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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: closed (10 April 2023) | Viewed by 48160

Special Issue Editors

Dr. Denis A. Vinnik

E-Mail Website
Guest Editor
Chemistry Faculty, South Ural State University, 454080 Chelyabinsk, Russia
Interests: single crystal growth; thermodynamic modeling; ceramics; functional magnetic oxides
Special Issues, Collections and Topics 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, Collections and Topics 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

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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 2600 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 (19 papers)

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Research

12 pages, 3623 KiB  
Article
Optimization of the Filler-and-Binder Mixing Ratio for Enhanced Mechanical Strength of Carbon–Carbon Composites
by Ji-Hong Kim, Hye-In Hwang and Ji-Sun Im
Materials 2023, 16(11), 4084; https://doi.org/10.3390/ma16114084 - 30 May 2023
Cited by 2 | Viewed by 1233
Abstract
In this paper, a method for optimizing the mixing ratio of filler coke and binder for high-strength carbon–carbon composites is proposed. Particle size distribution, specific surface area, and true density were analyzed to characterize the filler properties. The optimum binder mixing ratio was [...] Read more.
In this paper, a method for optimizing the mixing ratio of filler coke and binder for high-strength carbon–carbon composites is proposed. Particle size distribution, specific surface area, and true density were analyzed to characterize the filler properties. The optimum binder mixing ratio was experimentally determined based on the filler properties. As the filler particle size was decreased, a higher binder mixing ratio was required to enhance the mechanical strength of the composite. When the d50 particle size of the filler was 62.13 and 27.10 µm, the required binder mixing ratios were 25 and 30 vol.%, respectively. From this result, the interaction index, which quantifies the interaction between the coke and binder during carbonization, was deduced. The interaction index had a higher correlation coefficient with the compressive strength than that of the porosity. Therefore, the interaction index can be used in predicting the mechanical strength of carbon blocks and optimizing their binder mixing ratios. Furthermore, as it is calculated from the carbonization of blocks without additional analysis, the interaction index can be easily used in industrial applications. Full article
(This article belongs to the Special Issue Functional Materials, Machine Learning, and Optimization)
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21 pages, 4256 KiB  
Article
Modified Orange Peel Waste as a Sustainable Material for Adsorption of Contaminants
by Uloaku Michael-Igolima, Samuel J. Abbey, Augustine O. Ifelebuegu and Eyo U. Eyo
Materials 2023, 16(3), 1092; https://doi.org/10.3390/ma16031092 - 27 Jan 2023
Cited by 8 | Viewed by 4567
Abstract
World orange production is estimated at 60 million tons per annum, while the annual production of orange peel waste is 32 million tons. According to available data, the adsorption capacity of orange peel ranges from 3 mg/g to 5 mg/g, while their water [...] Read more.
World orange production is estimated at 60 million tons per annum, while the annual production of orange peel waste is 32 million tons. According to available data, the adsorption capacity of orange peel ranges from 3 mg/g to 5 mg/g, while their water uptake is lower than 1 mg/g. The low water uptake of orange peel and the abundance of biomass in nature has made orange peel an excellent biosorption material. This review summarised different studies on orange peel adsorption of various contaminants to identify properties of orange peel that influence the adsorption of contaminants. Most of the literature reviewed studied orange peel adsorption of heavy metals, followed by studies on the adsorption of dyes, while few studies have investigated adsorption of oil by orange peel. FTIR spectra analysis and SEM micrographs of raw and activated orange peels were studied to understand the structural properties of the biomass responsible for adsorption. The study identified pectin, hydroxyl, carbonyl, carboxyl, and amine groups as components and important functional groups responsible for adsorption in orange peel. Furthermore, changes were observed in the structural properties of the peel after undergoing various modifications. Physical modification increased the surface area for binding and the adsorption of contaminants, while chemical treatments increased the carboxylic groups enhancing adsorption and the binding of contaminants. In addition, heating orange peel during the thermal modification process resulted in a highly porous structure and a subsequent increase in adsorption capacities. In conclusion, physical, chemical, and thermal treatments improve the structural properties of orange peel, resulting in high biosorption uptake. However, orange peels treated with chemicals recorded the highest contaminants adsorption capacities. Full article
(This article belongs to the Special Issue Functional Materials, Machine Learning, and Optimization)
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26 pages, 3257 KiB  
Article
Marshall Stability Prediction with Glass and Carbon Fiber Modified Asphalt Mix Using Machine Learning Techniques
by Ankita Upadhya, Mohindra Singh Thakur, Mohammed Saleh Al Ansari, Mohammad Abdul Malik, Ahmad Aziz Alahmadi, Mamdooh Alwetaishi and Ali Nasser Alzaed
Materials 2022, 15(24), 8944; https://doi.org/10.3390/ma15248944 - 14 Dec 2022
Cited by 4 | Viewed by 1693
Abstract
Pavement design is a long-term structural analysis that is required to distribute traffic loads throughout all road levels. To construct roads for rising traffic volumes while preserving natural resources and materials, a better knowledge of road paving materials is required. The current study [...] Read more.
