COMPUTER METHODS IN MATERIALS SCIENCE (CN-cmms)

Permanent URI for this communityhttps://repo.agh.edu.pl/handle/AGH/102744

Adres wydawniczy: Kraków : Akapit, 2006- . Od vol. 20, no. 3 (2020): Wydawnictwa AGH.
Strona WWW: https://www.cmms.agh.edu.pl/
ISSN: 2720-4081 eISSN: 2720-3948
(Poprzedni ISSN: 1641-8581)
DOI: https://doi.org/10.7494/cmms
Poprzedni tytuł: Informatyka w Technologii Materiałów (2001-2005)

Computer Methods in Materials Science povides an international medium for the publication of studies related to various aspects of applications of computer methods in the broad area of materials science.

New! Aktualny numer: 2026 - Vol. 26 - No. 1

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Access status: Open Access ,
Geometric optimization of two-stage stamping dies for ultra-thin titanium bipolar plates using Sequential Physics-Informed Neural Networks
(Wydawnictwa AGH, 2026) Ke, Zijie; Huang, Yiwen; Guo, Ziqiang; Xiao, Yao; Hou, Zeran; Min, Junying
Bipolar plates are critical core components in proton exchange membrane fuel cells (PEMFCs). Titanium-based materials are highly favored due to their excellent corrosion resistance and high specific strength. However, the plates often experience severe local thinning and poor consistency in forming dimensions during the two-stage stamping process. Although traditional finite element method (FEM) optimization can mitigate these defects, it comes with high computational costs and time consumption. This study proposes a die design optimization framework based on the Sequential Physics-Informed Neural Network (S-PINN). Unlike traditional single-layer neural network models, S-PINN adopts a sequential architecture that effectively maps the two-stage forming process of the plates. This architecture can explicitly predict the evolution of forming quality from the pre-forming stage to the final stage. By embedding the core physical laws of plastic deformation into the network loss function, the S-PINN model effectively predicts the complex nonlinear relationship between mold geometry and forming quality, while ensuring physical consistency. Experimental and simulation results show that the S-PINN model’s prediction accuracy for dimensional consistency (DC) is 73.8% higher than that of the PINN model and 33.9% higher than that of the S-ANN model. Compared with traditional modeling methods, the S-PINN-optimized die design can reduce the thinning rate and improve channel dimensional consistency.
Computer Methods in Materials Science
2026 - Vol. 26 - No. 1
Access status: Open Access ,
A Digital Twin for temperature prediction in the laser hardening process of NC10 steel
(Wydawnictwa AGH, 2026) Lacki, Piotr; Derlatka, Anna; Lacki, Michał; Lachs, Kuba
In this study, Artificial Neural Networks (ANN) were created to develop a Digital Twin (DT) for temperature prediction in the laser hardening process of NC10 steel. The ANN were trained to predict temperature on the top layer during the laser hardening process of NC10 steel samples with different thicknesses and with various laser power and laser scanning speeds. The prediction developed during the project work was based on a parametric numerical model of the laser hardening process for a sample of NC10 steel, using the Finite Element Method (FEM) within the ADINA software. Numerical simulations enabled a detailed analysis of the temperature produced on the surface of each sample, as well as a visualization of the structural changes made to the sample according to the laser hardening process. It is crucial to create data that reflects reality as closely as possible to assess the best setting for each process. A well created DT allows to make automatically important changes along laser hardening process. To obtain a set of the most efficient parameters for the desired result, Genetic Algorithms (GA) were integrated with the developed ANN. As a result, the authors developed an effective and efficient tool to predict the temperature produced along the laser hardening process.
Computer Methods in Materials Science
Vol. 26 (2026)
Access status: Open Access ,
Artificial intelligence-enhanced algebraic multigrid for 3D finite element simulations
(Wydawnictwa AGH, 2026) Goik, Damian; Banaś, Krzysztof
The paper presents preliminary investigations into a strategy for solving linear systems resulting from 3D finite element simulations, based on the algebraic multigrid (AMG) method, enhanced using artificial intelligence techniques. In particular, we adapt to 3D problems the algorithm presented in Luz et al. (2020) for using a graph neural network to create the prolongation and restriction operators in a way that will improve convergence. The process of training the network proceeds on the basis of a set of system matrices obtainedfor tasks much smaller in scale than the target problem to be solved. Learning is aimed at decreasing the spectral radius of the error propagation matrix after applying modified prolongation and restriction. We describe some implementation details of the solver developed based on the presented strategy and show several numerical examples of its application for medium-sized problems.
Computer Methods in Materials Science
