In material science, models are derived to predict emergent material properties (e.g. elasticity, strength,conductivity) and their relations to processing conditions. A major drawback is the calibration of modelparameters that depend on processing conditions. Currently, these parameters must be optimized to fitmeasured data since their relations to processing conditions (e.g. deformation temperature, strain rate) arenot fully understood. We present a new approach that identifies the functional dependency of calibrationparameters from processing conditions based on genetic programming. We propose two (explicitandimplicit)methods to identify these dependencies and generate short interpretable expressions. The approach is used toextend a physics-based constitutive model for deformation processes. This constitutive model operates withinternal material variables such as a dislocation density and contains a number of parameters, among themthree calibration parameters. The derived expressions extend the constitutive model and replace the calibrationparameters. Thus, interpolation between various processing parameters is enabled. Our results show that theimplicit method is computationally more expensive than the explicit approach but also produces significantlybetter results.     «

In material science, models are derived to predict emergent material properties (e.g. elasticity, strength,conductivity) and their relations to processing conditions. A major drawback is the calibration of modelparameters that depend on processing conditions. Currently, these parameters must be optimized to fitmeasured data since their relations to processing conditions (e.g. deformation temperature, strain rate) arenot fully understood. We present a new approach that identifies the functional d...     »