A neural-network approach to describe the scatter of cyclic stress–strain curves

In order to predict a product’s durability in the early phases of development it is necessary to know the stress–strain behaviour of the material, its resistance to fatigue and the loading states in the material. These parameters, however, tend to exhibit a considerable degree of uncertainty. Due to a lack of knowledge of the actual circumstances in which the product is used, during the early development phase, simulations based on statistical methods are used. The results of the experiments show that the cyclic stress–strain curves demonstrate not only a large amount of scatter, but also a dependence on the temperature, the size of the cross-section, the content of alloying elements, the loading rate, etc.