Multi-scale Modeling and Optimisation of Materials

Topic Information

Dear Colleagues,

More than a century ago, manufactured materials’ fatigue started to be investigated, while material performance evaluation was rethought as a result of the introduction of new technical materials, testing techniques, and computer methodologies. By combining cutting-edge sensor technology with real-time photos of material behavior, it became possible to gain a better understanding of the mechanisms causing damage on a sub-microscale. Meanwhile, incorporating computational methods into multi-scale modeling techniques, continuously enhanced by an ever-increasing computer power, resulted in further insights into the optimization and design of resilient materials. The use of data-driven algorithms allowed for the successful completion of complex structure–property interactions, which would have been computationally costly had physics-based models been used alone. Although substantial research has been conducted on the topic, the materials science community is in even greater need of interdisciplinary methods for multi-scale modeling and optimization. Therefore, we welcome notable and pioneering researchers to participate in our endeavor to advance the current state of the art in this field, within the scope outlined below.

Dr. Mustafa Awd
Prof. Dr. Frank Walther
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Mechanics applmech	1.5	3.5	2020	20.4 Days	CHF 1400	Submit
Applied Sciences applsci	2.5	5.5	2011	19.8 Days	CHF 2400	Submit
Materials materials	3.2	6.4	2008	15.2 Days	CHF 2600	Submit
Polymers polymers	4.9	9.7	2009	14 Days	CHF 2700	Submit
Solids solids	2.4	4.5	2020	22.6 Days	CHF 1200	Submit
Metals metals	2.5	5.3	2011	18 Days	CHF 2600	Submit

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Published Papers (4 papers)

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31 pages, 8853 KB  

Open AccessArticle

Atomistic-Based Fatigue Property Normalization Through Maximum A Posteriori Optimization in Additive Manufacturing

by Mustafa Awd, Lobna Saeed and Frank Walther

Materials 2025, 18(14), 3332; https://doi.org/10.3390/ma18143332 - 15 Jul 2025

Viewed by 481

Abstract

This work presents a multiscale, microstructure-aware framework for predicting fatigue strength distributions in additively manufactured (AM) alloys—specifically, laser powder bed fusion (L-PBF) AlSi10Mg and Ti-6Al-4V—by integrating density functional theory (DFT), instrumented indentation, and Bayesian inference. The methodology leverages principles common to all 3D [...] Read more.

This work presents a multiscale, microstructure-aware framework for predicting fatigue strength distributions in additively manufactured (AM) alloys—specifically, laser powder bed fusion (L-PBF) AlSi10Mg and Ti-6Al-4V—by integrating density functional theory (DFT), instrumented indentation, and Bayesian inference. The methodology leverages principles common to all 3D printing (additive manufacturing) processes: layer-wise material deposition, process-induced defect formation (such as porosity and residual stress), and microstructural tailoring through parameter control, which collectively differentiate AM from conventional manufacturing. By linking DFT-derived cohesive energies with indentation-based modulus measurements and a MAP-based statistical model, we quantify the effect of additive-manufactured microstructural heterogeneity on fatigue performance. Quantitative validation demonstrates that the predicted fatigue strength distributions agree with experimental high-cycle and very-high-cycle fatigue (HCF/VHCF) data, with posterior modes and 95 % credible intervals of ${\widehat{\sigma }}_f^{\mathrm{AlSi}10\mathrm{Mg}}={86}_{-7}^{+8}\mathrm{MPa}$ and ${\widehat{\sigma }}_f^{\mathrm{Ti}–6\mathrm{Al}–4\mathrm{V}}={115}_{-9}^{+10}\mathrm{MPa}$, respectively. The resulting Woehler (S–N) curves and Paris crack-growth parameters envelop more than 92 % of the measured coupon data, confirming both accuracy and robustness. Furthermore, global sensitivity analysis reveals that volumetric porosity and residual stress account for over 70 % of the fatigue strength variance, highlighting the central role of process–structure relationships unique to AM. The presented framework thus provides a predictive, physically interpretable, and data-efficient pathway for microstructure-informed fatigue design in additively manufactured metals, and is readily extensible to other AM alloys and process variants. Full article

(This article belongs to the Topic Multi-scale Modeling and Optimisation of Materials)

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27 pages, 4124 KB  

Open AccessArticle

Evaluating Binary Molybdenum Alloys as Strong and Ductile High-Temperature Materials

by Cheng Fu, Jiayi Yan, Jiang Yu, Yuhong Ren and Sha Li

Materials 2025, 18(14), 3329; https://doi.org/10.3390/ma18143329 - 15 Jul 2025

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Abstract

Molybdenum alloys as refractory alloys can provide strength levels at operating temperatures higher than that of Ni-base superalloys, yet their ductility is usually inferior to Ni-base alloys. Currently, commercialized Mo alloys are much fewer than Ni alloys. The motivation of this work is [...] Read more.

