Hybrid Computational Methods in Materials Engineering

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

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI ai	-	-	2020	20.8 Days	CHF 1600
Alloys alloys	-	-	2022	15.0 days *	CHF 1000
Applied Sciences applsci	2.7	4.5	2011	16.9 Days	CHF 2400
Materials materials	3.4	5.2	2008	13.9 Days	CHF 2600
Metals metals	2.9	4.4	2011	15 Days	CHF 2600

* Median value for all MDPI journals in the second half of 2023.

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Published Papers (6 papers)

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16 pages, 2862 KiB  

Open AccessArticle

A Hybrid Method for Calculating the Chemical Composition of Steel with the Required Hardness after Cooling from the Austenitizing Temperature

by Jacek Trzaska and Wojciech Sitek

Materials 2024, 17(1), 97; https://doi.org/10.3390/ma17010097 - 24 Dec 2023

Viewed by 672

Abstract

The article presents a hybrid method for calculating the chemical composition of steel with the required hardness after cooling from the austenitizing temperature. Artificial neural networks (ANNs) and genetic algorithms (GAs) were used to develop the model. Based on 550 diagrams of continuous [...] Read more.

The article presents a hybrid method for calculating the chemical composition of steel with the required hardness after cooling from the austenitizing temperature. Artificial neural networks (ANNs) and genetic algorithms (GAs) were used to develop the model. Based on 550 diagrams of continuous cooling transformation (CCT) of structural steels available in the literature, a dataset of experimental data was created. Artificial neural networks were used to develop a hardness model describing the relationship between the chemical composition of the steel, the austenitizing temperature, and the hardness of the steel after cooling. A genetic algorithm was used to identify the chemical composition of the steel with the required hardness. The value of the objective function was calculated using the neural network model. The developed method for identifying the chemical composition was implemented in a computer application. Examples of calculations of mass concentrations of steel elements with the required hardness after cooling from the austenitizing temperature are presented. The model proposed in this study can be a valuable tool to support chemical composition design by reducing the number of experiments and minimizing research costs. Full article

(This article belongs to the Topic Hybrid Computational Methods in Materials Engineering)

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13 pages, 3905 KiB  

Open AccessArticle

Hybrid Smoothed-Particle Hydrodynamics/Finite Element Method Simulation of Water Droplet Erosion on Ductile Metallic Targets

by Alejandro Mora, Ruihan Xu and Siegfried Schmauder

Metals 2023, 13(12), 1937; https://doi.org/10.3390/met13121937 - 27 Nov 2023

Viewed by 794

Abstract

Erosion of metallic surfaces due to the permanent impact of high-speed water droplets is a significant concern in diverse industrial applications like turbine blades, among others. In the initial stage of water droplet erosion, there is an incubation regime with negligible mass loss [...] Read more.

Erosion of metallic surfaces due to the permanent impact of high-speed water droplets is a significant concern in diverse industrial applications like turbine blades, among others. In the initial stage of water droplet erosion, there is an incubation regime with negligible mass loss whose duration is strongly dependent on water droplet sizes and velocities, the initial state of the surface, and the material properties of the target. The prediction of the incubation period duration is one of the main topics of research in the field. In this work, the interaction of the water droplets with a metallic surface is simulated using a hybrid Smoothed-Particle Hydrodynamics/Finite Element Method modeling scheme. The effect of multiple random impacts on representative target areas for certain ranges of impact angles and velocities was studied using a combination of simple material and damage models for the target surface of Ti-6Al-4V titanium alloy. The simulation is able to reproduce the main dependencies of the incubation regime and the first stages of water droplet erosion on the impact angle and velocity as reported in the literature. This framework can be considered a foundation for more advanced models with the goal of a better understanding of the physical mechanisms behind the incubation regime in order to devise strategies to extend it in real applications. Full article

(This article belongs to the Topic Hybrid Computational Methods in Materials Engineering)

