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Abstract Generalized frequency division multiplexing (GFDM) is a flexible block‐structured multi‐carrier scheme recently proposed for next‐generation wireless communication systems. There are various approaches suggested for its analysis and implementation via simulations but testing in real‐time environments is not heavily investigated. This paper carries out the real‐time implementation of the GFDM system utilizing software‐defined radio (SDR) by emphasizing mainly channel estimation and synchronization. Symbol timing, frequency offset, and channel estimate algorithms are applied using a windowed preamble with two identical halves to satisfy low egress noise requirements. Time and frequency estimation is evaluated in terms of residual offsets along with symbol error rate over frequency selective channels. This algorithm is extended to a preamble composed of multiple identical parts. This facilitates a large frequency estimation range at the cost of complexity. For practical validation of the above concepts, the National Instruments (NI) universal software radio peripheral (USRP) 2953R is employed as hardware and it is interfaced with LabVIEW.

IntroductionWire-electric discharge machining (WEDM) possesses multiple benefits over traditional production approaches; it allows for the precise processing of complex and rigid particulate-reinforced composite materials. Aluminium alloys have found widespread applications in surgical components, shipbuilding, aircraft, automobiles, and inhaling gas cylinders for scuba diving, due to its good strength, and light weight properties.MethodsThe main aim of this investigation is to optimize different process variables for LM6/B4C/Fly ash particle reinforced hybrid composites using WEDM to attain performance metrics such as maximum material removal rate (MRR) and minimal surface roughness (SR). Taguchi’s L27 orthogonal array (OA) matrix and Grey Relational Analysis (GRA) were used.Results and DiscussionAccording to ANOVA, the two variables with the most significant impact on MRR and SR are gap voltage and reinforcement percentage, with respective impacts of 29.59% and 20.69%. When the composite is machined, the following process variables work best: GV of 30 V, Ton of 10 µs, Toff of 2 µs, WF of 4 m/min, and R of 6%. A low gap voltage causes the erosion to rise and the MRR to increase. However, SR will also increase which is undesirable, so optimum gap voltage values are required for WEDM.

This research presents a novel approach to fault diagnosis for CNC hobbing cutters using machine learning techniques, leveraging three-axis vibration data to ensure machining precision and tool reliability. Traditional methods of tool monitoring are insufficient for real-time and complex machining environments, prompting the integration of automated machine learning models. A robust dataset was collected from a CNC hobbing machine, capturing vibration signals under healthy and faulty tool conditions. Statistical features, including Root Mean Square (RMS), Crest Factor, and Kurtosis, were extracted from the vibration data for model training. Various machine learning algorithms, including Decision Trees, Efficient Linear models, Neural Networks, and Ensemble methods, were evaluated for their classification accuracy. Among these, the Ensemble model achieved perfect classification accuracy (100 %) with minimal computational cost, making it optimal for real-time applications. Explainable AI techniques, such as LIME and Shapley values, were employed to interpret model predictions, enhancing the system's transparency and reliability. The proposed framework demonstrated superior performance compared to existing methodologies in the literature, addressing key gaps such as overfitting, data quality, and model explainability. Real-world deployment challenges, including diverse operating conditions and generalizability across machines, were also discussed, with recommendations for incorporating multi-sensor data and transfer learning approaches in future research. This study establishes a foundation for predictive maintenance in CNC machining, significantly reducing downtime and improving operational efficiency through precise fault diagnosis in hobbing cutters.

Pyridine derivatives have emerged as promising candidates in the field of biomedical research, showcasing a wide array of applications in drug development and therapeutic interventions. The recent advances in the design and utilization of pyridine derivatives, focusing on their diverse roles in biomedical applications is the key understanding in this study. The versatility of pyridine-based compounds has been leveraged to address various challenges in the realms of pharmaceuticals and medicinal chemistry, offering innovative solutions for improved healthcare outcomes. This review encompasses the synthesis methodologies of pyridine derivatives, elucidating key synthetic strategies that enable the tailoring of these compounds for specific biomedical purposes and medicinal properties. This underscores the recent advancements in understanding the pharmacokinetics and pharmacodynamics of pyridine derivatives, emphasizing their potential impact on the future landscape of biomedical research. The synthesis and application of these compounds represent a dynamic frontier in drug development, offering innovative solutions to address unmet medical needs and propel the field toward more effective and personalized therapies. Pyridine derivatives play an important role in bio-imaging applications for the diagnosis of various diseases. Pyridine-based macromolecules have great potential for the efficient and specific delivery of drugs.

This study examined the use of geopolymer aggregate (GPA), produced from slag and an alkaline solution, as a substitute for coarse aggregate in conventional concrete. The research aimed to evaluate how different proportions of GPA (25%, 50%, 75%, and 100% replacement) affect the durability and mechanical properties of concrete. A series of tests were conducted, including slump, compressive strength, flexural strength, chloride resistance, sulfate resistance, sorptivity, and water absorption. The results demonstrated significant improvements in concrete properties with the inclusion of GPA. The compressive strength of GPA concrete ranged from 31 to 38 MPa, showing a 10%–15% increase over that of standard concrete. Flexural strength improved by 6% at 7 days and 7.5% at 28 days compared to control mixes. Water absorption was reduced by 42.58%, and sorptivity decreased by 47.9% compared to ordinary aggregate concrete. GPA concrete also excelled in acid resistance, with a weight loss of 28.6% and a lower reduction in strength compared to traditional acidic aggregates. In sulfate resistance tests, GPA concrete showed a 31% reduction in both weight and strength loss. These results highlight the advantages of using GPA as an alternative to conventional coarse aggregates. GPA not only enhances the mechanical and durability properties of concrete but also presents a more sustainable option, contributing to reduced environmental impacts associated with traditional aggregate materials.

