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The growing demand for Magnesium in the automotive and aviation industries has enticed the need to improve its corrosive properties. In this study, the WE43 magnesium alloys were friction stir welded (FSW) by varying the traverse speed. FSW eliminates defects such as liquefication cracking, expulsion, and voids in joints encountered frequently in fusion welding of magnesium alloys. The microstructural properties were scrutinized using light microscopy (LM) and scanning electron microscopy (SEM). Additionally, the elemental makeup of precipitates was studied using electron dispersive X-ray spectroscopy (EDS). The electrochemical behavior of specimens was evaluated by employing potentiodynamic polarization tests and was correlated with the microstructural properties. A defect-free weldment was obtained at a traverse and rotational speed of 100 mm/min and 710 rpm, respectively. All weldments significantly improved corrosion resistance compared to the base alloy. Moreover, a highly refined microstructure with redistribution/dissolution of precipitates was obtained. The grain size was reduced from 256 µm to around 13 µm. The corrosion resistance of the welded sample was enhanced by 22 times as compared to the base alloy. Hence, the reduction in grain size and the dissolution/distribution of secondary-phase particles within the Mg matrix are the primary factors for the enhancement of anti-corrosion properties. [ABSTRACT FROM AUTHOR]

In 2020, coronavirus (COVID-19) was declared a global pandemic and it remains prevalent today. A necessity to model the transmission of the virus has emerged as a result of COVID-19's exceedingly contagious characteristics and its rapid propagation throughout the world. Assessing the incidence of infection could enable policymakers to identify measures to halt the pandemic and gauge the required capacity of healthcare centers. Therefore, modeling the susceptibility, exposure, infection, and recovery in relation to the COVID-19 pandemic is crucial for the adoption of interventions by regulatory authorities. Fundamental factors, such as the infection rate, mortality rate, and recovery rate, must be considered in order to accurately represent the behavior of the pandemic using mathematical models. The difficulty in creating a mathematical model is in identifying the real model variables. Parameters might vary significantly across models, which can result in variations in the simulation results because projections primarily rely on a particular dataset. The purpose of this work was to establish a susceptible–exposed–infected–recovered (SEIR) model describing the propagation of the COVID-19 outbreak throughout the Kingdom of Saudi Arabia (KSA). The goal of this study was to derive the essential COVID-19 epidemiological factors from actual data. System dynamics modeling and design of experiment approaches were used to determine the most appropriate combination of epidemiological parameters and the influence of COVID-19. This study investigates how epidemiological variables such as seasonal amplitude, social awareness impact, and waning time can be adapted to correctly estimate COVID-19 scenarios such as the number of infected persons on a daily basis in KSA. This model can also be utilized to ascertain how stress (or hospital capacity) affects the percentage of hospitalizations and the number of deaths. Additionally, the results of this study can be used to establish policies or strategies for monitoring or restricting COVID-19 in Saudi Arabia. [ABSTRACT FROM AUTHOR]

Aluminium foams possess multi-functional properties and a low specific weight, making them one of the most suitable choices in the application domain of the automobile and aviation sectors, vibrating machining, and structural parts. Compared to traditional fabrication routes, friction stir processing (FSP) is gaining acceptance as it is a cost-effective, highly efficient, and innocuous process to fabricate the foam precursors from the bulk substrate. In the current study, FSP was utilized to develop a precursor with MgCO3 powder acting as the blowing agent. The FSP experiments were performed as per Taguchi's L8 orthogonal array. The precursor was heat treated in an electric furnace at a holding temperature of 650 °C for 10 min. After the post-heat treatments, this precursor resulted in a porous structure due to the evolution of CO2 gas from the composite. The simultaneous effect of tool rotation speed, traverse speed, and shoulder diameter was investigated on the pore size and porosity of the foam produced. The composite parameter "unit stirring" is found to be closely related to the processed zone (PZ) and volume processing rate of the processed zone, pore size, and the degree of porosity. The highest porosity of 16.67% was obtained with an average pore size of 10.5 µm. The largest pore size, 17.8 µm, was observed to be associated with a porosity of 14.40%. The analysis of the Kiviat plot revealed that the values of the PZ area and volume processing rate possess a polar symmetry with unit stirring. The pore size and pore density were both found to be symmetrically distributed about the unit stirring. [ABSTRACT FROM AUTHOR]

