Special Issue "Functional Materials, Machine Learning, and Optimization"

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Advanced Composites".

Deadline for manuscript submissions: 15 February 2022.

Special Issue Editors

Dr. Denis A. Vinnik
E-Mail Website
Guest Editor
Chemistry Faculty, South Ural State University, Chelyabinsk, Russia
Interests: single crystal growth; thermodynamic modeling; ceramics; functional magnetic oxides
Special Issues and Collections in MDPI journals
Dr. Asif Afzal
E-Mail Website
Guest Editor
Department of Mechanical Engineering, P. A. College of Engineering (Affiliated to Visvesvaraya Technological University, Belagavi), Mangaluru 574153, India
Interests: nano-materials; machine learning; optimization; design of experiments; thermal sciences; numerical analysis
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Machine learning is one of the most exciting tools that the materials science toolbox has entered in recent years. This statistical collection has already shown that both fundamental and applied research can be considerably accelerated. The introduction of machine learning into materials science methods is still recent. Experiments have traditionally played a leading role in the identification and characterization of new materials. For a very limited number of materials, experimental research needs to be carried out over a long period of time, as it has high resource and equipment requirements. In this scenario, machine learning can easily help material scientists in exploring the hidden features of materials using this experimented data without really doing any experiment.

In the last decade, optimization in the materials field has been accelerated because the enormous efficiency gain can be achieved through the optimization of topology at the conception stage. The ability to control the geometry, structures, cost, and properties of materials established for them can be solved by single and multi-objective optimization.

This Special Issue will bring these emerging fields of science and technology to one platform to address the importance of functional materials for various applications, including the application of machine learning in modeling, data analysis of material properties, and the use of optimization techniques to obtain the established properties of these materials. The Special Issue covers a large number of topics, including the preparation of functional materials, their characterization, and the study of mechanical and tribological properties. Modeling of this data using available regression algorithms of supervised learning and optimization of properties of materials applying various soft computing algorithms and the design of experiments.

Dr. Denis A. Vinnik
Dr. Asif Afzal
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Materials is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • composite materials
  • ceramics
  • nano-composites
  • metallic materials
  • characterization
  • mechanical properties
  • synthesis
  • tribological properties
  • bio-materials
  • experimental investigations
  • modeling
  • regression
  • prediction
  • optimization
  • fitness functions
  • algorithms

Published Papers (10 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
Nuclear Radiation Shielding Characteristics of Some Natural Rocks by Using EPICS2017 Library
Materials 2021, 14(16), 4669; https://doi.org/10.3390/ma14164669 - 19 Aug 2021
Viewed by 256
Abstract
Radiation leakage is a serious problem in various technological applications. In this paper, radiation shielding characteristics of some natural rocks are elucidated. Mass attenuation coefficients (µ/ρ) of these rocks are obtained at different photon energies with the help of the EPICS2017 library. The [...] Read more.
Radiation leakage is a serious problem in various technological applications. In this paper, radiation shielding characteristics of some natural rocks are elucidated. Mass attenuation coefficients (µ/ρ) of these rocks are obtained at different photon energies with the help of the EPICS2017 library. The obtained µ/ρ values are confirmed via the theoretical XCOM program by determining the correlation factor and relative deviation between both of these methods. Then, effective atomic number (Zeff), absorption length (MFP), and half value layer (HVL) are evaluated by applying the µ/ρ values. The maximum μ/ρ values of the natural rocks were observed at 0.37 MeV. At this energy, the Zeff values of the natural rocks were 16.23, 16.97, 17.28, 10.43, and 16.65 for olivine basalt, jet black granite, limestone, sandstone, and dolerite, respectively. It is noted that the radiation shielding features of the selected natural rocks are higher than that of conventional concrete and comparable with those of commercial glasses. Therefore, the present rocks can be used in various radiation shielding applications, and they have many advantages for being clean and low-cost products. In addition, we found that the EPICS2017 library is useful in determining the radiation shielding parameters for the rocks and may be used for further calculations for other rocks and construction building materials. Full article
(This article belongs to the Special Issue Functional Materials, Machine Learning, and Optimization)
Show Figures

