Nowadays, machine learning is used in almost all areas of research and thereby heavily changes the approach to many existing problems. Machine learning therefore has a high potential to create new innovations. This is also true for material science in general and especially for forming processes. As experts in characterizing materials, material modeling and numerical simulation of forming processes, the experts at the Fraunhofer IWM investigate how machine learning can be applied to solve problems concerning materials sciences in the forming industry. We are active in the following areas:
The aim is to identify material model parameters by fitting the material model response to experimental measurements. Machine learning enables setting up a transfer function to directly map experimental measurements on model parameters.
(see Fig. 1.1)
The successfully trained machine learning model directly estimates material model parameters. In contrast to optimization methods, no iterative (and time consuming) procedure is necessary.
It is possible to apply the machine learning model on different materials tested as well as to identify model parameters for which the machine learning model is trained within the same experimental setup (see Fig. 1.2).
Sufficient sampling in parameter space leads to a greater likelihood offinding a global solution for the problem of parameter identification. In contrast to optimizers, the machine learning model does not tend to get stuck in local optima, especially when the problem is ambiguous.