@Article{cmes.2020.09645,
AUTHOR = {N. Ganesh, R. K. Ghadai, A. K. Bhoi, K. Kalita, Xiao-Zhi Gao},
TITLE = {An Intelligent Predictive Model-Based Multi-Response Optimization of EDM Process},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {124},
YEAR = {2020},
NUMBER = {2},
PAGES = {459--476},
URL = {http://www.techscience.com/CMES/v124n2/39532},
ISSN = {1526-1506},
ABSTRACT = {Electrical Discharge Machining (EDM) is a popular non-traditional
machining process that is widely used due to its ability to machine hard and brittle
materials. It does not require a cutting tool and can machine complex geometries
easily. However, it suffers from drawbacks like a poor rate of machining and
excessive tool wear. In this research, an attempt is made to address these issues
by using an intelligent predictive model coupled global optimization approach
to predict suitable combinations of input parameters (current, pulse on-time and
pulse off-time) that would effectively increase the material removal rate and minimize the tool wear. The predictive models, which are based on the symbolic
regression approach exploit the machine intelligence of Genetic Programming
(GP). As compared to traditional polynomial response surface (PRS) predictive
models, the GP predictive models show compactness as well as better prediction
capability. The developed GP predictive models are deployed in conjunction with
NSGA-II to predict Pareto optimal solutions.},
DOI = {10.32604/cmes.2020.09645}
}