/GargTLS14,
-
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,
http://dblp.uni-trier.de/db/journals/jim/jim25.html#GargTLS14",
-
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
models.",
- }
Genetic Programming entries for
Akhil Garg
Kang Tai
C H Lee
M M Savalani
Citations