Topic Editors

Department of Engineering Materials and Biomaterials, Silesian University of Technology, Gliwice 44-100, Poland
Department of Engineering Materials and Biomaterials, Silesian University of Technology, Gliwice 44-100, Poland
John von Neumann Faculty of Informatics, Obuda University, H-1034 Budapest, Bécsi út 96/B, Hungary

Hybrid Computational Methods in Materials Engineering

Abstract submission deadline
31 August 2023
Manuscript submission deadline
30 November 2023
Viewed by
3605

Topic Information

Dear Colleagues,

In recent years, there has been a dynamic development of methods and tools enabling the modeling and simulation of technological processes of manufacturing, processing and shaping the structure and properties of engineering materials such as steel and metal alloys, composites, plastics, and ceramics. The use of computational methods in engineering is related, inter alia, to the optimization of material production technology to achieve the desired material properties. This requires establishing the relationship between process parameters, material structure, and properties, and is often based on the analysis of existing experimental data sets. Considering the enormous potential and undoubted benefits of using computational methods in materials engineering, there are more and more examples of their combination in the so-called hybrid methods. These examples concern the connection of, for example, neural networks and evolutionary algorithms, the finite element method and the cellular automaton method, statistical methods and neural networks, or the finite element method and neural networks. Such combined hybrid methods are mainly used to analyze phenomena where their full physical description is not possible. This Topic is dedicated to the application of hybrid computational methods in the engineering of metallic and other materials, including statistical methods, artificial intelligence methods, biologically inspired methods, finite element/boundary/volume methods, data mining, machine learning, cellular automata and image analysis.

Prof. Dr. Wojciech Sitek
Prof. Dr. Jacek Trzaska
Prof. Dr. Imre Felde
Topic Editors

Keywords

  • engineering materials
  • alloys
  • metals
  • steels
  • composites
  • plastics
  • ceramics
  • material properties
  • microstructure
  • hybrid computational methods
  • artificial intelligence methods
  • mathematical modeling
  • computer simulation
  • data-driven modeling
  • machine learning

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
AI
ai
- - 2020 25.1 Days 1200 CHF Submit
Alloys
alloys
- - 2022 15.0 days * 1000 CHF Submit
Applied Sciences
applsci
2.838 3.7 2011 14.9 Days 2300 CHF Submit
Materials
materials
3.748 4.7 2008 13.9 Days 2300 CHF Submit
Metals
metals
2.695 3.8 2011 16.9 Days 2000 CHF Submit

* Median value for all MDPI journals in the second half of 2022.


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Published Papers (3 papers)