Pavement design is a long-term structural analysis that is required to distribute traffic loads throughout all road levels. To construct roads for rising traffic volumes while preserving natural resources and materials, a better knowledge of road paving materials is required. The current study focused on the prediction of Marshall stability of asphalt mixes constituted of glass, carbon, and glass-carbon combination fibers to exploit the best potential of the hybrid asphalt mix by applying five machine learning models, i.e., artificial neural networks, Gaussian processes, M5P, random tree, and multiple linear regression model and further determined the optimum model suitable for prediction of the Marshall stability in hybrid asphalt mixes. It was equally important to determine the suitability of each mix for flexible pavements. Five types of asphalt mixes, i.e., glass fiber asphalt mix, carbon fiber asphalt mix, and three modified asphalt mixes of glass-carbon fiber combination in the proportions of 75:25, 50:50, and 25:75 were utilized in the investigation. To measure the efficiency of the applied models, five statistical indices, i.e., coefficient of correlation, mean absolute error, root mean square error, relative absolute error, and root relative squared error were used in machine learning models. The results indicated that the artificial neural network outperformed other models in predicting the Marshall stability of modified asphalt mix with a higher value of the coefficient of correlation (0.8392), R2 (0.7042), a lower mean absolute error value (1.4996), and root mean square error value (1.8315) in the testing stage with small error band and provided the best optimal fit. Results of the feature importance analysis showed that the first five input variables, i.e., carbon fiber diameter, bitumen content, hybrid asphalt mix of glass-carbon fiber at 75:25 percent, carbon fiber content, and hybrid asphalt mix of glass-carbon fiber at 50:50 percent, are highly sensitive parameters which influence the Marshall strength of the modified asphalt mixes to a greater extent. Full article
(This article belongs to the Special Issue Functional Materials, Machine Learning, and Optimization)
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14 pages, 5088 KiB  
Article
Structural Morphology and Optical Properties of Strontium-Doped Cobalt Aluminate Nanoparticles Synthesized by the Combustion Method
by Sivaraman Kanithan, Natarajan Arun Vignesh, Siva Baskar, Santhosh Nagaraja, Mohamed Abbas, Abdul Aabid and Muneer Baig
Materials 2022, 15(22), 8180; https://doi.org/10.3390/ma15228180 - 17 Nov 2022
Cited by 2 | Viewed by 1310
Abstract
The study of structural morphology and the optical properties of nanoparticles produced by combustion methods are gaining significance due to their multifold applications. In this regard, in the present work, the strontium-doped cobalt aluminate nanoparticles were synthesized by utilizing Co1−xSr [...] Read more.