Figure 1

Article
Influence of Li2O Incrementation on Mechanical and Gamma-Ray Shielding Characteristics of a TeO2-As2O3-B2O3 Glass System
Materials 2021, 14(14), 4060; https://doi.org/10.3390/ma14144060 - 20 Jul 2021
Viewed by 513
Abstract
According to the Makishema–Mackenzie model assumption, the dissociation energy and packing density for a quaternary TeO2-As2O3-B2O3-Li2O glass system were evaluated. The dissociation energy rose from 67.07 to 71.85 kJ/cm3, [...] Read more.
According to the Makishema–Mackenzie model assumption, the dissociation energy and packing density for a quaternary TeO2-As2O3-B2O3-Li2O glass system were evaluated. The dissociation energy rose from 67.07 to 71.85 kJ/cm3, whereas the packing factor decreased from 16.55 to 15.21 cm3/mol associated with the replacement of TeO2 by LiO2 compounds. Thus, as a result, the elastic moduli (longitudinal, shear, Young, and bulk) were enhanced by increasing the LiO2 insertion. Based on the estimated elastic moduli, mechanical properties such as the Poisson ratio, microhardness, longitudinal velocity, shear velocity, and softening temperature were evaluated for the investigated glass samples. In order to evaluate the studied glasses’ gamma-ray shield capacity, the MCNP-5 code, as well as a theoretical Phy-X/PSD program, were applied. The best shielding capacity was achieved for the glass system containing 25 mol% of TeO2, while the lowest ability was obtained for the glass sample with a TeO2 concentration of 5 mol%. Furthermore, a correlation between the studied glasses’ microhardness and linear attenuation coefficient was performed versus the LiO2 concentration to select the glass sample which possesses a suitable mechanical and shielding capacity. Full article
(This article belongs to the Special Issue Functional Materials, Machine Learning, and Optimization)
Show Figures

Figure 1

Article
Investigation of Mechanical and Physical Properties of Big Sheep Horn as an Alternative Biomaterial for Structural Applications
Materials 2021, 14(14), 4039; https://doi.org/10.3390/ma14144039 - 20 Jul 2021
Cited by 1 | Viewed by 530
Abstract
This paper investigates the physical and mechanical properties of bighorns of Deccani breed sheep native from Karnataka, India. The exhaustive work comprises two cases. First, rehydrated (wet) and ambient (dry) conditions, and second, the horn coupons were selected for longitudinal and lateral (transverse) [...] Read more.
This paper investigates the physical and mechanical properties of bighorns of Deccani breed sheep native from Karnataka, India. The exhaustive work comprises two cases. First, rehydrated (wet) and ambient (dry) conditions, and second, the horn coupons were selected for longitudinal and lateral (transverse) directions. More than seventy-two samples were subjected to a test for physical and mechanical property extraction. Further, twenty-four samples were subjected to physical property testing, which included density and moisture absorption tests. At the same time, mechanical testing included analysis of the stress state dependence with the horn keratin tested under tension, compression, and flexural loading. The mechanical properties include the elastic modulus, yield strength, ultimate strength, failure strain, compressive strength, flexural strength, flexural modulus, and hardness. The results showed anisotropy and depended highly on the presence of water content more than coupon orientation. Wet conditioned specimens had a significant loss in mechanical properties compared with dry specimens. The observed outcomes were shown at par with results for yield strength of 53.5 ± 6.5 MPa (which is better than its peers) and a maximum compressive stress of 557.7 ± 5 MPa (highest among peers). Young’s modulus 6.5 ± 0.5 GPa and a density equivalent to a biopolymer of 1.2 g/cc are expected to be the lightest among its peers; flexural strength 168.75 MPa, with lowest failure strain percentage of 6.5 ± 0.5 and Rockwell hardness value of 60 HRB, seem best in the class of this category. Simulation study identified a suitable application area based on impact and fatigue analysis. Overall, the exhaustive experimental work provided many opportunities to use this new material in various diversified applications in the future. Full article
(This article belongs to the Special Issue Functional Materials, Machine Learning, and Optimization)
Show Figures

