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With the enhancement in science and technology, necessity of complex shapes in manufacturing industries have become essential for more versatile applications. This leads to the demand for lightweight and durable materials for applications in aerospace, defense, automotive, as well as sports and thermal management. Wire electric discharge machining (WEDM) is an extensively utilized process that is used for the exact and indented shaped components of all materials that are electrically conductive. This technique is suitable in practically all industrial sectors owing to its widespread application. The present investigation explores WEDM for LM6/fly ash composites to optimize different process variables for attaining performance measures in terms of maximum material removal rate (MRR) and minimum surface roughness (SR). Taguchi’s L27 OA design of experiments, grey relational analysis, and analysis of variance (ANOVA) were employed to optimize SR and MRR. It has been noted from ANOVA that reinforcement (R) percentage and pulse on time are the most influential aspects for Grey Relational Grade (GRG) with their contributions of 28.22 and 18.18%, respectively. It is found that the best process variables for achieving the highest MRR and lowest SR simultaneously during the machining of the composite are gap voltage of 30 V, pulse on time of 10 µs, pulse off time of 2 µs, wire feed of 8 m/min, and R of 9%. The predicted GRG is 0.84, and the experimental GRG value is 0.86. The validation experiments at the optimized setting show close agreement between predicted and experimental values. The morphological study by optical microscopy revealed a homogenous distribution of reinforcement in the matrix which enhances the composite’s hardness and decreases the density.

Using a cutting-edge net-shape manufacturing technique called Additive Layer Manufacturing (ALM), highly complex components that are not achievable with conventional wrought and cast methods can be produced. As a result, the aerospace sector is paying closer attention to using this technology to fabricate superalloys based on nickel to develop the holistic gas turbine. Because of this, there is an increasing need for the mechanical characterisation of such material. Conventional mechanical testing is hampered by the limited availability of material that has been processed, especially given the large number of process factors that need to be assessed. Thus, the present study focuses on manufacturing CM247LC Ni-based superalloy with exceptional mechanical characteristics by laser powder bed fusion (L-PBF). This study evaluates the effect of input process variables such as laser power, scan speed, hatch distance and volumetric energy density on the mechanical performance of the LPBF CM247LC superalloy. The maximum value of as-built tensile strength obtained in the study is 997.81 MPa. Plotting Pearson's heatmap and the Feature importance (F-test) was used in the data analysis to examine the impact of input parameters on tensile strength. The accuracy of the tensile strength data classification by machine learning algorithms, such as k-nearest neighbours, Naïve Baiyes, Support vector machine, XGBoost, AdaBoost, Decision tree, Random forest, and logistic regression algorithms, was 92.5%, 83.75%, 83%, 85%, 87.5%, 90%, 91.25%, and 77.5%, respectively. [ABSTRACT FROM AUTHOR]

The research introduces an innovative method for creating a digital twin (DT) of heat energy storage and retrieval system (HESRS) for real-time monitoring and performance analysis. The HESRS, type of HVAC system, is evaluated based on parameters like stored heat energy, heat extraction via heat transfer fluid (HTF), and Phase Change Material (PCM) temperatures. Data from temperature sensors is sent to the cloud in real-time. A reduced-order model (ROM) analyses it on the cloud and sends results to an Android app. The DT is then simulated in augmented reality through our app, Twin-X, marking distinctive approach to digital twin development. [ABSTRACT FROM AUTHOR]

This paper proposes a convolutional neural network (CNN) model of the signal distribution control algorithm (SDCA) to maximize the dynamic vehicular traffic signal flow for each junction phase. The aim of the proposed algorithm is to determine the reward value and new state. It deconstructs the routing components of the current multi-directional queuing system (MDQS) architecture to identify optimal policies for every traffic scenario. Initially, the state value is divided into a function value and a parameter value. Combining these two scenarios updates the resulting optimized state value. Ultimately, an analogous criterion is developed for the current dataset. Next, the error or loss value for the present scenario is computed. Furthermore, utilizing the Deep Q-learning methodology with a quad agent enhances previous study discoveries. The recommended method outperforms all other traditional approaches in effectively optimizing traffic signal timing. [ABSTRACT FROM AUTHOR]

