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Computer Aided Simulation in Metallurgy and Material Engineering

Submission Deadline: 31 October 2024 (closed) View: 493

Guest Editors

Prof. Radim Kocich, VŠB – Technical University of Ostrava, Czech Republic
Assoc. Prof. Lenka Kunčická, VŠB – Technical University of Ostrava, Czech Republic

Summary

Incorporation of computer aided simulations to research and development has introduced indisputable benefits to numerous fields of industry and commerce. Their application in metallurgy and materials engineering has brought numerous advantages and highly effective solutions for sustainable development. Nevertheless, the relevance and accuracy of the acquired predictions are only defined by the robustness and reliability of the input data and defined initial and boundary conditions. The reliability of the used data, and thus of the predicted results, is ensured by conducting properly designed experiments, the results of which are applied to calculate rheology models and define material behaviours during processing. Besides the highly favoured finite element method, suitable to solve numerous tasks, the popularity of neural networks, enabling to generate self-learning algorithms, has recently been on the rise; this trend shifted the possibility of application of computer modelling within the metallurgy and materials engineering research to an entirely new level.  


Keywords

FEM, Rheological law, plastic deformation, metal casting

Published Papers


  • Open Access

    ARTICLE

    Numerically and Experimentally Establishing Rheology Law for AISI 1045 Steel Based on Uniaxial Hot Compression Tests

    Josef Walek, Petr Lichý
    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 3135-3153, 2025, DOI:10.32604/cmes.2025.059889
    (This article belongs to the Special Issue: Computer Aided Simulation in Metallurgy and Material Engineering)
    Abstract Plastometric experiments, supplemented with numerical simulations using the finite element method (FEM), can be advantageously used to characterize the deformation behavior of metallic materials. The accuracy of such simulations predicting deformation behaviors of materials is, however, primarily affected by the applied rheology law. The presented study focuses on the characterization of the deformation behavior of AISI 1045 type carbon steel, widely used e.g., in automotive and power engineering, under extreme conditions (i.e., high temperatures, strain rates). The study consists of two main parts: experimentally analyzing the flow stress development of the steel under different thermomechanical… More >

  • Open Access

    ARTICLE

    Finite Element Modeling of Thermo-Viscoelastoplastic Behavior of Dievar Alloy under Hot Rotary Swaging

    Josef Izák, Marek Benč, Petr Opěla
    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 3115-3133, 2025, DOI:10.32604/cmes.2025.059234
    (This article belongs to the Special Issue: Computer Aided Simulation in Metallurgy and Material Engineering)
    Abstract The paper deals with the FEM (Finite Element Method) simulation of rotary swaging of Dievar alloy produced by additive manufacturing technology Selective Laser Melting and conventional process. Swaging was performed at a temperature of 900°C. True flow stress-strain curves were determined for 600°C–900°C and used to construct a Hensel-Spittel model for FEM simulation. The process parameters, i.e., stress, temperature, imposed strain, and force, were investigation during the rotary swaging process. Firstly, the stresses induced during rotary swaging and the resistance of the material to deformation were investigated. The amount and distribution of imposed strain in… More >

  • Open Access

    ARTICLE

    Machine Learning Techniques in Predicting Hot Deformation Behavior of Metallic Materials

    Petr Opěla, Josef Walek, Jaromír Kopeček
    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 713-732, 2025, DOI:10.32604/cmes.2024.055219
    (This article belongs to the Special Issue: Computer Aided Simulation in Metallurgy and Material Engineering)
    Abstract In engineering practice, it is often necessary to determine functional relationships between dependent and independent variables. These relationships can be highly nonlinear, and classical regression approaches cannot always provide sufficiently reliable solutions. Nevertheless, Machine Learning (ML) techniques, which offer advanced regression tools to address complicated engineering issues, have been developed and widely explored. This study investigates the selected ML techniques to evaluate their suitability for application in the hot deformation behavior of metallic materials. The ML-based regression methods of Artificial Neural Networks (ANNs), Support Vector Machine (SVM), Decision Tree Regression (DTR), and Gaussian Process Regression More >

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