The study of structural morphology and the optical properties of nanoparticles produced by combustion methods are gaining significance due to their multifold applications. In this regard, in the present work, the strontium-doped cobalt aluminate nanoparticles were synthesized by utilizing Co1−xSrxAl2O4 (0 ≤ x ≤ 0.5) L-Alanine as a fuel in an ignition cycle. Subsequently, several characterization studies viz., X-ray diffraction (XRD), energy-dispersive X-ray (EDX) analysis, high-resolution scanning electron microscopy (HRSEM), Fourier transform infrared spectroscopy (FTIR), ultraviolet (UV) spectroscopy and vibrating sample magnetometry (VSM) were accomplished to study the properties of the materials. The XRD analysis confirmed the cubic spinel structure, and the average crystallite size was found to be in the range of 14 to 20 nm using the Debye–Scherrer equation. High-resolution scanning electron microscopy was utilized to inspect the morphology of the Co1−xSrxAl2O4 (0 ≤ x ≤ 0.5) nanoparticles. Further, EDS studies were accomplished to determine the chemical composition. Kubelka–Munk’s approach was used to determine the band gap, and the values were found to be in the range of 3.18–3.32 eV. The energy spectra for the nanoparticles were in the range of 560–1100 cm−1, which is due to the spinel structure of Sr-doped CoAl2O4 nanoparticles. The behavior plots of magnetic induction (M) against the magnetic (H) loops depict the ferromagnetic behavior of the nanomaterials synthesized. Full article
(This article belongs to the Special Issue Functional Materials, Machine Learning, and Optimization)
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16 pages, 21140 KiB  
Article
Response Surface Methodology Based Optimization of Test Parameter in Glass Fiber Reinforced Polyamide 66 for Dry Sliding, Tribological Performance
by Narendran Jagadeesan, Anthoniraj Selvaraj, Santhosh Nagaraja, Mohamed Abbas, C. Ahamed Saleel, Abdul Aabid and Muneer Baig
Materials 2022, 15(19), 6520; https://doi.org/10.3390/ma15196520 - 20 Sep 2022
Cited by 4 | Viewed by 1114
Abstract
The tribological performance of a glass fiber reinforced polyamide66 (GFRPA66) composite with varying fiber weight percentage (wt.%) [30 wt.% and 35 wt.%] is investigated in this study using a pin-on-disc tribometer. GFRPA66 composite specimens in the form of pins with varying percentages of [...] Read more.
The tribological performance of a glass fiber reinforced polyamide66 (GFRPA66) composite with varying fiber weight percentage (wt.%) [30 wt.% and 35 wt.%] is investigated in this study using a pin-on-disc tribometer. GFRPA66 composite specimens in the form of pins with varying percentages of fiber viz., 30 wt.% and 35 wt.% are fabricated by an injection molding process. Tribological performances, such as coefficient of friction (COF) and the specific wear rate (SWR), are investigated. The factors affecting the wear of GFRPA66 composites [with 30 wt.% and 35 wt.% reinforcements] are identified based on the process parameters such as load, sliding velocity, and sliding distance. Design Expert 13.0 software is used for the experimental data analysis, based on the design of experiments planned in accordance with the central composite design (CCD) of the response surface methodology (RSM) technique. The significance of the obtained results are analyzed using analysis of variance (ANOVA) techniques. To attain minimum SWR and COF, the wear performance is optimized in dry sliding conditions. The analysis of experimental data revealed that SWR and COF increased with increasing load, sliding velocity, and sliding distance for GFRPA66 [30 wt.%], but decreased with increasing polyamide weight percentage. The SWR for a maximum load of 80 N, and for a sliding velocity of 0.22 m/s, and a sliding distance of 3500 m for GFRPA66 composite specimens with 30 wt.% reinforcements are found to be 0.0121 m3/Nm, while the SWR for the same set of parameters for GFRPA66 composite specimens with 35 wt.% reinforcements are found to be 0.0102 m3/Nm. The COF for the GFRPA66 composite specimens with 30 wt.% reinforcements for the above set of parameters is found to be 0.37, while the GFRPA66 composite specimens with 35 wt.% reinforcements showed significant improvement in wear performance with a reduction in COF to 0.25. Finally, using a scanning electron microscope (SEM), the worn surfaces of the GFRPA66 are examined and interpreted. Full article
(This article belongs to the Special Issue Functional Materials, Machine Learning, and Optimization)
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22 pages, 3476 KiB  
Article
Machine Learning Techniques for Evaluating Concrete Strength with Waste Marble Powder
by Nitisha Sharma, Mohindra Singh Thakur, Parveen Sihag, Mohammad Abdul Malik, Raj Kumar, Mohamed Abbas and Chanduveetil Ahamed Saleel
Materials 2022, 15(17), 5811; https://doi.org/10.3390/ma15175811 - 23 Aug 2022
Cited by 15 | Viewed by 1631
Abstract
The purpose of the research is to predict the compressive and flexural strengths of the concrete mix by using waste marble powder as a partial replacement of cement and sand, based on the experimental data that was acquired from the laboratory tests. In [...] Read more.