This article details the existing wearable assistive devices that could mimic a human's active range of motion and aid individuals in recovering from stroke. The survey has identified several risk factors associated with musculoskeletal pain, including physical factors such as engaging in high-intensity exercises, experiencing trauma, aging, dizziness, accidents, and damage from the regular wear and tear of daily activities. These physical risk factors impact vital body parts such as the cervical spine, spinal cord, ankle, elbow, and others, leading to dysfunction, a decrease in the range of motion, and diminished coordination ability, and also influencing the ability to perform the activities of daily living (ADL), such as speaking, breathing and other neurological responses. An individual with these musculoskeletal disorders requires therapies to regain and restore the natural movement. These therapies require an experienced physician to treat the patient, which makes the process expensive and unreliable because the physician might not repeat the same procedure accurately due to fatigue. These reasons motivated researchers to develop and control robotics-based wearable assistive devices for various musculoskeletal disorders, with economical and accessible solutions to aid, mimic, and reinstate the natural active range of motion. Recently, advancements in wearable sensor technologies have been explored in healthcare by integrating machine-learning (ML) and artificial intelligence (AI) techniques to analyze the data and predict the required setting for the user. This review provides a comprehensive discussion on the importance of personalized wearable devices in pre- and post-clinical settings and aids in the recovery process. [ABSTRACT FROM AUTHOR]

The present design of a set of worm gears used in a soot blower produced by a certain manufacturer has an efficiency of 68.8%. A soot blower is one of the most critical components in industrial applications for removing the large amounts of soot generated by boilers and is required to be operational 24×7. The energy consumption of the soot blower depends on its working efficiency and ultimately the design of its set of worm gears. This paper focuses mainly on the design and analysis of available industrial worm-gear sets used in soot blowers. The theoretical, experimental, and finite-element analysis approaches are validated for the stability of the worm gear set under typical input conditions. This paper also describes an analytical design of experiments (DOE) approach to identify the most significant factor for performance (efficiency) improvement and suggests some design improvements for the worm gear set using the profile modification approach. These ensure the efficiency improvement of the current industrial design of the set of worm gears used in a soot blower. The analytical DOE approach helped identify that the number of worm wheel teeth (Z2) and gear module (m) are the two most significant factors affecting performance. Accordingly, based on the improved design, the final efficiency increased from 68.8% to 74.6% (~8.5% increment), resulting in lower power consumption during industrial application. [ABSTRACT FROM AUTHOR]

The piping system connected with the shipboard equipment may be subjected to excessive vibration due to harmonic base excitation produced by hydrodynamic force imposed on the propeller blades interacting with the hull and by other sources. Vibration design aspects for shipboard pipework are often ignored, which may cause catastrophic fatigue failures and, consequently, leakage and spillage in the sea environment. Without dedicated design codes, the integrity of shipboard equipment against this environment loading can be ensured by testing as per test standard MIL-STD-167-1A (2005). However, in many cases, testing is not feasible and economically viable. Hence, this study develops an FE-based vibration analysis methodology based on MIL-STD-167-1A, which can be a valuable tool to optimize the testing requirement without compromising the integrity of these piping systems. The simulated model dynamic properties are validated with experimental modal testing and Harmonic response analysis result confirm that a mitigating solution option can be verified by a FE based vibration analysis to mitigate the vibration problem. [ABSTRACT FROM AUTHOR]

