2021/08/03 by Gabriel Kronberger, Evgeniya Kabliman, Kronberger, Gabriel +5 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · #68T05 #74-04 #74-05 #74-10 #74C99 #Evolutionary Algorithms and Applications #FOS: Computer and information sciences #Metallurgy and Material Forming #Neural and Evolutionary Computing (cs.NE) #Viral Infectious Diseases and Gene Expression in Insects #cs.NE #msc:68T05 #msc:74-04 #msc:74-05 #msc:74-10 #msc:74C99
paper · pdf · doi:10.48550/arxiv.2108.01595
Preprint submitted to Applications in Engineering Sciences
openalex publication_date 2021/08/03 · arxiv created 2021/11/19 · arxiv updated 2021/11/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.