The purpose of the research is to predict the compressive and flexural strengths of the concrete mix by using waste marble powder as a partial replacement of cement and sand, based on the experimental data that was acquired from the laboratory tests. In order to accomplish the goal, the models of Support vector machines, Support vector machines with bagging and Stochastic, Linear regression, and Gaussian processes were applied to the experimental data for predicting the compressive and flexural strength of concrete. The effectiveness of models was also evaluated by using statistical criteria. Therefore, it can be inferred that the gaussian process and support vector machine methods can be used to predict the respective outputs, i.e., flexural and compressive strength. The Gaussian process and Support vector machines Stochastic predicts better outcomes for flexural and compressive strength because it has a higher coefficient of correlation (0.8235 and 0.9462), lower mean absolute and root mean squared error values as (2.2808 and 1.8104) and (2.8527 and 2.3430), respectively. Results suggest that all applied techniques are reliable for predicting the compressive and flexural strength of concrete and are able to reduce the experimental work time. In comparison to input factors for this data set, the number of curing days followed by the CA, C, FA, w, and MP is essential in predicting the flexural and compressive strength of a concrete mix for this data set. Full article
(This article belongs to the Special Issue Functional Materials, Machine Learning, and Optimization)
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25 pages, 6025 KiB  
Article
Strength Predictive Modelling of Soils Treated with Calcium-Based Additives Blended with Eco-Friendly Pozzolans—A Machine Learning Approach
by Eyo U. Eyo, Samuel J. Abbey and Colin A. Booth
Materials 2022, 15(13), 4575; https://doi.org/10.3390/ma15134575 - 29 Jun 2022
Cited by 7 | Viewed by 1827
Abstract
The unconfined compressive strength (UCS) of a stabilised soil is a major mechanical parameter in understanding and developing geomechanical models, and it can be estimated directly by either lab testing of retrieved core samples or remoulded samples. However, due to the effort, high [...] Read more.
The unconfined compressive strength (UCS) of a stabilised soil is a major mechanical parameter in understanding and developing geomechanical models, and it can be estimated directly by either lab testing of retrieved core samples or remoulded samples. However, due to the effort, high cost and time associated with these methods, there is a need to develop a new technique for predicting UCS values in real time. An artificial intelligence paradigm of machine learning (ML) using the gradient boosting (GB) technique is applied in this study to model the unconfined compressive strength of soils stabilised by cementitious additive-enriched agro-based pozzolans. Both ML regression and multinomial classification of the UCS of the stabilised mix are investigated. Rigorous sensitivity-driven diagnostic testing is also performed to validate and provide an understanding of the intricacies of the decisions made by the algorithm. Results indicate that the well-tuned and optimised GB algorithm has a very high capacity to distinguish between positive and negative UCS categories (‘firm’, ‘very stiff’ and ‘hard’). An overall accuracy of 0.920, weighted recall rates and precision scores of 0.920 and 0.938, respectively, were produced by the GB model. Multiclass prediction in this regard shows that only 12.5% of misclassified instances was achieved. When applied to a regression problem, a coefficient of determination of approximately 0.900 and a mean error of about 0.335 were obtained, thus lending further credence to the high performance of the GB algorithm used. Finally, among the eight input features utilised as independent variables, the additives seemed to exhibit the strongest influence on the ML predictive modelling. Full article
(This article belongs to the Special Issue Functional Materials, Machine Learning, and Optimization)
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18 pages, 2501 KiB  
Article
A Pragmatic Transfer Learning Approach for Oxygen Vacancy Formation Energies in Oxidic Ceramics
by Xiaoyan Yin, Robert Spatschek, Norbert H. Menzler and Claas Hüter
Materials 2022, 15(8), 2879; https://doi.org/10.3390/ma15082879 - 14 Apr 2022
Viewed by 1263
Abstract
Lower oxygen vacancy formation energy is one of the requirements for air electrode materials in solid oxide cells applications. We introduce a transfer learning approach for oxygen vacancy formation energy prediction for some ABO3 perovskites from a two-species-doped system to four-species-doped system. [...] Read more.
