Open AccessInfluence of synthetically generated inclusions on the stress accumulation and concentration in X65 pipeline steel
Author(s) -
Niklas Fehlemann,
Yannik Sparrer,
Felix Pütz,
Markus Könemann,
Sebastian Münstermann
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1157/1/012056
Subject(s) - pipeline (software) , materials science , generator (circuit theory) , microstructure , stress (linguistics) , process (computing) , log normal distribution , biological system , computer science , inclusion (mineral) , composite material , mathematics , geology , mineralogy , thermodynamics , statistics , power (physics) , linguistics , physics , philosophy , biology , programming language , operating system
The use of a simulative approach with representative volume elements (RVE’s) is particularly well suited to investigate the influence of different microstructural parameters on the damage behavior of a material. In order to statistically analyze the individual components of the microstructure (e.g. geometric structure of grains and inclusions), well-known distribution functions such as logNormal/Gamma are normally used, but these do not take into account the interdependencies between the different parameters. However, newer approaches like machine learning techniques can only describe one phase of a single material at a time. Therefore, in this study, we extended an existing Wasserstein Generative Adversarial Network (WGAN) to a Conditional Wasserstein GAN with Gradient Penalty (CWGAN-GP), with which it is possible to process multiple materials/phases simultaneously. Training this algorithm on different steels and associated inclusions showed that a single trained network can generate synthetic microstructure for all different phases and materials with very high quality. A newly implemented evaluation method using the regularized Wasserstein-distance confirmed the excellent agreement of the real data with the synthetic data for all phases/materials. As a use case for our algorithm, the influence of different inclusions on the stress accumulation and concentration of X65 pipeline steel was investigated to find initiation sites for damage in the material. These investigations showed a pronounced correlation between stress concentration and inclusion parameters, thus confirming the usefulness of the CWGAN-GP as an input-generator for RVE’s.