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Article
Influence of Target-Substrate Distance on the Transport Process of Sputtered Atoms: MC-MD Multiscale Coupling Simulation
Materials 2022, 15(24), 8904; https://doi.org/10.3390/ma15248904 - 13 Dec 2022
Viewed by 422
Abstract
A Monte Carlo (MC) and molecular dynamics (MD) coupling simulation scheme for sputtered particle transport was first proposed in this work. In this scheme, the MC method was utilized to model the free-flight process of sputtered atoms, while the MD model was adopted [...] Read more.
A Monte Carlo (MC) and molecular dynamics (MD) coupling simulation scheme for sputtered particle transport was first proposed in this work. In this scheme, the MC method was utilized to model the free-flight process of sputtered atoms, while the MD model was adopted to simulate the collision between the sputtered atom and background gas atom so as to self-consistently calculate the post-collision velocity of the sputtered atom. The reliability of the MD collision model has been verified by comparing the computation results of the MD model and of an analytical model. This MC-MD coupling simulation scheme was used to investigate the influence of target-substrate distance on the transport characteristic parameters of sputtered Cu atoms during magnetron sputtering discharge. As the target-substrate distance increased from 30 to 150 mm, the peak energy of the incident energy distribution of deposited Cu atoms decreased from 2 to 1 eV due to the gradual thermalization of sputtered atoms. The distribution of differential deposition rate in unit solid angle firstly became more forward-peaked and then reversely approached the cosine distribution, which was agreed with the existing experimental observations. This work is expected to provide a more realistic simulation scheme for sputtered particle transport, which can be further combined with the MD simulation of sputtered film growth to explore the influence mechanism of process parameters on the properties of sputtered film. Full article
(This article belongs to the Topic Hybrid Computational Methods in Materials Engineering)
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Article
Research on Thickness Defect Control of Strip Head Based on GA-BP Rolling Force Preset Model
Metals 2022, 12(6), 924; https://doi.org/10.3390/met12060924 - 27 May 2022
Cited by 1 | Viewed by 897
Abstract
Due to the inaccuracy of the preset rolling force of cold rolling, there is a severe thickness defect in the strip head after cold rolling due to the flying gauge change (FGC), which affects the yield of the strip. This paper establishes a [...] Read more.
Due to the inaccuracy of the preset rolling force of cold rolling, there is a severe thickness defect in the strip head after cold rolling due to the flying gauge change (FGC), which affects the yield of the strip. This paper establishes a rolling force preset model (RFPM) by combining the rolling force optimization model (RFOM) and the rolling force deviation prediction model (RFDPM). The RFOM used a genetic algorithm (GA) to optimize the deformation resistance and friction coefficient models. The RFDPM is constructed using a backpropagation (BP) neural network. The calculation result of the RFPM shows that the average fraction defect of the preset rolling force is only 1.24%, which proves that the RFPM has good calculation accuracy. Experiments show that the defect length proportion of the strip head thickness at less than 20 m after FGC increases from 38.8% to 55.8%, while the average defect length decreases from 47.3 m to 29.6 m, effectively improving the yield of cold rolling. Full article
(This article belongs to the Topic Hybrid Computational Methods in Materials Engineering)
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Article
Algorithm for Determining Time Series of Phase Transformations in the Solid State Using Long-Short-Term Memory Neural Network
Materials 2022, 15(11), 3792; https://doi.org/10.3390/ma15113792 - 26 May 2022
Cited by 1 | Viewed by 746
Abstract
In the numerical analysis of manufacturing processes of metal parts, many material properties depending on, for example, the temperature or stress state, must be taken into account. Often these data are dependent on the temperature changes over time. Strongly non-linear material property relationships [...] Read more.
In the numerical analysis of manufacturing processes of metal parts, many material properties depending on, for example, the temperature or stress state, must be taken into account. Often these data are dependent on the temperature changes over time. Strongly non-linear material property relationships are usually represented using diagrams. In numerical calculations, these diagrams are analyzed in order to take into account the coupling between the properties. An example of these types of material properties is the dependence of the kinetics of phase transformations in the solid state on the rate and history of temperature change. In literature, these data are visualized Continuous Heating Transformation (CHT) and Continuous Cooling Transformation (CCT) diagrams. Therefore, it can be concluded that time series analysis is important in numerical modeling. This analysis can also be performed using neural networks. This work presents a new approach to storing and analyzing the data contained in the discussed CCT diagrams. The application of Long-Short-Term Memory (LSTM) neural networks and their architecture to determine the correct values of phase fractions depending on the history of temperature change was analyzed. Moreover, an area of research was elements that determine what type of information should be stored by LSTM network coefficients, e.g., whether the network should store information about changes of single phase transformations, or whether it would be better to extract data from differences between several networks with similar architecture. The purpose of the studied network is strongly different from typical applications of artificial neural networks. The main goal of the network was to store information (even by overfitting the network) rather than some form of generalization that allows computation for unknown cases. Therefore, the authors primarily investigated in the ability of the layer-based LSTM network to store nonlinear time series data. The analyses presented in this paper are an extension of the issues presented in the paper entitled “Model of the Austenite Decomposition during Cooling of the Medium Carbon Steel Using LSTM Recurrent Neural Network”. Full article
(This article belongs to the Topic Hybrid Computational Methods in Materials Engineering)
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