Lower oxygen vacancy formation energy is one of the requirements for air electrode materials in solid oxide cells applications. We introduce a transfer learning approach for oxygen vacancy formation energy prediction for some ABO3 perovskites from a two-species-doped system to four-species-doped system. For that, an artificial neural network is used. Considering a two-species-doping training data set, predictive models are trained for the determination of the oxygen vacancy formation energy. To predict the oxygen vacancy formation energy of four-species-doped perovskites, a formally similar feature space is defined. The transferability of predictive models between physically similar but distinct data sets, i.e., training and testing data sets, is validated by further statistical analysis on residual distributions. The proposed approach is a valuable supporting tool for the search for novel energy materials. Full article
(This article belongs to the Special Issue Functional Materials, Machine Learning, and Optimization)
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16 pages, 3505 KiB  
Article
XAS Data Preprocessing of Nanocatalysts for Machine Learning Applications
by Oleg O. Kartashov, Andrey V. Chernov, Dmitry S. Polyanichenko and Maria A. Butakova
Materials 2021, 14(24), 7884; https://doi.org/10.3390/ma14247884 - 20 Dec 2021
Cited by 10 | Viewed by 3260
Abstract
Innovative development in the energy and chemical industries is mainly dependent on advances in the accelerated design and development of new functional materials. The success of research in new nanocatalysts mainly relies on modern techniques and approaches for their precise characterization. The existing [...] Read more.
Innovative development in the energy and chemical industries is mainly dependent on advances in the accelerated design and development of new functional materials. The success of research in new nanocatalysts mainly relies on modern techniques and approaches for their precise characterization. The existing methods of experimental characterization of nanocatalysts, which make it possible to assess the possibility of using these materials in specific chemical reactions or applications, generate significant amounts of heterogeneous data. The acceleration of new functional materials, including nanocatalysts, directly depends on the speed and quality of extracting hidden dependencies and knowledge from the obtained experimental data. Usually, such experiments involve different characterization techniques and different types of X-ray absorption spectroscopy (XAS) too. Using the machine learning (ML) methods based on XAS data, we can study and predict the atomic-scale structure and another bunch of parameters for the nanocatalyst efficiently. However, before using any ML model, it is necessary to make sure that the XAS raw experimental data is properly pre-processed, cleared, and prepared for ML application. Usually, the XAS preprocessing stage is vaguely presented in scientific studies, and the main efforts of researchers are devoted to the ML description and implementation stage. However, the quality of the input data influences the quality of ML analysis and the prediction results used in the future. This paper fills the gap between the stage of obtaining XAS data from synchrotron facilities and the stage of using and customizing various ML analysis and prediction models. We aimed this study to develop automated tools for the preprocessing and presentation of data from physical experiments and the creation of deposited datasets on the basis of the example of studying palladium-based nanocatalysts using synchrotron radiation facilities. During the study, methods of preliminary processing of XAS data were considered, which can be conditionally divided into X-ray absorption near edge structure (XANES) and extended X-ray absorption fine structure (EXAFS). This paper proposes a software toolkit that implements data preprocessing scenarios in the form of a single pipeline. The main preprocessing methods used in this study proposed are principal component analysis (PCA); z-score normalization; the interquartile method for eliminating outliers in the data; as well as the k-means machine learning method, which makes it possible to clarify the phase of the studied material sample by clustering feature vectors of experiments. Among the results of this study, one should also highlight the obtained deposited datasets of physical experiments on palladium-based nanocatalysts using synchrotron radiation. This will allow for further high-quality data mining to extract new knowledge about materials using artificial intelligence methods and machine learning models, and will ensure the smooth dissemination of these datasets to researchers and their reuse. Full article
(This article belongs to the Special Issue Functional Materials, Machine Learning, and Optimization)
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8 pages, 5694 KiB  
