Prediction of energy consumption of machine tools using multi-gene genetic programming
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- @Article{PAWANR:2022:MTP,
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author = "Shailendra Pawanr and Girish Kant Garg and
Srikanta Routroy",
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title = "Prediction of energy consumption of machine tools
using multi-gene genetic programming",
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journal = "Materials Today: Proceedings",
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year = "2022",
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ISSN = "2214-7853",
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DOI = "doi:10.1016/j.matpr.2022.01.156",
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URL = "https://www.sciencedirect.com/science/article/pii/S2214785322001833",
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keywords = "genetic algorithms, genetic programming, Energy
consumption, Energy modelling, Sustainable machining,
Artificial intelligence, MGGP",
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abstract = "In the past, researchers have applied different
analytical, numerical, and empirical modelling
techniques to analyze energy consumption. In the
present study, computational artificial
intelligence-based Multi-Gene Genetic Programming is
used to model the energy consumption of machine tool.
The experiments were performed on a heavy-duty HMT
lathe machine tool under a dry environment in the
interest of sustainable machining. The Taguchi full
factorial orthogonal array L27 was used to develop the
experimental plan. The power consumption of the machine
tool was measured using a Fluke 435 power analyzer. The
dataset was split into training and testing data based
on the 80-20 ratio. Further, 99.77percent goodness of
fit was achieved in training and 98.60percent for
testing the model. The adequacy of the model was tested
by determining four error indices i.e. root means
square error, mean absolute error, sum of squared
error, and mean square error. The model is validated by
conducting two hypothesis tests, t-test and f-test on
predicted data. The hypothesis results confirm the
model's goodness of fit statistically, indicating that
the proposed model can be easily applied in the
manufacturing industry to predict energy consumption",
- }
Genetic Programming entries for
Shailendra Pawanr
Girish Kant Garg
Srikanta Routroy
Citations