Figure 1

Article
The Role of La2O3 in Enhancement the Radiation Shielding Efficiency of the Tellurite Glasses: Monte-Carlo Simulation and Theoretical Study
Materials 2021, 14(14), 3913; https://doi.org/10.3390/ma14143913 - 13 Jul 2021
Viewed by 413
Abstract
The radiation shielding competence was examined for a binary glass system xLa2O3 + (1 − x) TeO2 where x = 5, 7, 10, 15, and 20 mol% using MCNP-5 code. The linear attenuation coefficients (LACs) of the [...] Read more.
The radiation shielding competence was examined for a binary glass system xLa2O3 + (1 − x) TeO2 where x = 5, 7, 10, 15, and 20 mol% using MCNP-5 code. The linear attenuation coefficients (LACs) of the glasses were evaluated, and it was found that LT20 glass has the greatest LAC, while LT5 had the least LAC. The transmission factor (TF) of the glasses was evaluated against thicknesses at various selected energies and was observed to greatly decrease with increasing thickness; for example, at 1.332 MeV, the TF of the LT5 glass decreased from 0.76 to 0.25 as the thickness increased from 1 to 5 cm. The equivalent atomic number (Zeq) of the glasses gradually increased with increasing photon energy above 0.1 MeV, with the maximum values observed at around 1 MeV. The buildup factors were determined to evaluate the accumulation of photon flux, and it was found that the maximum values for both can be seen at around 0.8 MeV. This research concluded that LT20 has the greatest potential in radiation shielding applications out of the investigated glasses due to the glass having the most desirable parameters. Full article
(This article belongs to the Special Issue Functional Materials, Machine Learning, and Optimization)
Show Figures

Figure 1

Article
Synthesis and Characterization of Mechanical Properties and Wire Cut EDM Process Parameters Analysis in AZ61 Magnesium Alloy + B4C + SiC
Materials 2021, 14(13), 3689; https://doi.org/10.3390/ma14133689 - 01 Jul 2021
Cited by 1 | Viewed by 478
Abstract
Wire Cut Electric Discharge Machining (WCEDM) is a novel method for machining different materials with application of electrical energy by the movement of wire electrode. For this work, an AZ61 magnesium alloy with reinforcement of boron carbide and silicon carbide in different percentage [...] Read more.
Wire Cut Electric Discharge Machining (WCEDM) is a novel method for machining different materials with application of electrical energy by the movement of wire electrode. For this work, an AZ61 magnesium alloy with reinforcement of boron carbide and silicon carbide in different percentage levels was used and a plate was formed through stir casting technique. The process parameters of the stir casting process are namely reinforcement %, stirring speed, time of stirring, and process temperature. The specimens were removed from the casted AZ61 magnesium alloy composites through the Wire Cut Electric Discharge Machining (WCEDM) process, the material removal rate and surface roughness vales were carried out creatively. L 16 orthogonal array (OA) was used for this work to find the material removal rate (MRR) and surface roughness. The process parameters of WCEDM are pulse on time (105, 110, 115 and 120 µs), pulse off time (40, 50, 60 and 70 µs), wire feed rate (2, 4, 6 and 8 m/min), and current (3, 6, 9 and 12 Amps). Further, this study aimed to estimate the maximum ultimate tensile strength and micro hardness of the reinforced composites using the Taguchi route. Full article
(This article belongs to the Special Issue Functional Materials, Machine Learning, and Optimization)
Show Figures

Figure 1

Article
Mechanical and Gamma Ray Absorption Behavior of PbO-WO3-Na2O-MgO-B2O3 Glasses in the Low Energy Range
Materials 2021, 14(13), 3466; https://doi.org/10.3390/ma14133466 - 22 Jun 2021
Cited by 1 | Viewed by 314
Abstract
The Makishima and Mackenzie model has been used to determine the mechanical properties of the PbO-WO3-Na2O-MgO-B2O3 glass system. The number of bonds per unit volume of the glasses (nb) increases from 9.40 × 10 [...] Read more.
The Makishima and Mackenzie model has been used to determine the mechanical properties of the PbO-WO3-Na2O-MgO-B2O3 glass system. The number of bonds per unit volume of the glasses (nb) increases from 9.40 × 1022 to 10.09 × 1022 cm−3 as the PbO content increases from 30 to 50 mol%. The Poisson’s ratio (σ) for the examined glasses falls between 0.174 and 0.210. The value of the fractal bond connectivity (d) for the present glasses ranges from 3.08 to 3.59. Gamma photon and fast neutron shielding parameters were evaluated via Phy-X/PSD, while that of electrons were calculated via the ESTAR platform. Analysis of the parameters showed that both photon and electron attenuation ability improve with the PbO content. The fast neutron removal cross section of the glasses varies from 0.094–0.102 cm−1 as PbO molar content reduced from 50–30 mol%. Further analysis of shielding parameters of the investigated glass system showed that they possess good potential to function in radiation protection applications. Full article
(This article belongs to the Special Issue Functional Materials, Machine Learning, and Optimization)
Show Figures

