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Publications (101)
The room temperature sodium-sulfur (RT/NaS) battery provides a potential energy storage technology with high theoretical capacity and low cost. However, the gap between its practical performance and theoretical expectation confines its comprehensive implementation. In this work, a simple annealing process successfully synthesized a hierarchical mic...
The machine learning (ML) approach, motivated by artificial intelligence (AI), is an inspiring mathematical algorithm that accurately simulates many engineering processes. Machine learning algorithms solve nonlinear and complex relationships through data training; additionally, they can infer previously unknown relationships, allowing for a simplif...
This present work uses artificial neural networks (ANNs) to examine the association between various dimensions of coaching leadership and turnover Intention. The coaching leadership data were collected from 194 employees across multiple schools in Korea. The ANN models are capable of higher predictive accuracy than conventional linear regression an...
Hydrokinetic turbines are the most efficient way to generate energy and electricity in hydropower applications. A hydrokinetic turbine’s operational characteristics and physical dimensions affect its efficiency. The relationship between the turbine’s geometric configuration and output is complicated and nonlinear. Thus, in the current work, a stand...
In this work, we report the SnO2 anode material in Na-ion batteries (SIBs) synthesized via hydrothermal in the presence of reduced graphene oxide (rGO). The precursors used in this method to prepare SnO2/[email protected] are hydrazine hydrate and ethylenediamine (ED). The dual strategy has been obtained from ED, which acts as a reducing agent as w...
Artificial neural networks (ANN) models are becoming more popular than mathematical and transport-based models due to their high performance and accuracy. Previous literature shows a lack of application of powerful ANN techniques for predicting forward osmosis (FO) performance. In this study, we developed a feedforward network to predict and analyz...
With the ever-growing demand for high energy and power density lithium-ion batteries (LIBs), tin (Sn) has been considered a capable anode material because of its high theoretical capacity (993.4 mAh/g). However, the practical application of Sn anodes suffers from low capacity retention due to significant volume expansion (~257%) and poor ion and el...
The γ-TiAl alloys fabricated by additive manufacturing have gathered significant attention in recent years due to their unique microstructural features and properties. However, the oxidation kinetics of additively manufactured γ-TiAl alloys have not been studied thoroughly. Herein, the TNM-B1 alloy was fabricated with the help of electron beam melt...
The artificial neural network (ANN) approach motivated by the biological nervous system is an inspiring mathematical tool that simulates many complicated engineering applications. ANN learn from data and model real-life nonlinear and complex relationships; they can infer hidden relationships, thus making a generalized model and predicting unseen da...
The impact of process factors on wire-cut electrical discharge machining (WEDM) performance is complex and nonlinear. In the present work, initially, the WEDM tests were conducted on titanium alloy (Ti-6Al-4V) with eight input factors and four machinability performance parameters. Later, an artificial neural network (ANN) model was established to e...
Inconel 718 nickel superalloys' superior properties at elevated temperatures necessitate several applications in the aviation, marine, and automotive industries. However, the poor thermal conductivity and rapid strain hardening properties of Inconel 718 led to poor machinability and machined surface integrity. The main purpose of this study is to p...
Removing decolorizing acid blue 113 (AB113) dye from textile wastewater is challenging due to its high stability and resistance to removal. In this study, we used an artificial neural network (ANN) model to estimate the effect of five different variables on AB113 dye removal in the sonophotocatalytic process. The five variables considered were reac...
Recently, the hybrid method has been developed in which wire and arc additive manufacturing (WAAM) use to produce the near net shape preform for the single-step hot forging process. The hybrid method overcomes the defects and anisotropic properties of WAAM processed preform and produce the net shape of the component with better mechanical propertie...
The effects of nanoprecipitations on the mechanical properties of Al-Zn-Mg-Cu alloys after GBF (gas bubbling filtration) and EMS (electromagnetic stirring) casting were investigated. Dendritic cell structures were formed after GBF processing, while globular dendritic structures were nucleated after EMS processing. Equiaxed cell sizes were smaller i...