2025 - Vol. 25 - No. 4
Access status: Open Access ,
Full-field approaches for austenite-ferrite phase transformation simulations
(Wydawnictwa AGH, 2025) Wermiński, Mariusz; Sitko, Mateusz; Madej, Łukasz
Understanding the local evolution of phase transformations in steels, particularly the γ (austenite) → α (ferrite) transformation, is crucial for controlling the microstructure and properties of steel components. Over recent decades, significant progress has been made in the numerical modeling of this complex phenomenon. This development has been driven by both scientific curiosity and industrial needs, especially in processes such as hot rolling, forging, thermal treatment, etc. The developed models have evolved from simple solutions based on local equilibrium to more complex approaches that consider local heterogeneities. Modern computational approaches, such as Phase-Field (PF), Level-Set (LS), Cellular Automata (CA), Monte Carlo (MC) or Vertex based simulations, allow for the precise reproduction of microstructural evolution considering local instabilities. They also enable the analysis of phase boundary motion in an explicit manner. These techniques also allow for direct integration with thermodynamic data and mechanical models, thereby better capturing the physical mechanisms of phase transformations, such as chemical composition, diffusion resistance, or the influence of deformation. An overview of the state of the art in this area is presented within the paper. The model’s concepts, assumptions, fundamental equations, advantages, limitations, and potential practical applications are summarized. Special attention is given to modeling the γ → α transformation by the Cellular Automata method. The importance of incorporating phenomena such as diffusion, nucleation, and growth is emphasized. The need for consistency between experimental results and simulations is also highlighted.
Access status: Open Access ,
High-fidelity modeling of interface crossing in the diffusion welding process at the polycrystalline scale
(Wydawnictwa AGH, 2025) Godinot, Camille; Rigal, Emmanuel; Bernard, Frédéric; Emonot, Philippe; Frayssines, Pierre-Eric; Védie, Luc; Bernacki, Marc
Controlling the microstructure of a diffusion welded interface is a critical point to ensure optimum mechanical properties and the homogeneity of the joint. Beyond the intimate contact formation between bonded parts studied in the literature, this article focuses on the grain boundary crossing of the interface during this process and its measurement. Following this perspective, a level-set method has been used for full-field microstructure simulations in 2D with various interface parameters. Two crossing measurement models have been formulated, tested and discussed over the simulations.
Access status: Open Access ,
Polycrystalline plasticity analysis of cyclic loading and stress relaxation in 316H austenitic stainless steel
(Wydawnictwa AGH, 2025) Acar, Sadik Sefa; Yalçinkaya, Tuncay
The mechanical behavior of 316H austenitic stainless steel is investigated in this study under cyclic strain-controlled loading with and without hold periods at elevated temperatures. Understanding the low-cycle fatigue (LCF) and fatigue-creep interaction (FCI) characteristics is essential for ensuring the structural performance and safety of reactor components, particularly under conditions typical of modular and generation IV reactors. The new generation of nuclear power plants require more resistant and durable materials as the operating environments impose significantly higher demands, including increased neutron irradiation levels and elevated operating temperatures, leading to accelerated material degradation. A combined isotropic-kinematic hardening model within a crystal plasticity framework is employed to capture the cyclic and time-dependent mechanical response of the material. Model parameters are calibrated by fitting cyclic loading simulation results to experimental data at 550°C using polycrystalline representative volume elements (RVE). Strain-controlled uniaxial loading simulations are performed to analyze peak stress evolution throughout cyclic loading and stress relaxation behavior during strain-hold periods. The RVE simulation results are in strong agreement with experiments under LCF loading. For the loading with strain-holds, stress relaxation during hold periods exhibits two distinct stages: an initial rapid decay followed by a steady decline, both of which are captured in simulations. Beyond the macroscopic response, analyses reveal the heterogeneous evolution of field variables at the microstructural level, as strain hardening during loading and stress relaxation during hold periods varied across grains due to their crystal orientations and interactions with neighboring grains. These findings enhance the understanding of high-temperature mechanical behavior at both macroscopic and microstructural scales, contributing to the efforts for the design, operation, and life extension of nuclear reactor components.
Computer Methods in Materials Science
2025 - Vol. 25 - No. 3

COMPUTER METHODS IN MATERIALS SCIENCE (CN-cmms)

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