Energy conservation in manufacturing operations: modelling the milling process by a new complexity-based evolutionary approach
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- @Article{Garg:2015:JCP,
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author = "Akhil1 Garg and Jasmine Siu Lee Lam and L. Gao",
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title = "Energy conservation in manufacturing operations:
modelling the milling process by a new complexity-based
evolutionary approach",
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journal = "Journal of Cleaner Production",
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volume = "108, Part A",
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pages = "34--45",
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year = "2015",
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ISSN = "0959-6526",
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DOI = "doi:10.1016/j.jclepro.2015.06.043",
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URL = "http://www.sciencedirect.com/science/article/pii/S0959652615007726",
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abstract = "From the perspective of energy conservation, the
notion of modelling of energy consumption as a vital
element of environmental sustainability in any
manufacturing industry remains a current and important
focus of study for climate change experts across the
globe. Among the manufacturing operations, machining is
widely performed. Extensive studies by peer researchers
reveal that the focus was on modelling and optimizing
the manufacturing aspects (e.g. surface roughness, tool
wear rate, dimensional accuracy) of the machining
operations by computational intelligence methods such
as analysis of variance, grey relational analysis,
Taguchi method, and artificial neural network.
Alternatively, an evolutionary based multi-gene genetic
programming approach can be applied but its effective
functioning depends on the complexity measure chosen in
its fitness function. This study proposes a new
complexity-based multi-gene genetic programming
approach based on orthogonal basis functions and
compares its performance to that of the standardized
multi-gene genetic programming in modelling of energy
consumption of the milling process. The hidden
relationships between the energy consumption and the
input process parameters are unveiled by conducting
sensitivity and parametric analysis. From these
relationships, an optimum set of input settings can be
obtained which will conserve greater amount of energy
from these operations. It was found that the cutting
speed has the highest impact on the milling process
followed by feed rate and depth of cut.",
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keywords = "genetic algorithms, genetic programming, Environmental
sustainability, Energy conservation, Energy
consumption, Machining, Computational intelligence,
Milling process",
- }
Genetic Programming entries for
Akhil Garg
Jasmine Siu Lee Lam
Liang Gao
Citations