This article uses the artificial neural networks (ANNs) method to investigate the association between various dimensions of demographic and coaching leadership with the job satisfaction of teachers in Korean schools. ANN models demonstrate a superior capability to model the relationship with higher predictive accuracy than multiple regression analy...
Pb(II) is a heavy metal that is a prominent contaminant in water contamination. Among the different pollution removal strategies, adsorption was determined to be the most effective. The adsorbent and its type determine the adsorption process's efficiency. As part of this effort, a magnetic reduced graphene oxide-based inverse spinel nickel ferrite...
Arsenic contamination is a global problem, as it affects the health of millions of people. For this study, data-driven artificial neural network (ANN) software was developed to predict and validate the removal of As(V) from an aqueous solution using graphene oxide (GO) under various experimental conditions. A reliable model for wastewater treatment...
AISI 52100 steel machining has drawn a greater interest in industrial and manufacturing applications due to its high strength, sublime hardness, and impressive wear resistance. Conventional cutting fluid-assisted machining is an objectionable option owing to its threat to the environment and operators. Dry machining or near dry machining is the pri...
Direct energy deposited (DED) Ti-5Ni (wt.%) alloys promote significant grain refinement with fine equiaxed grains (~30–50 μm); they have discontinuous grain boundary α, fine eutectoid α laths (~1 μm width), and α + Ti2Ni phases. Post-heat treatment below beta transus for 24 h stimulates the formation of near equiaxed α, and the temperature signific...
Transformation-induced plasticity (TRIP) Ti alloys are promising structural materials that offer high strength and ductility. However, these alloys often include heavy, expensive, and high-melting-point β-stabilizing elements such as V, Nb, Mo, and W. Herein, an artificial neural network (ANN) was used to develop a Ti–Al–Fe–Mn-based TRIP alloy comp...
To improve the quality and productivity of the process or system before resorting to expensive and laborious experimental tests, it is essential to model and predict the system performance concerning its operational parameters. Predictive modeling and parameter optimization through machine learning techniques has been the most advantageous process...
AlCoCrFeNi high entropy alloy (HEA) was coated on AISI410 stainless steel through the electro spark deposition. Fine melt droplets in the arc-zone led to fine dendrites in the coating. Coated phase contained body centered cubic with a minor fraction of face centered cubic phase. Constituent elements from HEA were diffused in the substrate and led t...
Direct energy deposition (DED) is a highly applicable additive manufacturing (AM) method and, therefore, widely employed in industrial repair-based applications to fabricate defect-free and high degree precision components. To obtain high-quality products by using DED, it is necessary to understand the influence of the process parameters on the pro...
The ideal sulfur supporting material for room-temperature sodium-sulfur (RT-NaS) batteries would concurrently incorporate adsorption capabilities, and high-electrical conductivity, which are essential for improving cycling stability and crucial for enhancing cycling stability and implementation in large-scale applications. In this work, the layered...
The current work implements machine learning techniques such as artificial neural network (ANN), support vector machine (SVM), and genetic algorithm (GA) to model and optimize the surface roughness during wire electrical discharge machining (WEDM) of Inconel 718. For this, surface roughness values were obtained from real-time WEDM experiments condu...
Ti–6Al–4V alloy is a typical 3D printing metal, and its application has been expanded to various fields owing to its excellent characteristics such as high specific strength, high corrosion resistance, and biocompatibility. In particular, direct energy deposition (DED) has been actively explored in the fields of deposition and the repair of large t...
The room temperature sodium-sulfur batteries are an attraction to worldwide industrial and academic as a next-generation energy storage system due to the high energy density, theoretical capacity, and cheap cost of sulfur. However, the practical application is being overdue by fast decay, poor conductivity, and the shuttle effect attributed to the...
