Optimization of Expanding Velocity for a High-speed Tube Expander using a Genetic Algorithm with a Neural Network

W.-J. Chung, J.-L. Kim, T.-J. Song, K.-J. Kim, and C.-M. Han (Korea)


Genetic Algorithm, Neural Network, Optimization, TubeExpander, Plastic Deformation


This paper presents the optimization of expanding velocity for tube expanding process in the manufacturing of a heat exchanger. In specific, the expanding velocity has a great influence on the performance of a heat exchanger because it is a key variable determining the quantity of tube expanding at assembly stage as well as a key parameter determining overall production rate. Accordingly, the heat exchanger with good performance can be manufactured by both modeling mathematically the profile of expanding velocity and optimizing the parameters of tube expanding by using a genetic algorithm with a neural network. The simulation showed that the genetic algorithm used in this paper resulted in the optimal profile of tube expanding velocity by performing the following series of iteration; the generation of arbitrary population for tube expanding parameters, consequently the generation of tube expanding profiles and the evaluation of process time, the evaluation of tube expanding quantity using the pre trained data of plastic deformation by means of a neural network, and finally the generation of next population using a penalty function and a Roulette wheel method.

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