## 7 Discussion  
In this work, an acceleration test with an hourglass specimen and DIC measurement is presented to calibrate the material model. The material model consists of three parts: Chaboche model, Perzyna model, and the Norton model. Each of the three parts captures a different component of the material behaviour, and the parameters of these models can be determined separately based on analytical relationships and selected experiments. This methodology is presented in Section 4.3, and the parameters found are used as an initial for further calibration. In practice, such a material model is then used for FEM simulations in various commercial software. In the case of FE models, the so-called FEMU approach is then used for identification, which was used in this article to \"tune\" the parameter values and is briefly described in Section 5. The solution results, see Section 6, show that the second calibration using the FEMU approach significantly improves the quality of the solution, which is expressed by the value of the objective function (or graphs). Several problem areas were also identified during the solution. These were not analysed in more detail in the article, as the goal was to test the basic procedure. Hourglass samples seem to generate slightly different results compared to classic samples; the problem is shown in Section 3.2.3. Different stress states (multi-axial stress states in the hourglass sample) or slightly different manufacturing technologies can be a problem here. However, similar problems can also arise in practice. The FEMU approach does not capture the standard physical meaning of the parameters (for example, [PERSON]'s modulus \(E\), the value of the yield strength \(\sigma_{y}\)) but rather the overall effect of the parameter on the given curve and approaches the material model as a black box. When using the FEMU approach, the value corresponding to the physical meaning of the parameter may deviate from this interpretation. The problem is shown in Section 6. We can remove such parameters from FEMU identification or significantly strengthen the influence of the parameter on the resulting objective function value. However, both steps can lead to a decrease in the quality of the resulting estimate. Another problem is determining the weights of individual experiments when calculating the value of the total objective function. Tensile tests and creep tests were used for identification. In creep tests, a large part of the error value is related to only two parameters in the Norton model. The parameter values of the Perzyna model will affect the beginning of the curve in creep tests (primary creep) and also the tensile curve at different speeds. For the Chaboche model, there will be a predominant influence in tensile tests. In this paper, creep and tensile tests were given equal weight. Through a visual comparison of the results, see Figures 16 and 17, we came to the opinion that the creep tests show a better agreement with the experiment. There is a question as to whether the number of parameters tied to it should not be taken into account when designing weights for individual experiments. With repetitive FEM calculations, the question arises how to reduce the required computation time? The total computational time required to tune the material model used has not been accurately measured but is estimated to take seven days. When the number of elements is reduced, significant time savings can be achieved, but the accuracy may be affected. This effect has been investigated and can be expressed using Figure 18.  
From Figure 18a, it can be concluded that the size of the element does not have a significant effect on the precision of the tensile test results. All three curves overlap. From Figure 18b, it can be seen that the effect of the size of the element on the accuracy of the prediction of the creep test is very small. Thus, in both cases, an element size of about 3 mm was used to minimise computational time.