We used SimCentral26 simulation software. Robustness is important when the results are obtained by inputting random data into the process simulator. With SimCentral, we receive an error flag and can discard the results when the solution is divergent and cannot be obtained. In an iterative process, a sequential simulator takes a long time to converge; but a convergent solution can be obtained in a short time with SimCcntral. In addition, X can be set as needed in ADoE because the specifications can be freely changed. Furthermore, SimCentral can be performed using JavaScript and can be automated by association with Python programs, which means that the proposed method can be easily combined with SimCentral.
We succeeded in searching for X that achieved target ranges of Y using 139 simulations (D-optimal design required 50, and Bayesian optimization required 89). As a comparison method, X candidates were selected in a random search. In this case, the target ranges of the Y could not be achieved at all. Table 3 shows the result of 139 simulations, and Figure 4 shows the behavior of Y for 139 simulations. In Bayesian optimization, the number of samples within the target range of Y increased with the number of trials. The accuracy of the GP model improved as the number of samples increased with the number of trials. The Y8result, which has a narrow range, has a low probability and is not considered by P all. Therefore, it was possible to treat each variable equally by performing range scaling, as shown in Eq (13).
Table 3. The results of Y that achieved targets