This study investigates self-propelled rotary tool (SPRT) performance in hard turning using 3D finite element (FE) models. The FE models developed in this study are based on coupled temperature-displacement analysis using an explicit time-integration scheme. The developed FE models can predict chip morphology, cutting forces, tool and workpiece stresses and temperatures. For model verification, hard turning experiments were conducted using an SPRT on AISI 4340 bars. Cutting forces and maximum tool–chip interface temperatures were recorded and compared with the model findings. The effects of different process parameters were analyzed and discussed using the developed FE models. The FE models were run with a central composite design (CCD-25) matrix with four input variables, i.e., the cutting speed, the feed rate, the depth of the cut and the inclination angle. Response surfaces based on the Gaussian process were generated for each performance variable in order to predict design points not available in the original design of the experiment matrix. An optimization study was carried out to minimize tool stress and temperature while setting limits for the material removal rate (MRR) and specific cutting energy for the process. Optimized processes were found with moderate cutting speeds and feed rates and high depths of cut and inclination angles. [ABSTRACT FROM AUTHOR]

Support structures play a significant role in all additive manufacturing (AM) processes. The type of supports, as well as their size, placement, and other characteristics, greatly determine how effectively and efficiently the AM process works. In order to reduce the amount of material and post-processing requirements, tree-like support structures are revolutionary support structures that have so far been employed in polymer AM and have shown good performance. However, they have not yet been investigated for metal AM processes. Therefore, this study aims to propose and optimize the tree-like support structures for additively manufactured metal (Ti6Al4V) overhangs. The overhang specimens are fabricated using Electron Beam Melting (EBM) with a variety of design and process parameters. The effect of design and process structure parameters on the performance of the support is evaluated and optimized experimentally. MOGA-ll is used to perform multi-objective optimization. The results have shown the feasibility of using tree-like support structures in metal AM. The findings of this study demonstrate how important it is to choose the proper minimum distance between rows in order to reduce support volume and support removal time. Furthermore, the most crucial factors in limiting the overhang deviation are the beam current and beam scanning speed. Additionally, the data demonstrate that lowering the beam current and raising the beam scanning speed significantly reduce deformation. Consequently, it is critical to find the right balance between beam current, beam scanning speed, minimum spacing between rows, and branch top diameters that can produce the lowest support volume, lowest support removal time, and least amount of deformation. [ABSTRACT FROM AUTHOR]

The performance of a self-propelled rotary carbide tool when cutting hardened steel is evaluated in this study. Although various models for evaluating tool wear in traditional (fixed) tools have been introduced and deployed, there have been no efforts in the existing literature to predict the progression of tool wear while employing self-propelled rotary tools. The work-tool geometric relationship and the empirical function are used to build a flank wear model for self-propelled rotary cutting tools. Cutting experiments are conducted on AISI 4340 steel, which has a hardness of 54–56 HRC, at various cutting speeds and feeds. The rate of tool wear is measured at various intervals of time. The constant in the proposed model is obtained using genetic programming. When experimental and predicted flank wear are examined, the established model is found to be competent in estimating the rate of rotary tool flank wear progression. [ABSTRACT FROM AUTHOR]

With the advent of the fourth industrial revolution, the application of artificial intelligence in the manufacturing domain is becoming prevalent. Maintenance is one of the important activities in the manufacturing process, and it requires proper attention. To decrease maintenance costs and to attain sustainable operational management, Predictive Maintenance (PdM) has become important in industries. The principle of PdM is forecasting the next failure; thus, the respective maintenance is scheduled before the predicted failure occurs. In the construction of maintenance management, facility managers generally employ reactive or preventive maintenance mechanisms. However, reactive maintenance does not have the ability to prevent failure and preventive maintenance does not have the ability to predict the future condition of mechanical, electrical, or plumbing components. Therefore, to improve the facilities' lifespans, such components are repaired in advance. In this paper, a PdM planning model is developed using intelligent methods. The developed method involves five main phases: (a) data cleaning, (b) data normalization, (c) optimal feature selection, (d) prediction network decision-making, and (e) prediction. Initially, the data pertaining to PdM are subjected to data cleaning and normalization in order to arrange the data within a particular limit. Optimal feature selection is performed next, to reduce redundant information. Optimal feature selection is performed using a hybrid of the Jaya algorithm and Sea Lion Optimization (SLnO). As the prediction values differ in range, it is difficult for machine learning or deep learning face to provide accurate results. Thus, a support vector machine (SVM) is used to make decisions regarding the prediction network. The SVM identifies the network in which prediction can be performed for the concerned range. Finally, the prediction is accomplished using a Recurrent Neural Network (RNN). In the RNN, the weight is optimized using the hybrid J-SLnO. A comparative analysis demonstrates that the proposed model can efficiently predict the future condition of components for maintenance planning by using two datasets—aircraft engine and lithium-ion battery datasets. [ABSTRACT FROM AUTHOR]