Introduction: The demand for improved small arms ammunition has led to exploring advanced materials and manufacturing techniques. This research investigates the machining characteristics of CM and WNF alloy bullets, aiming to enhance ballistic performance and durability. Methods: Bullet profile-making trials were conducted to evaluate the impact of machining parameters such as cutting speed and feed. The study also considered variables including surface roughness, cutting temperature, and hardness, alongside a detailed morphological analysis, The evaluation utilized an orthogonal array and MCDM approach, incorporating the TOPSISmethod for decision-making processes. Results: The findings reveal that WNF alloy bullets exhibit 3.01% to 27.95% lower machining temperatures, 24.88%-61.85% reduced surface roughness, and 19.45%-34% higher microhardness compared to CM bullets. Moreover, CM bullets demonstrated higher machining temperatures, resulting in 47.53% increased tool flank wear. WNF bullets showed a 24.89% reduction in crater wear and a 38.23% decrease in compressive residual stress in bullet profiles, indicating superior machining performance. Discussion: The superior machining performance of WNF alloy bullets suggests their potential to improve the ballistic performance and durability of small arms ammunition. The reduced tool wear and favorable machining parameters highlight WNF alloy's advantages for military and defense applications. A ballistic impact analysis using a finite element method (FEM) model in Abaqus software further supports the potential of WNF alloy bullets, providing a solid foundation for future advancements in bullet manufacturing technologies. [ABSTRACT FROM AUTHOR]

Surface integrity is one of the key elements used to judge the quality of machined surfaces, and surface roughness is one such quality parameter that determines the pass level of the machined product. In the present study, AISI D2 steel was machined with electric discharge at different process parameters using Jatropha and EDM oil. Titanium dioxide (TiO2) nanopowder was added to the dielectric to improve surface integrity. Experiments were performed using the one variable at a time (OVAT) approach for EDM oil and Jatropha oil as dielectric media. From the experimental results, it was observed that response trends of surface roughness (SR) using Jatropha oil are similar to those of commercially available EDM oil, which proves that Jatropha oil is a technically and operationally feasible dielectric and can be efficiently replaced as dielectric fluid in the EDM process. The lowest value of S.R. (i.e., 4.5 microns) for EDM and Jatropha oil was achieved at current = 9 A, Ton = 30 μs, Toff = 12 μs, and Gap voltage = 50 V. As the values of current and pulse on time increase, the S.R. also increases. Current and pulse-on-time were the most significant parameters affecting S.R. Machine learning methods like linear regression, decision trees, and random forests were used to predict the surface roughness. Random forest modeling is highly accurate, with an R2 value of 0.89 and an MSE of 1.36% among all methods. Random forest models have better predictive capabilities and may be one of the best options for modeling complex EDM processes. [ABSTRACT FROM AUTHOR]

Utilizing Machine Learning (ML) to oversee the status of hobbing cutters aims to enhance the gear manufacturing process's effectiveness, output, and quality. Manufacturers can proactively enact measures to optimize tool performance and minimize downtime by conducting precise real-time assessments of hobbing cutter conditions. This proactive approach contributes to heightened product quality and decreased production costs. This study introduces an innovative condition monitoring system utilizing a Machine Learning approach. A Failure Mode and Effect Analysis (FMEA) were executed to gauge the severity of failures in hobbing cutters of Computer Numerical Control (CNC) Hobbing Machine, and the Risk Probability Number (RPN) was computed. This numerical value aids in prioritizing preventive measures by concentrating on failures with the most substantial potential impact. Failures with high RPN numbers were considered to implement the Machine Learning approach and artificial faults were induced in the hobbing cutter. Vibration signals (displacement, velocity, and acceleration) were then measured using a commercial high-capacity and high-frequency range Data Acquisition System (DAQ). The analysis covered operating parameters such as speed (ranging from 35 to 45 rpm), feed (ranging from 0.6 to 1 mm/rev), and depth of cut (6.8 mm). MATLAB code and script were employed to extract statistical features. These features were subsequently utilized to train seven algorithms (Decision Tree, Naive Bayes, Support Vector Machine (SVM), Efficient Linear, Kernel, Ensemble and Neural Network) as well as the application of Bayesian optimization for hyperparameter tuning and model evaluation were done. Amongst these algorithms, J48 Decision tree (DT) algorithm demonstrated impeccable accuracy, correctly classifying 100% of instances in the provided dataset. These algorithms stand out for their accuracy and efficiency in building, making them well-suited for this purpose. Based on ML model performance, it is recommended to employ J48 Decision Tree Model for the condition monitoring of a CNC hobbing cutter. The emerging confusion matrix was crucial in creating a condition monitoring system. This system can analyze statistical features extracted from vibration signals to assess the health of the cutter and classify it accordingly. The system alerts the operator when a hobbing cutter approaches a worn or damaged condition, enabling timely replacement before any issues arise. [ABSTRACT FROM AUTHOR]

