Abstract—This study combined the Taguchi method with the
genetic algorithm (GA) to analyse the optimal design
parameters of the thermal distribution in an air-core linear
brushless permanent magnet motor (ALBPMM). First, this
study adopted an L18(21×37) orthogonal array to determine the
significant factors, including active currents, the length of
magnets, pole distance of magnets, air-gap length, and thickness
and width of coils. Then, the study uses response surface
methodology (RSM) to construct the predictive model. Finally,
the optimal combinations of design parameters that involve
using real-code GA were obtained and verified by finite element
modelling. The simulation results show that the thermal
distribution in the optimal design of parameters is 41% more
effective than that of any models in which the parameters are
not optimised. Therefore, the proposed approach can be used as
the basis for designing and predicting the temperature effects of
the ALBPMM..
Index Terms—Taguchi, genetic algorithm, air-core linear
brushless permanent magnet motor, response surface
methodology.
The authors are with the Department of Industrial Education and
Technology, National Changhua University of Education, Taiwan (e-mail:
wjong@cc.ncue.edu.tw)
[PDF]
Cite: W. J. Chen, J. R. Lin, D. C. Chen, and F. L. Nian, "Optimisation Design of a Taguchi-Based Real-Code
Genetic Algorithm for Thermal Reducing of Air-Core
Linear Brushless Permanent Magnet Motor," International Journal of Modeling and Optimization vol. 4, no. 5, pp. 402-409, 2014.