Figure 1

Article
Multi Ceramic Particles Inclusion in the Aluminium Matrix and Wear Characterization through Experimental and Response Surface-Artificial Neural Networks
Materials 2021, 14(11), 2895; https://doi.org/10.3390/ma14112895 - 28 May 2021
Cited by 3 | Viewed by 622
Abstract
Lightweight composite materials have recently been recognized as appropriate materials have been adopted in many industrial applications because of their versatility. The present research recognizes the inclusion of ceramics such as Gr and B4C in manufacturing AMMCs through stir casting. Prepared [...] Read more.
Lightweight composite materials have recently been recognized as appropriate materials have been adopted in many industrial applications because of their versatility. The present research recognizes the inclusion of ceramics such as Gr and B4C in manufacturing AMMCs through stir casting. Prepared composites were tested for hardness and wear behaviour. The tests’ findings revealed that the reinforced matrix was harder (60%) than the un-reinforced alloy because of the increased ceramic phase. The rising content of B4C and Gr particles led to continuous improvements in wear resistance. The microstructure and worn surface were observed through SEM (Scanning electron microscope) and revealed the formation of mechanically mixed layers of both B4C and Gr, which served as the effective insulation surface and protected the test sample surface from the steel disc. With the rise in the content of B4C and Gr, the weight loss declined, and significant wear resistance was achieved at 15 wt.% B4C and 10 wt.% Gr. A response surface analysis for the weight loss was carried out to obtain the optimal objective function. Artificial neural network methodology was adopted to identify the significance of the experimental results and the importance of the wear parameters. The error between the experimental and ANN results was found to be within 1%. Full article
(This article belongs to the Special Issue Functional Materials, Machine Learning, and Optimization)
Show Figures

Figure 1

Article
Determination of Non-Recrystallization Temperature for Niobium Microalloyed Steel
Materials 2021, 14(10), 2639; https://doi.org/10.3390/ma14102639 - 18 May 2021
Cited by 3 | Viewed by 386
Abstract
In the present investigation, the non-recrystallization temperature (TNR) of niobium-microalloyed steel is determined to plan rolling schedules for obtaining the desired properties of steel. The value of TNR is based on both alloying elements and deformation parameters. In the literature, [...] Read more.
In the present investigation, the non-recrystallization temperature (TNR) of niobium-microalloyed steel is determined to plan rolling schedules for obtaining the desired properties of steel. The value of TNR is based on both alloying elements and deformation parameters. In the literature, TNR equations have been developed and utilized. However, each equation has certain limitations which constrain its applicability. This study was completed using laboratory-grade low-carbon Nb-microalloyed steels designed to meet the API X-70 specification. Nb- microalloyed steel is processed by the melting and casting process, and the composition is found by optical emission spectroscopy (OES). Multiple-hit deformation tests were carried out on a Gleeble® 3500 system in the standard pocket-jaw configuration to determine TNR. Cuboidal specimens (10 (L) × 20 (W) × 20 (T) mm3) were taken for compression test (multiple-hit deformation tests) in gleeble. Microstructure evolutions were carried out by using OM (optical microscopy) and SEM (scanning electron microscopy). The value of TNR determined for 0.1 wt.% niobium bearing microalloyed steel is ~ 951 °C. Nb- microalloyed steel rolled at TNR produce partially recrystallized grain with ferrite nucleation. Hence, to verify the TNR value, a rolling process is applied with the finishing rolling temperature near TNR (~951 °C). The microstructure is also revealed in the pancake shape, which confirms TNR. Full article
(This article belongs to the Special Issue Functional Materials, Machine Learning, and Optimization)
Show Figures