Purpose. The present study aimed to improve the aqueous solubility, permeability, bioavailability, and nootropic potential of standardized Emblica officinalis extract (EOE) by developing a novel phytosomal formulation. Method. Emblica officinalis extract-loaded phytosomes (EOPs) were prepared using solvent evaporation. The EOP was prepared at different molar ratios of extract and phospholipid. Herein, the effects of phospholipid extract ratio (A), temperature (B), and reaction time (C) were systematically investigated on entrapment efficiency using Box-Behnken design. In vitro and in vivo characterizations of the optimized formulation were performed. Results. Optimized EOP formulation (89.90 ± 0.24 μg/ml) exhibited improved aqueous solubility than plain EOE (11.85 ± 0.25 μg/ml). The optimized formulation's particle size and Zeta potential were 198.4 ± 0.20 nm and −39.0 ± 0.40 mv. DSC and XRD studies confirmed the partial amorphization of EOE in phytosomes. Optimized formulation exhibited 69.82 ± 0.17% of EOE release at 12 h and followed zero-order release kinetics. Moreover, the phytosomal formulation of EOE exhibited its rationality with an improvement of bioavailability by 2.7 folds compared with pure EOE. Compared to EOE, EOP showed significantly ( p < 0.05 lower escape and transfer latencies on both days in MWMT and EPMT, indicating more effective memory-enhancing activity. Furthermore, EOP-treated rats exhibited improved acetylcholine (Ach) levels than EOE. Brain tissue concentrations measured following EOP oral administration (1.06 ± 0.04 μg/ml) were substantially greater (p < 0.05) than those following EOE (0.32 ± 0.07 μg/ml). The brain dopamine and serotonin concentration were found to be higher (16.27 ± 1.209 and 43.28 ± 1.550 ng/ml) in the EOP-treated group as compared to the pure extract-treated group (10.40 ± 1.185 and 32.79 ± 1.738 ng/ml). Conclusion. Improvement of aqueous solubility, permeability, dissolution, bioavailability, and narrower particle size distribution could facilitate enhancement in the nootropic potential of EOE phytosomal formulation. [ABSTRACT FROM AUTHOR]

This article provides a comprehensive overview of the performance evaluation of looped tube thermoacoustic power generators using numerical and experimental analysis. The study focuses on small-scale generators operating at atmospheric pressure and utilizing low-cost linear alternators. The analysis considers factors such as temperature variation, pressure fluctuation, volume flow rate, and acoustic power distribution. The results show that the generators produce maximum acoustic power at a frequency of 78 Hz, with an overall thermal to electric efficiency of approximately 8.3%. The article also includes references to other research papers and articles that cover various aspects of thermoacoustic engines and generators, providing valuable insights into the field. [Extracted from the article]

Introduction: This study presents a comprehensive design and analysis of a 300L steel fuel tank intended for heavy off-road vehicles. The design process integrates numerical simulations and experimental investigations to optimize the tank's performance and durability under various operating conditions. Methods: The design methodology involves CAD model optimization, numerical analysis setup, and experimental validation. CAD model optimization simplifies the tank geometry while retaining structural integrity. Numerical analysis setup includes defining boundary conditions, meshing strategies, and simulation parameters. Experimental validation entails testing the tank under dynamic loading conditions to assess its structural response. Results: Numerical simulations reveal insights into stress distribution, deformation behavior, and fluid dynamics within the tank. Experimental tests confirm the numerical predictions and provide valuable data for model validation. Key results include stress concentrations in critical areas, deformation patterns under different loading conditions, and fluid flow characteristics. Discussion: The integrated approach combining numerical simulations and experimental tests offers a comprehensive understanding of the fuel tank's behavior. Findings highlight areas for design improvement, such as reinforcement of stress-prone regions and optimization of fluid flow dynamics. The study contributes to enhancing the performance, reliability, and safety of fuel tanks for heavy off-road vehicles. [ABSTRACT FROM AUTHOR]