The quality of micro-features in various technologies is mostly affected by the choice of the micro-fabrication technique, which in turn results in several limitations with regard to materials, productivity, and cost. Laser beam micro-machining has a distinct edge over other non-traditional methods in terms of material choices, precision, shape complexity, and surface integrity. This study investigates the effect of laser fluence and pulse overlap while developing microchannels in alumina ceramic using an neodymium-doped yttrium aluminum garnet (Nd:YAG) laser. Microchannels 200 µm wide with different depths were machined using different laser peak fluence and pulse overlap (percentage of overlap between successive laser pulses) values. It was found that high pulse overlaps and fluences should be avoided as they give rise to V-shaped microchannels i.e., 100% bottom width errors. The optimal peak fluence range was found to be around 125–130 J/cm2 corresponding to 3–5 µm depth per scan. In addition, channels fabricated with moderate pulse overlap were found to be of good quality compared to low pulse overlaps. [ABSTRACT FROM AUTHOR]

Titanium aluminides qualify adequately for advanced aero-engine applications in place of conventional nickel based superalloys. The combination of high temperature properties and lower density gives an edge to the titanium aluminide alloys. Nevertheless, challenges remain on how to process these essentially intermetallic alloys in to an actual product. Electron Beam Melting (EBM), an Additive Manufacturing Method, can build complex shaped solid parts from a given feedstock powder, thus overcoming the shortcomings of the conventional processing techniques such as machining and forging. The amount of energy supplied by the electron beam has considerable influence on the final build quality in the EBM process. Energy input is decided by the beam voltage, beam scan speed, beam current, and track offset distance. In the current work, beam current and track offset were varied to reflect three levels of energy input. Microstructural and mechanical properties were evaluated for these samples. The microstructure gradually coarsened from top to bottom along the build direction. Whereas higher energy favored lath microstructure, lower energy tended toward equiaxed grains. Computed tomography analysis revealed a greater amount of porosity in low energy samples. In addition, the lack of bonding defects led to premature failure in the tension test of low energy samples. Increase in energy to a medium level largely cancelled out the porosity, thereby increasing the strength. However, this trend did not continue with the high energy samples. Electron microscopy and X-ray diffraction investigations were carried out to understand this non-linear behavior of the strength in the three samples. Overall, the results of this work suggest that the input energy should be considered primarily whenever any new alloy system has to be processed through the EBM route. [ABSTRACT FROM AUTHOR]

Porous metal structures have many benefits over fully dense structures for use in bio-implants. The designs of porous structures can be made more sophisticated by altering their pore volume and strut orientation. Porous structures made from biocompatible materials such as titanium and its alloys can be produced using electron-beam melting, and recent reports have shown the biocompatibility of titanium aluminide (γ-TiAl). In the present work, we produced porous γ-TiAl structures by electron-beam melting, incorporating varying pore volumes. To achieve this, the individual pore dimensions were kept constant, and only the strut thickness was altered. Thus, for the highest pore volume of ~77%, the struts had to be as thin as half a millimeter. To accomplish such fine struts, we used various beam currents and scan strategies. Microscopy showed that selecting a proper scan strategy was most important in producing these fine struts. Microcomputed tomography revealed no major gaps in the struts, and the fine struts displayed compressive stiffness similar to that of natural bone. The characteristics of these highly-porous structures suggest their promise for use in bio-implants. [ABSTRACT FROM AUTHOR]

Friction stir alloying (FSA) of commercially pure Cu with Ni, Zn, and Mg is implemented in the current study. Mechanical and microstructural aspects of the successfully fabricated alloy structure have been examined. Energy dispersive X-ray spectroscopy revealed a uniform distribution of alloying elements and coalescence at the atomic level. The compositional and grain size heterogeneity is managed in the stir zone, allowing for microstructural control with FSA. Thus, the present study is essential for the development of novel materials whose fabrication requires temperature well below the melting point of base metals. The alloying process is found to be accompanied by ultra-refined grains, with the smallest grain size being ~0.44 μm. The fabricated alloy managed to retain the FCC phase, and no brittle intermetallic compounds formed, according to X-ray diffraction. The fabricated alloy exhibits maximum and average microhardness enhancements of 18.4% and 6%, respectively. Tensile properties have also been investigated and correlated with microstructural morphology. A shift toward grain bimodality has also been documented, which is a highly sought-after property nowadays, especially to overcome the strength-ductility trade-off. [ABSTRACT FROM AUTHOR]

