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Optimising tension levelling process by means of genetic algorithms and finite element method

Abstract This paper presents a method based on genetic algorithms and the finite element method which is useful for automatically adjusting the parameters of a tension levelling process. First, the optimum parameters of the steel to be used in the simulation programme are sought. The process consists of simulating controlled cyclical deformation tests on finite element (FE) models of standard steel test pieces with different laws of cyclical behaviour. Genetic algorithms are used to optimise the parameters of the simulation model so that the behaviour of the material is as close as possible to the results obtained in real experimental tests. This ensures that the behaviour of the material in the FE model is as realistic as possible. The model of behaviour of the material selected is used to design and check out a second tension levelling FE model. Based on this second model, the roll penetration, the lengthwise tension and the strip feedrate are adjusted. There is also optimisation with genetic algorithms so that the final residual tensions in the product are below a specified threshold and as even as possible. For a solution to be considered as valid, it must be confirmed that the steel plate is subject to the tensions envisaged at various process control points. From the best solutions found, the one with the fastest feedrate is selected so as to maximise output.