NAO brake friction materials with 4%, 5%, and 6% (w/v) sodium hydroxide treated hemp fiber reinforcement having 25% wt. fiber loading and fixed percentage of phenol formaldehyde resin content (20% wt.) along with other fillers have been studied and reported by the authors earlier. However, the effect of variations in the resin content on the tribological performance has been studied and reported in the present paper. Five variants were prepared with varying percentages of phenol formaldehyde resin from 12% wt. to 22% wt. with incremental steps of 2% wt, along with the optimum of 6% (w/v) sodium hydroxide treated hemp fibers and other fillers. The prepared test variants' tribological characterization was done using Taguchi's L25 orthogonal array on a pin-on-disc experimental setup, as per ASTM G99, at room temperature and compared with the best of the earlier studied friction composite. Fade and recovery tests of the best of the earlier studied and present ones were performed on a chase tribology tester per SAE J661 standards. The results revealed moderate coefficient of friction of 0.4496, lower wear rate of 0.57 gm, and better fade recovery for the HF25P20 variant compared to its counterparts studied here. [ABSTRACT FROM AUTHOR]

Nowadays, research has shown the emergence of the 3D printing method for printing a functionalized component. Graphene nanomaterial has an enormous conducting property that can compete with conducting materials like copper and silicon. This paper describes the electrical conductivity investigation of 3D-printed graphene nanomaterial in extrusion-based 3D printing methods. In extrusion, two different approaches of the 3D printing method were used to print the graphene-based structure: the fused deposition modeling (FDM) method and the direct ink writing (DIW) method. Both printing methods follow the two printing processes and select material forms. Selection of testing was made to analyze the characterization variations in the printed material, such as XRD, TGA, viscosity, Raman shift, and Scanning Electron Microscopy analyses, which shows the changes of effect in the conductivity due to various parameter differences in both the printing methods. A four-point probe technique was used to analyze the electrical conductivity of the two different methods. These analysis results prove that the characterization variations differ in the FDM and DIW printed models. [ABSTRACT FROM AUTHOR]

This research focuses on the relationship between the tensile strength of PLA material and several 3D printing parameters, such as infill density, layer height, print speed, and extrusion temperature, utilizing the Fused Deposition Modeling (FDM) method of Additive Manufacturing (AM). Tensile strength of the samples was determined in compliance with ASTM D638 standard, and the experiments were carried out according to a planned arrangement. Six distinct methods were used to optimize the tensile strength: Particle Swarm Optimization (PSO), Teaching Learning Based Optimization (TLBO), Genetic Algorithm (GA), Simulated Annealing (SA), and Cohort Intelligence (CI). Several runs of the optimization methods demonstrated their consistency in producing the same values of tensile strength, indicating their reliability. The optimization results showed that JAYA performed better than the other algorithms, resulting in a material with the maximum tensile strength of 55.475 N/mm². Validation experiments were carried out to confirm the efficacy of these algorithms. The results showed that the ideal input parameters produced tensile strength values that closely matched the anticipated values with a low percentage error. The benefits of applying these algorithms to improve the tensile strength of PLA materials for 3D printing are demonstrated by this study, which also offers insightful information about how to optimize FDM procedures. [ABSTRACT FROM AUTHOR]