Figure 1

Article
Influence of the Fly Ash Material Inoculants on the Tensile and Impact Characteristics of the Aluminum AA 5083/7.5SiC Composites
Materials 2021, 14(9), 2452; https://doi.org/10.3390/ma14092452 - 09 May 2021
Cited by 4 | Viewed by 657
Abstract
The choice of suitable inoculants in the grain refinement process and subsequent enhancement of the characteristics of the composites developed is an important materials research topic, having wide scope. In this regard, the present work is aimed at finding the appropriate composition and [...] Read more.
The choice of suitable inoculants in the grain refinement process and subsequent enhancement of the characteristics of the composites developed is an important materials research topic, having wide scope. In this regard, the present work is aimed at finding the appropriate composition and size of fly ash as inoculants for grain refinement of the aluminum AA 5083 composites. Fly ash particles, which are by products of the combustion process in thermal power plants, contributing to the large-scale pollution and landfills can be effectively utilized as inoculants and interatomic lubricants in the composite matrix–reinforcement subspaces synthesized in the inert atmosphere using ultrasonic assisted stir casting setup. Thus, the work involves the study of the influence of percentage and size of the fly ash dispersions on the tensile and impact strength characteristics of the aluminum AA 5083/7.5SiC composites. The C type of fly ash with the particle size in the series of 40–75 µm, 76–100 µm, and 101–125 µm and weight % in the series of 0.5, 1, 1.5, 2, and 2.5 are selected for the work. The influence of fly ash as distinct material inoculants for the grain refinement has worked out well with the increase in the ultimate tensile strength, yield strength, and impact strength of the composites, with the fly ash as material inoculants up to 2 wt. % beyond which the tensile and impact characteristics decrease due to the micro coring and segregation. This is evident from the microstructural observations for the composite specimens. Moreover, the role of fly ash as material inoculants is distinctly identified with the X-Ray Diffraction (XRD) for the phase and grain growth epitaxy and the Energy Dispersive Spectroscopy (EDS) for analyzing the characteristic X-Rays of the fly ash particles as inoculant agents in the energy spectrum. Full article
(This article belongs to the Special Issue Functional Materials, Machine Learning, and Optimization)
Show Figures

Figure 1

Article
Evaluation of the Infill Design on the Tensile Response of 3D Printed Polylactic Acid Polymer
Materials 2021, 14(9), 2195; https://doi.org/10.3390/ma14092195 - 25 Apr 2021
Viewed by 645
Abstract
The current study explores the effects of geometrical shapes of the infills on the 3D printed polylactic acid (PLA) plastic on the tensile properties. For this purpose, by utilizing an accessible supply desktop printer, specimens of diamond, rectangular, and hexagonal infill patterns were [...] Read more.
The current study explores the effects of geometrical shapes of the infills on the 3D printed polylactic acid (PLA) plastic on the tensile properties. For this purpose, by utilizing an accessible supply desktop printer, specimens of diamond, rectangular, and hexagonal infill patterns were produced using the fused filament fabrication (FFF) 3D printing technique. Additionally, solid samples were printed for comparison. The printed tensile test specimens were conducted at environmental temperature, Ta of 23 °C and crosshead speed, VC.H of 5 mm/min. Mainly, this study focuses on investigating the percentage infill with respect to the cross-sectional area of the investigated samples. The mechanical properties, i.e., modulus of toughness, ultimate tensile stress, yield stress, and percent elongation, were explored for each sample having a different geometrical infill design. The test outcomes for each pattern were systematically compared. To further validate the experimental results, a computer simulation using finite element analysis was also performed and contrasted with the experimental tensile tests. The experimental results mainly suggested a brittle behavior for solidly infilled specimen, while rectangular, diamond, and hexagonal infill patterns showed ductile-like behavior (fine size and texture of infills). This brittleness may be due to the relatively higher infill density results that led to the high bonding adhesion of the printed layers, and the size and thickness effects of the solid substrate. It made the solidly infilled specimen structure denser and brittle. Among all structures, hexagon geometrical infill showed relative improvement in the mechanical properties (highest ultimate tensile stress and modulus values 1759.4 MPa and 57.74 MPa, respectively) compared with other geometrical infills. Therefore, the geometrical infill effects play an important role in selecting the suitable mechanical property’s values in industrial applications. Full article
(This article belongs to the Special Issue Functional Materials, Machine Learning, and Optimization)
Show Figures

Figure 1

Back to TopTop