ANN model developed for modeling biosorption process to treat sewage wastewater. • A standalone ANN software developed for easy operation. • The input-output relationship is predicted by performing sensitivity analysis. • The proposed Virtual system quantitatively estimates metal removal from wastewater. Editor: Frederic Coulon Keywords: Artificial...
In the present work, we developed an artificial neural networks (ANN) model to predict and analyze the polycaprolactone fiber diameter as a function of 3D melt electrospinning process parameters. A total of 35 datasets having various combinations of electrospinning writing process variables (collector speed, tip to nozzle distance, applied pressure...
Heavy metal ions in aqueous solutions are taken into account as one of the most harmful environmental issues that ominously affect human health. Pb(II) is a common pollutant among heavy metals found in industrial wastewater, and various methods were developed to remove the Pb(II). The adsorption method was more efficient, cheap, and eco-friendly to...
Al2Y4-xO9:xTb³⁺ luminescent powder phosphors were synthesized by the sol-gel method. The prepared samples were characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier transform infrared spectroscopy, photoluminescence (PL), and CIE techniques. The microstructural and structural analyses of samples confirmed the form...
Celestite and barite formation results in contamination of barium and strontium ions hinder oilfield water purification. Conversion of bio-waste sorbent products deals with a viable, sustainable and clean remediation approach for removing contaminants. Biochar sorbent produced from rice straw was used to remove barium and strontium ions of saline w...
Lithium-sulfur (Li-S) batteries are attractive and prominent power sources du e to high theoretical capacity and the availability of sulfur at a low price. However, sulfur has limitations such as the forma tion of polysul-fides and low condu ctivity. To overcome these problems , we prepared a ch eese-like carbon (CLC) using a simple annealing proce...
Electrospun polycaprolactone (PCL) scaffolds are broadly used in tissue engineering applications due to their superior biomechanical properties and compatibility with the cell matrix. The properties of PCL scaffolds depend on electrospinning parameters. The relationships between electrospinning process parameters and scaffold properties are complic...
Electrospun polycaprolactone (PCL) scaffolds are broadly used in tissue engineering applications due to their superior biomechanical properties and compatibility with the cell matrix. The properties of PCL scaffolds depend on electrospinning parameters. The relationships between electrospinning process parameters and scaffold properties are complic...
Chemical composition affects the properties and the martensite start (Ms) temperature of steels. This study predicts the Ms temperature of high carbon steel via artificial neural networks. Meanwhile, it enables us to estimate the quantitative effect of alloying elements on the Ms temperature on a sizeable selectable scale, which is the first time t...
An artificial neural network method is proposed to correlate the relationship between the corrosion rate of 3C steels with seawater environment factors. The predictions with the unseen test data are in good agreement with experimental values. Further, the developed model used to simulate the combined effect of environmental factors (temperature, di...
This study shows an artificial neural network (ANN) model of chlorophenol rejection from aqueous solutions and predicting the performance of spiral wound reverse osmosis (SWRO) modules. This type of rejection shows complex non-linear dependencies on feed pressure, feed temperature, concentration, and feed flow rate. It provides a demanding test of...
Usage of cyclic volatile methyl siloxanes(cVMSs) in the industrial process is unavoidable due to their superior properties; however, it is hazardous to human health. Photocatalytic zinc oxide coated aluminum anode is used to degrade the cVMSs in wastewater. In this work, we investigated the relationship among degradation process parameters such as...
Past two decades, the usage of ceramic tools has increased especially in milling and turning process. These advanced ceramic tools have good characteristics that are capable in maintaining high hardness in temperatures and also wears much slower when compared to carbide tools. With limited data available on the tool itself, research is to be done o...
Owing to the speculated price hike and scarcity of lithium resources, sodium-ion batteries are attracting significant research interest these days. However, sodium-ion battery anodes do not deliver good electrochemical performance, particularly rate performance. Herein, we report the facile electrospinning synthesis of a free-standing nickel disulf...
