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International Journal of Automotive Technology 2019;20(S): 57-66. |
| MINIMIZATION OF SURFACE DEFLECTION IN RECTANGULAR
EMBOSSING USING AUTOMATIC TRAINING OF ARTIFICIAL NEURAL
NETWORK AND GENETIC ALGORITHM |
| Sungmin Cho, Wanjin Chung |
| Seoul National University of Science and Technology |
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| ABSTRACT |
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Surface deflection is a phenomenon that causes fine wrinkles on the outer surfaces of sheet metal and
deteriorates product external appearance. It is quantitatively defined as the difference between the section curve of the sheet
and the ideal curve. In this study, using neural networks, a prediction model for surface deflection according to material
properties was constructed and combined with a genetic algorithm; the combination of the material properties was studied to
predict the minimum surface deflection. Because of the limited number of simulation data, neural networks were developed
using several sampling methods such as central composite design, Latin hypercube sampling, and random sampling. In the
training of the neural networks, the optimal hyper-parameter of the neural network was found automatically using Latin
hypercube sampling. In conclusion, for prediction of surface deflection in rectangular embossing, neural networks made by
central composite design showed the best performance. In addition, it was confirmed that the procedure of combining
automatic training of a neural network and the genetic algorithm accurately predicted the set of material properties that
generates the minimum surface deflection. Also, the quantity of surface deflection predicted by the neural network was very
close to that predicted by finite element analysis. |
| Key Words:
Surface deflection, Artificial neural network, Finite element analysis, Data sampling, Genetic algorithm |
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Corresponding Author.
Wanjin Chung , Email. 
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