Genetic programming approach to predict the performance characteristics of WEDM taper cutting process
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @Article{NAYAK:2022:matpr,
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author = "Bijaya Bijeta Nayak and Nitish Kumar and
Abhishek Singh and Sasmita Sahu and Shiv Sankar Das and
Debashree Debadatta Behera and Rita Kumari Sahu",
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title = "Genetic programming approach to predict the
performance characteristics of {WEDM} taper cutting
process",
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journal = "Materials Today: Proceedings",
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volume = "62",
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pages = "4504--4508",
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year = "2022",
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note = "International Conference on Materials, Processing \&
Characterization (13th ICMPC)",
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ISSN = "2214-7853",
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DOI = "doi:10.1016/j.matpr.2022.04.948",
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URL = "https://www.sciencedirect.com/science/article/pii/S2214785322031522",
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keywords = "genetic algorithms, genetic programming, Genetic
programming (GP), WEDM, Taper cutting, Angular error,
Prediction model",
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abstract = "One of the unique applications of the wire electrical
discharge machining (WEDM) method is taper cutting
process. This process is ideal for numerous intricate,
difficult to machine materials with complex profiles,
deep slots with tight corners and features at different
angles, those are basically used in defense and
aerospace applications. According to the literature,
numerous mathematical models based on physics and data
may be created to identify the optimum parameter
settings throughout the taper cutting process. However,
it is a challenging process since it necessitates
extensive understanding of how to do tapering
operations with WEDM. As a result, a genetic
programming (GP) model is created in this study to
anticipate the angle inaccuracy during WEDM taper
cutting. Six process parameters including workpiece
thickness, taper angle, pulse duration, discharge
current, wire speed and wire tension were considered at
three level each for minimizing the angular error and
surface roughness during WEDM taper cutting process.
The L27 orthogonal array of Taguchi's design of
experiment is used to collect data on the procedure.
Three assessing criteria are used to assess the
correctness of the suggested model: root mean square
error (RSME), mean absolute percentage error (MAPE) and
co-efficient of determination (R2). In compared to
typical prediction approaches, the results suggest that
MGGP is a successful strategy. The model is cost
effective and time saving way to investigate angular
error and surface roughness in taper cutting before
performing the actual machining process",
- }
Genetic Programming entries for
Bijaya Bijeta Nayak
Nitish Kumar
Abhishek Singh
Sasmita Sahu
Shiv Sankar Das
Debashree Debadatta Behera
Rita Kumari Sahu
Citations