Intelligent Iterative Experimental Design to Achieve Maximum Model Quality for Phase Change of 22MnB5

Abstract

Conducting experiments for material modeling is very costly and time-consuming when many parameters are involved, resulting in a large number of test conditions. Therefore, it is expedient to develop algorithms for the iterative identification of optimal test conditions. This method should allow the model to learn automatically so that only a small number of test conditions are selected at the beginning of the model calibration. In order to decide whether further experiments should be carried out and which test conditions need to be investigated, meta-models are generated, and the expected gain score is calculated. The next sample is selected based on the highest score, and this procedure continues until the material models meet a termination criteria. The result from the study shows that the implemented method uses 12 test conditions to generate a phase transformation model for 22MnB5 steel. The material models fitted with the proposed method provide acceptable predictions when compared with experimental data.