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24 pages, 8820 KiB  

Open AccessArticle

The Impact of ECAP Parameters on the Structural and Mechanical Behavior of Pure Mg: A Combination of Experimental and Machine Learning Approaches

by Waleed H. El-Garaihy, Amal BaQais, Abdulrahman I. Alateyah, Mohammed F. Alsharekh, Majed O. Alawad, Mahmoud Shaban, Fahad Nasser Alsunaydih and Mokhtar Kamel

Appl. Sci. 2023, 13(10), 6279; https://doi.org/10.3390/app13106279 - 20 May 2023

Cited by 1 | Viewed by 1172

Abstract

Commercial pure Mg specimens were processed through equal channel angular pressing (ECAP) using two dies with die angles of 90° and 120°. Mg billets were processed up to four passes via different route types. Machine learning (ML) techniques were adopted to estimate the [...] Read more.

Commercial pure Mg specimens were processed through equal channel angular pressing (ECAP) using two dies with die angles of 90° and 120°. Mg billets were processed up to four passes via different route types. Machine learning (ML) techniques were adopted to estimate the ECAP parameters and verify the experimental findings. Several ML techniques were employed to estimate the effect ECAP parameters of pure Mg on microstructural evolution, Vicker’s microhardness (HV), and tensile properties for ECAP billets and their as-annealed (AA) counterparts. Electron back-scatter diffraction (EBSD) was applied to determine the structural evolution and crystallographic texture both prior to and following the ECAP process for the Mg billets. EBSD analysis showed that route Bc is the most effective route in grain refinement, and four passes of route Bc experienced a significant refinement of 86% compared to the AA condition. Furthermore, the crystallographic texture showed that four passes of route Bc produced the most robust texture that was greater than 26.21 times random. ML findings revealed that the grain size demonstrated a strong correlation of −0.67 with rising number of passes, while ϕ affected the grain size strongly with 0.83. When adopting a 90°-die to accumulate the plastic strain up to 4Bc, the subsequent HV was indeed 111% higher than that of the AA equivalent. From ML findings it was clear that the number of passes was the most significant parameter on the Mg HV values, while ECAP channel angle (ϕ) revealed high correlation factor with HV values as well. Furthermore, four passes of route Bc with ϕ = 90° and 120° led to a significant increase of the tensile strength by 44.7%% and 35.7%, respectively, compared to the AA counterpart. ML findings revealed that the tensile strength was affected by the increasing number of passes with a strong correlation of 0.81, while affecting ductility moderately with 0.47. Full article

(This article belongs to the Topic Hybrid Computational Methods in Materials Engineering)

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16 pages, 6254 KiB  

Open AccessArticle

Influence of Target-Substrate Distance on the Transport Process of Sputtered Atoms: MC-MD Multiscale Coupling Simulation

by Guo Zhu, Qixin Du, Baijun Xiao, Ganxin Chen and Zhiyin Gan

Materials 2022, 15(24), 8904; https://doi.org/10.3390/ma15248904 - 13 Dec 2022

Cited by 3 | Viewed by 1040

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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17 pages, 10986 KiB  

Open AccessArticle

Research on Thickness Defect Control of Strip Head Based on GA-BP Rolling Force Preset Model

by Luzhen Chen, Wenquan Sun, Anrui He, Tieheng Yuan, Jianrui Shi and Yi Qiang

Metals 2022, 12(6), 924; https://doi.org/10.3390/met12060924 - 27 May 2022

Cited by 4 | Viewed by 1486

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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15 pages, 2662 KiB  

Open AccessArticle

Algorithm for Determining Time Series of Phase Transformations in the Solid State Using Long-Short-Term Memory Neural Network

by Joanna Wróbel and Adam Kulawik

Materials 2022, 15(11), 3792; https://doi.org/10.3390/ma15113792 - 26 May 2022

Cited by 2 | Viewed by 1284

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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