Improving environmental sustainability by formulation of generalized power consumption models using an ensemble based multi-gene genetic programming approach
Created by W.Langdon from
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- @Article{Garg:2015:JCPa,
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author = "Akhil1 Garg and Jasmine Siu Lee Lam",
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title = "Improving environmental sustainability by formulation
of generalized power consumption models using an
ensemble based multi-gene genetic programming
approach",
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journal = "Journal of Cleaner Production",
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volume = "102",
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pages = "246--263",
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year = "2015",
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ISSN = "0959-6526",
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DOI = "doi:10.1016/j.jclepro.2015.04.068",
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URL = "http://www.sciencedirect.com/science/article/pii/S0959652615004436",
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abstract = "Environmental sustainability is an important aspect
for accessing the performance of any machining
industry. Growing demand of customers for better
product quality has resulted in an increase in energy
consumption and thus a lower environmental performance.
Optimization of both product quality and energy
consumption is needed for improving economic and
environmental performance of the machining operations.
However, for achieving the global multi-objective
optimization, the models formulated must be able to
generalize the data accurately. In this context, an
evolutionary approach of multi-gene genetic programming
(MGGP) can be used to formulate the models for product
quality (surface roughness and tool life) and power
consumption. MGGP develops the model structure and its
coefficients based on the principles of genetic
algorithm (GA). Despite being widely applied, MGGP
generates models that may not give satisfactory
performance on the test data. The main reason behind
this is the inappropriate formulation procedure of the
multi-gene model and the difficulty in model selection.
Therefore, the present work proposes a new
ensemble-based-MGGP (EN-MGGP) framework that makes use
of statistical and classification strategies for
improving the generalization ability. The EN-MGGP
approach is applied on the reliable experimental
database (outputs: surface roughness, tool life and
power consumption) obtained from the literature, and
its performance is compared to that of the standardized
MGGP. The proposed EN-MGGP models outperformed the
standardized MGGP models. The conducted sensitivity and
parametric analysis validates the robustness of the
models by unveiling the non-linear relationships
between the outputs (surface roughness, tool life and
power consumption) and input parameters. It was also
found that the cutting speed has the most significant
impact on the power consumption in turning of AISI 1045
steel and the turning of 7075 Al alloy- 15 wtpercent
SIC composites. The generalized EN-MGGP models obtained
can easily be optimized analytically for attaining the
optimum input parameter settings that optimize the
product quality and power consumption simultaneously.",
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keywords = "genetic algorithms, genetic programming, Environmental
sustainability, Power consumption, Product quality,
Machining, Surface roughness",
- }
Genetic Programming entries for
Akhil Garg
Jasmine Siu Lee Lam
Citations