Present work was aimed to develop an artificial neural networks (ANN) model to predict the polysaccharide‐based biopolymer (Hylon VII starch) nanofiber diameter and classification of its quality (good, fair, and poor) as a function of polymer concentration, spinning distance, feed rate, and applied voltage during the electrospinning process. The re...
Application of artificial neural network (ANN) in process modelling and parameter optimization has become quite obvious because of its capability to predict the output quickly and precisely. The current study attempts to model and predict the surface roughness in wire electrical discharge machining (WEDM) of Inconel 718 using artificial neural netw...
To develop the next-generation energy storage systems, lithium-sulfur batteries represent an attractive option due to its high theoretical capacity, and energy density. In this work, MoS 2 /rGO (reduced graphene oxide) was prepared by hydrothermal synthesis and sulfur added by the melt diffusion method. The as-prepared MoS 2 /rGO has strong polysul...
In this work, the hydrothermal method was employed to produce SnO 2 /rGO as anode material. Nanostructured SnO 2 was prepared to enhance reversibility and to deal with the undesirable volume changes during cycling. The SnO 2 /rGO hybrid exhibits long cycle life in lithium-ion storage capacity and rate capability with an initial discharge capacity o...
An artificial neural network (ANN) model was designed to correlate the complex relations among composition, temperature, and mechanical properties of 18Cr-12Ni-Mo austenitic stainless steels. The developed model was used to estimate the composition-property and temperature-property correlations with 97% and 91% accuracy, for train and unseen test d...
Production of hydrogen rich syngas is one of the industrial important reactions as a feedstock for many energy applications. This reactor has environmental benefit as it consume CO₂ which is hazardous and creates globe warming. The most economical way to produce syngas is through transformations of hydrocarbons by several reforming process by both...
Artificial intelligence is widely employed in metallurgy for its ability to solve complex phenomena, which are associated with the learning process of previously obtained experimental data. Although numerous physical modeling techniques have been implemented for the prediction of mechanical strength using equations, several empirical efforts are ne...
Samarium (Sm³⁺) doped sodium calcium silicate (Na2CaSiO4: NCS) phosphors have been synthesized by employing a sol-gel technique. The structural, morphological, excitation and emission spectral studies have been carried out for the as-prepared phosphors. The phase purity in synthesized NCS phosphor with cubic structure has been confirmed by X-ray di...
The purpose of this study is to develop an artificial neural network (ANN) model to predict and analyze the relationship between properties and process parameters of polyvinyl chloride (PVC) composites. The tensile strength, ductility, and density of PVC are modeled as a function of virgin PVC, recycled PVC, CaCO3, di‐2‐ethylhexyl phthalate, chlori...
The relationship between the fiber diameter and electrospinning process variables is complicated and nonlinear. In this study, we developed an artificial neural network model to correlate the relationships between the elec-trospinning process variables (voltage, flow rate, distance, and collector rotating speed) and the fiber diameter of Ferrofluid...
From the point of view of designing materials, it is important to study the complex correlational research that involves measuring several variables and assessing the relation among them. Hence, the notion of machine-oriented data modeling is explored. Among various machine-learning tools, artificial neural networks (ANN) have been used as a stimul...
The present study describes the influence of β stabilizers (namely Fe and Cr) on the microstructural evolution and mechanical behavior of Ti–6Al–4V alloy fabricated by direct energy deposition. As the β stabilizer content increases from 1 to 4 wt%, the coarse columnar-grained morphology typical of additively manufactured Ti–6Al–4V is significantly...
Unfortunately, the acknowledgements were incomplete in the original version of this article.
Ti-2Al-9.2Mo-2Fe is a low-cost β titanium alloy with well-balanced strength and ductility, but hot working of this alloy is complex and unfamiliar. Understanding the nonlinear relationships among the strain, strain rate, temperature, and flow stress of this alloy is essential to optimize the hot working process. In this study, a deep neural network...
