Extending a Physics-Based Constitutive Model using Genetic Programming
This work addresses a bottleneck in material science for researchers and engineers by enabling interpolation between processing parameters, though it is incremental as it extends existing models with new methods.
The paper tackled the problem of calibrating parameters in physics-based constitutive models for material science by using genetic programming to identify functional dependencies from processing conditions, resulting in an implicit method that produced significantly better results despite higher computational cost.
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 model parameters that depend on processing conditions. Currently, these parameters must be optimized to fit measured data since their relations to processing conditions (e.g. deformation temperature, strain rate) are not fully understood. We present a new approach that identifies the functional dependency of calibration parameters from processing conditions based on genetic programming. We propose two (explicit and implicit) methods to identify these dependencies and generate short interpretable expressions. The approach is used to extend a physics-based constitutive model for deformation processes. This constitutive model operates with internal material variables such as a dislocation density and contains a number of parameters, among them three calibration parameters. The derived expressions extend the constitutive model and replace the calibration parameters. Thus, interpolation between various processing parameters is enabled. Our results show that the implicit method is computationally more expensive than the explicit approach but also produces significantly better results.