A highly advanced thermo-electric machining technique called wire electrical discharge machining (WEDM) can effectively produce parts with varying hardness or complicated designs that have sharp edges and are very difficult to machine using standard machining procedures. This useful technology for the WEDM operation depends on the typical EDM sparking phenomena and makes use of the commonly used non-contact material removal approach. Since its inception, WEDM has developed from a simple approach for creating tools and grown to an outstanding option for creating micro-scale components having the greatest degree of dimensional precision and surface finish characteristics. The WEDM method has endured over time as an efficient and affordable machining alternative that can meet the stringent operating specifications enforced by rapid manufacturing cycles and increasing expense demands. The possibility of wire damage and bent, nevertheless, has severely hindered the process' maximum potential and decreased the precision as well as effectiveness of the WEDM process. The article examines the wide range of investigations that have been done; from the WEDM through the EDM process' spin-offs. It describes WEDM investigation that required variables optimization and an assessment of the many influences on machining efficiency and accuracy. Additionally, the research emphasizes adaptive monitoring and control of the process while examining the viability of multiple approaches to control for achieving the ideal machining parameters. Numerous industrial WEDM applications are described with the advancement of hybrid machining techniques. The paper's conclusion examines these advancements and identifies potential directions for subsequent WEDM research. The investigation on WEDM of metal matrix composites (MMCs) is also reviewed; along with the impacts of various cutting variables like wire feed rate (F), voltage (V), wire tension (WT), and dielectric flow rate on cutting processes outcomes like material removal rate (MRR), kerf width (Kw) and surface roughness (SR). In the present article, future directions for WEDM research were also suggested. [ABSTRACT FROM AUTHOR]

This experimental study investigated the abrasive wear behaviour of pure copper-based and multi-walled carbon nanotube (MWCNT) doped composites synthesized by the powder metallurgy technique. Composite structures were formed by reinforcing MWCNT at different ratios between 1% and 8% in 99.9% pure copper by powder metallurgy. The microstructures of the nanocomposite samples were analyzed by X-ray diffraction. Then, density and hardness measurements and abrasive wear tests were performed to determine their mechanical properties. The collected data were evaluated with scanning electron microscopy images. It has been determined that copper's nano-sized carbon reinforcement material has a dry lubricant effect up to a specific ratio, reducing wear losses. On the contrary, wear losses increase as the MWCNT reinforcement ratio increases between 4% and 8%. The best results in lowering wear losses were obtained from the sample with 1% MWCNT reinforcement. Depending on the increase in the amount of nanomaterial reinforcement in the composite structure, it was observed that pore formation enlarges with reinforcement agglomeration. It was concluded that the dense porosity in the composite structure neglects the lubricating properties of the MWCNT reinforcement material and increases the wear losses by having a negative effect. [ABSTRACT FROM AUTHOR]

In the last few years, due to the superior mechanical qualities of Additive Manufacturing (AM) AlSi10Mg alloy to those of traditional casting process AlSi10Mg alloys, the application of AM technology has significantly increased. The ballistic impact research has a wide range of uses, notably in the mining, construction, spacecraft and defence sectors. This work focuses on analyzing the behavior of different projectile nose shapes on the AlSi10Mg alloy fabricated by AM. There are several projectile nose forms to consider, including blunt, hemispherical, conical, and ogive shapes. The impact of various projectile shapes on the ballistic limit of the additively created AlSi10Mg alloy is carefully examined in this study. All numerical simulations were carried out using LS-DYNA software, and the Johnson-Cook material and damage model were considered to assess the ballistic resistance behavior. The ballistic limit for various projectile shapes is computed using the Jonas-Lambert model, which describes the connection between residual velocity and starting projectile velocity. The results showed that, the ogive-shaped Projectile offers the highest ballistic limit, and the blunt projectile shows the lowest ballistic limit for a 5 mm thin target plate. The ballistic impact phenomenon showed plugging failure for the blunt nose projectile, the formation of plug and small fragments were observed in the case of hemispherical nose projectile, fragmenting failure is observed with radial necking in the case of conical nose projectile and petals are formed at the impacted zone in ogive nose shape projectile. Moreover, the ballistic limit of AM AlSi10Mg alloy was slightly higher compared to the ballistic limit of the die-cast AlSi10Mg alloy for the 7.62 mm AP bullet (core). Therefore, AM AlSi10Mg alloy may have equal or good ballistic properties compared to die-cast AlSi10Mg alloy. [ABSTRACT FROM AUTHOR]