Article
Nuclear Radiation Shielding Characteristics of Some Natural Rocks by Using EPICS2017 Library
by Mohammed Sultan Al-Buriahi, M. I. Sayyed, Rashad A. R. Bantan and Yas Al-Hadeethi
Materials 2021, 14(16), 4669; https://doi.org/10.3390/ma14164669 - 19 Aug 2021
Cited by 19 | Viewed by 2123
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)
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21 pages, 5273 KiB  
Article
Influence of Li2O Incrementation on Mechanical and Gamma-Ray Shielding Characteristics of a TeO2-As2O3-B2O3 Glass System
by Aljawhara H. Almuqrin, Mohamed Y. Hanfi, M. I. Sayyed, K. G. Mahmoud, Hanan Al-Ghamdi and Dalal Abdullah Aloraini
Materials 2021, 14(14), 4060; https://doi.org/10.3390/ma14144060 - 20 Jul 2021
Cited by 2 | Viewed by 1912
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)
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25 pages, 11181 KiB  
Article
Investigation of Mechanical and Physical Properties of Big Sheep Horn as an Alternative Biomaterial for Structural Applications
by Tajammul Hussain M. Mysore, Arun Y. Patil, G. U. Raju, N. R. Banapurmath, Prabhakar M. Bhovi, Asif Afzal, Sagr Alamri and C Ahamed Saleel
Materials 2021, 14(14), 4039; https://doi.org/10.3390/ma14144039 - 20 Jul 2021
Cited by 27 | Viewed by 4105
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)
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12 pages, 4599 KiB  
Article
The Role of La2O3 in Enhancement the Radiation Shielding Efficiency of the Tellurite Glasses: Monte-Carlo Simulation and Theoretical Study
by Aljawhara H. Almuqrin, Mohamed Hanfi, K. G. Mahmoud, M. I. Sayyed, Hanan Al-Ghamdi and Dalal Abdullah Aloraini
Materials 2021, 14(14), 3913; https://doi.org/10.3390/ma14143913 - 13 Jul 2021
Cited by 12 | Viewed by 1514
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)
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21 pages, 15230 KiB  
Article
Synthesis and Characterization of Mechanical Properties and Wire Cut EDM Process Parameters Analysis in AZ61 Magnesium Alloy + B4C + SiC
by Thanikodi Sathish, Vinayagam Mohanavel, Khalid Ansari, Rathinasamy Saravanan, Alagar Karthick, Asif Afzal, Sagr Alamri and C. Ahamed Saleel
Materials 2021, 14(13), 3689; https://doi.org/10.3390/ma14133689 - 01 Jul 2021
Cited by 44 | Viewed by 2810
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)
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13 pages, 2440 KiB  
Article
Mechanical and Gamma Ray Absorption Behavior of PbO-WO3-Na2O-MgO-B2O3 Glasses in the Low Energy Range
by Aljawhara H. Almuqrin, Badriah Albarzan, O. I. Olarinoye, Ashok Kumar, Norah Alwadai and M. I. Sayyed
Materials 2021, 14(13), 3466; https://doi.org/10.3390/ma14133466 - 22 Jun 2021
Cited by 16 | Viewed by 1398
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)
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24 pages, 10344 KiB  
Article
Multi Ceramic Particles Inclusion in the Aluminium Matrix and Wear Characterization through Experimental and Response Surface-Artificial Neural Networks
by Ballupete Nagaraju Sharath, Channarayapattana Venkataramaiah Venkatesh, Asif Afzal, Navid Aslfattahi, Abdul Aabid, Muneer Baig and Bahaa Saleh
Materials 2021, 14(11), 2895; https://doi.org/10.3390/ma14112895 - 28 May 2021
Cited by 56 | Viewed by 2948
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)
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14 pages, 18638 KiB  
Article
Determination of Non-Recrystallization Temperature for Niobium Microalloyed Steel
by Mohammad Nishat Akhtar, Muneer Khan, Sher Afghan Khan, Asif Afzal, Ram Subbiah, Sheikh Nazir Ahmad, Murtuja Husain, Mohammad Mursaleen Butt, Abdul Rahim Othman and Elmi Abu Bakar
Materials 2021, 14(10), 2639; https://doi.org/10.3390/ma14102639 - 18 May 2021
Cited by 30 | Viewed by 3293
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)
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19 pages, 13685 KiB  
Article
Influence of the Fly Ash Material Inoculants on the Tensile and Impact Characteristics of the Aluminum AA 5083/7.5SiC Composites
by Santhosh Nagaraja, Kempaiah Ujjaini Nagegowda, Anand Kumar V, Sagr Alamri, Asif Afzal, Deepak Thakur, Abdul Razak Kaladgi, Satyam Panchal and Ahamed Saleel C
Materials 2021, 14(9), 2452; https://doi.org/10.3390/ma14092452 - 09 May 2021
Cited by 31 | Viewed by 2819
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)
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20 pages, 17556 KiB  
Article
Evaluation of the Infill Design on the Tensile Response of 3D Printed Polylactic Acid Polymer
by Tanner David Harpool, Ibrahim Mohammed Alarifi, Basheer A. Alshammari, Abdul Aabid, Muneer Baig, Rizwan Ahmed Malik, Ahmed Mohamed Sayed, Ramazan Asmatulu and Tarek Mohamed Ahmed Ali EL-Bagory
Materials 2021, 14(9), 2195; https://doi.org/10.3390/ma14092195 - 25 Apr 2021
Cited by 19 | Viewed by 3622
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)
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