A nearly fully dense β stabilized γ-TiAl alloy was additively manufactured by electron beam melting. The as-fabricated specimen exhibited a fine structure consisting of α 2 /γ colonies (<5 μm), equiaxed γ (1 μm) and β o (<1 μm) grains. The unique microstructure rendered high strength (580 MPa) and high ductility (>50%) in the sample at 800 °C. Anne...
The isothermal compression tests were carried out to study the hot deformation behavior and microstructure evolution of Ti–19Al–22Mo alloy. The samples were deformed in the temperature range from 1100 to 1250 °C with an interval of 50 °C, strain rate ranging from 0.01 to 1 s⁻¹ and the height reduction of 50% using Gleeble-3800 thermal–mechanical si...
An accurate processing map for a metal provides a means of attaining a desired microstructure and required shape through thermo-mechanical processing. To construct such a map, the isothermal flow stress, σiso, is required. Conventionally, the non-isothermal flow stress measured by experiment is corrected to σiso using whole-temperature-range linear...
The present study focused on estimating the complex nonlinear relationship between the composition and phase transformation temperatures of Ti–Ni–Pd shape memory alloys by artificial neural networks (ANN). The ANN models were developed by using the experimental data of Ti–Ni–Pd alloys. It was found that the predictions are in good agreement with th...
Commonly used high-temperature near-alpha titanium alloys contain Al, Zr and Si as their alloying elements. Significant losses of mechanical properties and cleavage mode failures are evident due to the presence of Ti3Al and S2 types of zirconium silicides ((TiZr)6Si3) in these alloys. We developed a new alloy (Ti-6.5Al-3.0Sn-4.0Hf-0.2Nb-0.4Mo-0.4Si...
To determine the effect of electrolyte salts on the cycling properties of tin anodes in sodium ion batteries, sodium/tin cells were prepared using eight electrolytes containing NaCF3SO3, NaBF4, NaClO4, and NaPF6 in ethylene carbonate-dimethyl carbonate (EC-DMC) and EC-DMC/fluoroethylene carbonate (FEC) solvents. The first charge capacity and cyclin...
Nanohybrid materials have emerged as effective adsorbents for the removal of contaminants from the polluted water bodies. In this study we report two new hybrid materials as adsorbents for methylene blue from its aqueous solutions. Nanohybrid materials were prepared from methacrylic acid and methyl methacrylate or 2-hydroxyproypl methacrylate by em...
Large surface area, high porosity and good mechanical strength are some of the attributes of the polymer–inorganic hybrid materials those make these attractive candidates for use as adsorbents. New silica/titania–based polymer–inorganic hybrid material was synthesized via the sol–gel process. While SiO2 and TiO2 were used as the inorganic component...
Synthesis of immobilized enzymes via crosslinking is an easy route to develop biocatalyst with enhanced activity and recyclability. In the present study, cellulase from Aspergillus niger was crosslinked by ethylene glycol dimethacrylate (EGDMA) using ammonium persulphate (APS) as an initiator to obtain heat and pH stable crosslinked cellulase aggre...
Relationship between the electrospun fiber diameters of poly(methyl methacrylate) (PMMA) nanofibers with process parameters are complex and nonlinear. We used artificial neural networks technique to estimate the electrospun PMMA nanofiber diameter as a function of polymer concentration, nozzle-collector distance, temperature, flow rate, and voltage...
Three oxide dispersion-strengthened (ODS) steels are produced in order to investigate the effect of the mechanical alloying (MA) temperature on the microstructural evolution and high temperature mechanical properties. The microstructural evolution with different MA conditions is examined using small angle neutron scattering. As the MA temperature d...
Three oxide dispersion-strengthened (ODS) steels are produced in order to investigate the effect of the mechanical alloying (MA) temperature on the microstructural evolution and high temperature mechanical properties. The microstructural evolution with different MA conditions is examined using small angle neutron scattering. As the MA temperature d...