Data classification has been considered extensively in different fields, such as machine learning, artificial intelligence, pattern recognition, and data mining, and the expansion of classification has yielded immense achievements. The automatic classification of maintenance data has been investigated over the past few decades owing to its usefulness in construction and facility management. To utilize automated data classification in the maintenance field, a data classification model is implemented in this study based on the analysis of different mechanical maintenance data. The developed model involves four main steps: (a) data acquisition, (b) feature extraction, (c) feature selection, and (d) classification. During data acquisition, four types of dataset are collected from the benchmark Google datasets. The attributes of each dataset are further processed for classification. Principal component analysis and first-order and second-order statistical features are computed during the feature extraction process. To reduce the dimensions of the features for error-free classification, feature selection was performed. The hybridization of two algorithms, the Whale Optimization Algorithm (WOA) and Spotted Hyena Optimization (SHO), tends to produce a new algorithm—i.e., a Spotted Hyena-based Whale Optimization Algorithm (SH-WOA), which is adopted for performing feature selection. The selected features are subjected to a deep learning algorithm called Recurrent Neural Network (RNN). To enhance the efficiency of conventional RNNs, the number of hidden neurons in an RNN is optimized using the developed SH-WOA. Finally, the efficacy of the proposed model is verified utilizing the entire dataset. Experimental results show that the developed model can effectively solve uncertain data classification, which minimizes the execution time and enhances efficiency. [ABSTRACT FROM AUTHOR]

Nickel–Titanium (NiTi)-based shape-memory alloys (SMA) are utilized in automotive, biomedical, microsystem applications because of their excellent shape memory effect, biocompatibility and super elastic properties. These alloys are considered difficult to cut—especially with conventional technologies because of the work hardening and residual stresses. Laser-machining is one of the most effective tools for processing of these alloys especially for microsystem applications. In this work, a thorough investigation of effect of process parameters on machining of microchannels in NiTi SMA is presented. In addition, a multi-objective optimization is carried out in order to find the optimal input parameter settings for the desired output performances. The results show that the quality of microchannels is significantly affected by input parameters. Layer thickness was found to have a significant effect on taper angle of the microchannel. Scan speed, layer thickness and scan strategy were found to have significant effects on both spatter thickness and top-width error, but in opposite directions. The multi-objective optimization-minimizing taper angle and spatter thickness revealed an optimal solution that was characterized by high frequency, moderate speed and low layer-thickness and track displacement. [ABSTRACT FROM AUTHOR]

Ceramic microchannels have important applications in different microscale systems like microreactors, microfluidic devices and microchemical systems. However, ceramics are considered difficult to manufacture owing to their wear and heat resistance capabilities. In this study, microchannels are developed in alumina ceramic using direct Nd:YAG laser writing. The laser beam with a characteristic pulse width of 10 µs and a beam spot diameter of 30 µm is used to make 200 µm width microchannels with different depths. The effects of laser beam intensity and pulse overlaps on dimensional accuracy and material removal rate have been investigated using different scanning patterns. It is found that beam intensity has a major influence on dimensional accuracy and material removal rate. Optimum parameter settings are found using grey relational grade analysis. It is concluded that low intensity and low to medium pulse overlap should be used for better dimensional accuracy. This study facilitates further understanding of laser material interaction for different process parameters and presents optimum laser process parameters for the fabrication of microchannel in alumina ceramic. [ABSTRACT FROM AUTHOR]

This study aims to investigate the manufacturability of overhang round holes with and without support. The experiments were conducted by electron beam melting (EBM) using a Ti6Al4V powder. A large number of overhanging holes with and without support were fabricated and evaluated. The geometrical accuracy, mechanical properties, and microstructures were utilized as a measure of the process performance. It was demonstrated that overhanging features can be built successfully without support up to a certain dimension (or threshold value). Beyond that value, a minimal support structure can be employed to achieve the most suitable trade-off between production time, cost, and accuracy. [ABSTRACT FROM AUTHOR]

Applications of non-ferrous light metal alloys are especially popular in the field of aerospace. Hence it is important to investigate their properties in joining processes such as welding. Solid state joining process such as friction stir welding (FSW) is quite efficient for joining non-ferrous alloys, but with thick plates, challenges increase. In this study, Mg alloy plates of thickness 11.5 mm were successfully welded via single-pass FSW. Due to the dynamic recrystallization, grain size in the stir zone was reduced to 16 µm which is ≈15 times smaller than the parent material. The optimized rotational speed and traverse speed for optimum weld integrity were found to be 710 rpm and 100 mm/min, respectively. A sound weld with 98.96% joint efficiency, having an Ultimate Tensile Strength (UTS) of 161.8 MPa and elongation of 27.83%, was accomplished. Microhardness of the nugget was increased by 14.3%.