Szczegóły publikacji
Opis bibliograficzny
Fracture modeling in dual-phase steel grades based on the random cellular automata finite element approach / Konrad PERZYŃSKI, Łukasz MADEJ // Simulation : Transactions of the Society for Modeling and Simulation International ; ISSN 0037-5497. — 2016 — vol. 92 iss. 2 special issue: Simulation with Cellular Automata, s. 195–207. — Bibliogr. s. 206, Abstr. — Publikacja dostępna online od: 2015-12-30
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 96161 |
|---|---|
| Data dodania do BaDAP | 2016-02-16 |
| DOI | 10.1177/0037549715622280 |
| Rok publikacji | 2016 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Czasopismo/seria | Simulation - Transactions of the Society for Modeling and Simulation International |
Abstract
The development of a parallel version of the fracture model dedicated for multi-phase materials based on a combination of the finite element model and random cellular automata approach is the overall goal of this study. Dual-phase (DP) steel, commonly used in the automotive industry, is selected as a case study for the present investigation. Firstly, various fracture modes that can occur during deformation in DP steel grade microstructures are presented from an experimental point of view. To consider explicitly microstructure features that play a significant role during initiation and subsequent failure propagation, the digital material representation concept is used. Then, details of the developed random cellular automata model, fully embedded within the finite element framework, are discussed. The cellular automata space definition, internal variables, state variables and transition rules replicating investigated fracture modes are presented in detail and discussed. The concept of data transfer and parallelization based on the Message Passing Interface methodology in such an innovative hybrid numerical model is also clearly presented. The final section of the paper is devoted to examples of obtained results highlighting model predictive capabilities.