Modeling multiple-response environmental and manufacturing characteristics of EDM process
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- @Article{Garg:2016:JCPa,
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author = "Akhil1 Garg and Jasmine Siu Lee Lam and L. Gao",
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title = "Modeling multiple-response environmental and
manufacturing characteristics of {EDM} process",
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journal = "Journal of Cleaner Production",
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year = "2016",
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volume = "137",
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pages = "1588--1601",
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month = "20 " # nov,
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ISSN = "0959-6526",
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DOI = "doi:10.1016/j.jclepro.2016.04.070",
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URL = "http://www.sciencedirect.com/science/article/pii/S0959652616303389",
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abstract = "Among the machining operations, Electrical discharge
machining (EDM) process is widely used in production
industries because of its ability to machine the
materials of any hardness. However, the machining of
advanced materials including ceramics, composites, and
super-alloys requiring the precise surface finish and
dimensional accuracy also increases the energy
consumption and cost simultaneously. As such, both
environmental and economic performances are
compromised. Also, EDM process is itself considered
hazardous because of the large toxic liquid and solid
wastes and gases produced due to reaction products
developed from highly energized dielectric media placed
between tool and workpiece. Thus, an appropriate
balance between manufacturing and environmental aspects
is highly desirable for ensuring higher productivity
and environmental sustainability of the process. In
this context, the present work proposes two variants of
optimization approach of genetic programming (GP) in
modelling the multi-response characteristics, i.e. two
environmental aspects (thermal energy consumption and
dielectric consumption) and one manufacturing aspect
(relative tool to wear ratio) of the EDM process. These
variants are proposed by introducing two model
selection criteria from statistical learning theory to
be used as fitness functions in the framework of GP.
The performance of the proposed GP models is evaluated
against the experimental data based on five statistical
error metrics and the two hypothesis tests. Further,
the relationships between manufacturing, environmental
aspects and the input process parameters are unveiled,
which can be used by industry users to optimize the
process economically and environmentally. It was found
that the input peak current has the highest impact on
the environmental aspects of the EDM process.",
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keywords = "genetic algorithms, genetic programming, Electrical
discharge machining (EDM), Machining, Environmental,
Energy consumption, Relative tool to wear ratio",
- }
Genetic Programming entries for
Akhil Garg
Jasmine Siu Lee Lam
Liang Gao
Citations