Molybdenum alloys as refractory alloys can provide strength levels at operating temperatures higher than that of Ni-base superalloys, yet their ductility is usually inferior to Ni-base alloys. Currently, commercialized Mo alloys are much fewer than Ni alloys. The motivation of this work is to explore opportunities of discovering useful alloys from the usually less investigated binary Mo-X systems (X = alloying element). With computational thermodynamics (CALPHAD), first-principles calculation, and mechanistic modeling combined, in this work a large number of Mo-X binary systems are investigated in terms of thermodynamic features and mechanical properties (yield strength, ductility, ductile-brittle transition temperature, creep resistance, and stress-strain relationship). The applicability of the alloy systems as solution-strengthened or precipitation-strengthened alloys is investigated. Starting from 92 Mo-X systems, a down-selection process is implemented, the results of which include three candidate systems for precipitation strengthening (Mo-B, Mo-C, Mo-Si) and one system (Mo-Re) for solid-solution strengthened alloy. In a composition optimization of Mo alloys to reach the properties of Ni-base superalloys, improving ductility is of top priority, for which Re plays a unique role. The presented workflow is also applicable to other bcc refractory alloy systems. Full article

(This article belongs to the Topic Multi-scale Modeling and Optimisation of Materials)

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37 pages, 11657 KB  

Open AccessArticle

Experimental Evaluation of Temperature and Strain-Rate-Dependent Mechanical Properties of Austenitic Stainless Steel SS316LN and a New Methodology to Evaluate Parameters of Johnson–Cook and Ramberg–Osgood Material Models

by Sanjay Kumar Pandey and Mahendra Kumar Samal

Solids 2025, 6(1), 7; https://doi.org/10.3390/solids6010007 - 11 Feb 2025

Viewed by 2045

Abstract

Austenitic stainless steel SS316LN is used as the material of construction of the vessel and core components of fast breeder reactors, which operate at an elevated temperature of 550 °C. For design and integrity analysis using the finite element method, material models, such [...] Read more.

Austenitic stainless steel SS316LN is used as the material of construction of the vessel and core components of fast breeder reactors, which operate at an elevated temperature of 550 °C. For design and integrity analysis using the finite element method, material models, such as Johnson–Cook and Ramberg–Osgood, are widely used. However, the temperature- and strain-rate-dependent plasticity and damage parameters of these models for this material are not available in the literature. Moreover, the method of evaluation of temperature and strain-rate-dependent plasticity parameters, in literature, has some major shortcomings, which have been addressed in this work. In addition, a new optimization-based procedure has been developed to evaluate all nine plasticity and damage parameters, which uses results of combined finite element analysis and experimental data. The procedure has been validated extensively by testing tensile specimens at different temperatures, by testing notched tensile specimens of different notch radii, and by carrying out high strain-rate tests using a split Hopkinson pressure bar test setup. The parameters of the Johnson–Cook material model, evaluated in this work, have been used in finite element analysis to simulate load-displacement behavior and fracture strains of various types of specimens, and the results have been compared with experimental data in order to check the accuracy of the parameters. The procedure developed in this work shall help the researchers to adopt such a technique for accurate estimation of both plasticity and damage parameters of different types of material models. Full article

(This article belongs to the Topic Multi-scale Modeling and Optimisation of Materials)

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23 pages, 9957 KB  

Open AccessArticle

Multi-Objective Optimization of Three-Stage Turbomachine Rotor Based on Complex Transfer Matrix Method

by Hüseyin Tarık Niş and Ahmet Yıldız

Appl. Sci. 2024, 14(22), 10445; https://doi.org/10.3390/app142210445 - 13 Nov 2024

Viewed by 1294

Abstract

This study presents the complex transfer matrix method (CTMM) as an advanced mathematical model, providing significant advantages over the finite element method (FEM) by yielding rapid solutions for complex optimization problems. In order to design a more efficient structure of a three-stage turbomachine [...] Read more.

This study presents the complex transfer matrix method (CTMM) as an advanced mathematical model, providing significant advantages over the finite element method (FEM) by yielding rapid solutions for complex optimization problems. In order to design a more efficient structure of a three-stage turbomachine rotor, we integrated this method with various optimization algorithms, including genetic algorithm (GA), differential evolution (DE), simulated annealing (SA), gravitational search algorithm (GSA), black hole (BH), particle swarm optimization (PSO), Harris hawk optimization (HHO), artificial bee colony (ABC), and non-metaheuristic pattern search (PS). Thus, the best rotor geometry can be obtained fast with minimum bearing forces and disk deflections within design limits. In the results, the efficiency of the CTMM for achieving optimized designs is demonstrated. The CTMM outperformed the FEM in both speed and applicability for complex rotordynamic problems. The CTMM was found to deliver results of comparable quality much faster than the FEM, especially with higher element quality. The use of the CTMM in the iterative optimization process is shown to be highly advantageous. Furthermore, it is noted that among the different optimization algorithms, ABC provided the best results for this multi-objective optimization problem. Full article

(This article belongs to the Topic Multi-scale Modeling and Optimisation of Materials)

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