Recent advancements in concrete technology focus more on increasing strength than durability. Concrete with good durability will withstand adverse conditions like frost, chloride penetration, sulfate assault, alkali-aggregate reaction, steel corrosion, etc., which will lower the strength of the concrete. Strength is vital, but so is durability. The present study examined and discussed the durability parameters of conventional concrete made with geopolymer aggregate (GPA) as a partial substitute for natural aggregate. Strength studies in this research found that the optimal level of substitution for natural coarse aggregate by GPA was 100% replacement to produce the performing concrete. Replacement of natural coarse aggregate by geopolymer aggregate exhibits 9%–15% higher compressive strength than natural aggregate concrete. The findings reveal that concrete with 100% geopolymer aggregate exhibits a compressive strength increase of 9%–15% over that of concrete made with natural aggregates. Ultrasonic pulse velocity measurements for geopolymer aggregate concrete range between 4 km/s and 4.5 km/s, indicating good quality according to IS specifications. Additionally, Rebound Hammer test results further support the enhanced quality of geopolymer aggregate concrete. However, the porosity of geopolymer aggregates results in a sorptivity that is 10%–30% higher than that of natural aggregate concrete. Despite this, the increased resistance to acid and sulfate attacks is noted, attributed to the strong bonding between geopolymer aggregates and the cement matrix.

IntroductionFriction drilling is an innovative method in hole-making for sheet metal applications, thin sheets of conventional structural alloy materials like copper, titanium, steel etc., even though there are other methods, such as thermal distortion for the welding of nuts, riveting of nuts, and threading. For the last hundred years, researchers have focused on studying the development of this technique to maintain strength, hole roughness, hole geometry, hardness etc. It is interested in finding solutions for wear, tool life, and plastic deformation.MethodFriction drilling is also called a green hole-making process because this process uses the friction between the rotating tool and the workpiece. In this research, instead of regular HSS and Tungsten Carbide tools, the H13 tool steel is used, because the H13 steel tool has unique chemical compositions like chromium and molybdenum, which give high toughness, hot hardness, and wear resistance. Diamond-like-carbon (DLC) coating has been used in this research to enhance tool life and AZ31B magnesium alloy is used as the work material. Initially, in this research, the wear stability of the DLC-coated H13 tool was investigated, and later, the tool surface roughness and hole quality were verified.Results and DiscussionThe material loss observed for the DLC-coated H13 steel tool in the pin-on-disk test was 0.05 g. This investigation used two different diameter tools, namely 3 and 7 mm. Research has concluded that the 7 mm tool is better for friction drilling by seeing the roughness and hole quality. However, the conditions were that the spindle should rotate at 4,000 rpm and the feed rate of the tool to be at 200 mm/rev.

The state of cutting tools profoundly influences the efficiency of the machining processes within the manufacturing industry. Cutting tool faults are highly undesirable and can adversely impact the performance of machine tools, leading to a shortened operational lifespan. Consequently, it is imperative to minimize power consumption by closely monitoring the condition of cutting tools. This necessitates implementing an effective supervision system to continually assess and predict potential faults. In simpler terms, this entails identifying issues that could compromise the lifespan of cutting tools before they escalate into problems like wear, breakage, or complete failure. This proactive approach ensures the optimal and efficient utilization of cutting tools, reduces the need for maintenance and repair, and enhances process stability, among other benefits. In this paper, a novel approach of machine learning for the condition monitoring of hobbing cutters used in Computer Numerical Control Machines (CNCs) was built. Vibration signals from hobbing blades were recorded under various conditions, including healthy and faulty states. The histogram presents a comprehensive overview of the statistical distribution for five variables related to the studied dataset. MATLAB code and scripts are utilized for extracting relevant statistical features, and for identification of the most relevant features, decision tree algorithms were used. For training the ML algorithms the hyperparameters were selected by the Grid Search Method and the Principal component analysis (PCA) was enabled for the reduction of dimensionality and to simplify the data set. The various conditions of hobbing cutters were then classified using tree-based classification models, giving 100% classification accuracy. It helps to develop a novel condition monitoring system for CNC hobbing cutters using machine learning methods to identify problems in hobbing blades. This would ultimately lead to lower power consumption and enhanced performance of machine tools.

Abstract 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.

During the evolution process, a bamboo stem achieves a significant height (up to 20 m) to fulfil its phototropic requirements. While on land, the stem is mostly subjected to bending load which makes it liable to fail by uprooting. However, this failure is prohibited by smart structure of bamboo stem which includes graded arrangement of fibre bundles in the cross-section and a tapered cantilever form of the stem. This paper attempts to understand the optimal design of bamboo stem through the relationship between the stellar arrangement of stiff fibre bundles in the cross-section and the tapered form. In this work, a comparison between two types of stellar arrangement, namely uniform and graded, is presented in view of non-linear bending analysis through elastica theory and fracture-induced delamination, both numerically. It is observed from the results that a bamboo stem prefers to evolve with graded stellar arrangement which provides gradation of stiffness and toughness over the cross-section; the trend in toughness being opposite to that of stiffness. Moreover, interplay of stellar arrangement and gradation of stiffness-toughness thereof is found to be the governing mechanism for ensuring its mechanical integrity and stability in view of an optimal design perspective. The smart structure of bamboo is recommended for bio-mimicking.

Dissimilar metal combinations are frequently employed in the power generation and nuclear industries. Where stainless steel piping systems are connected to pressure vessels made of low-alloy steel, the subsystems of liquid rocket engines also have different, dissimilar material combinations. Dissimilar welding plays a vital role in ensuring the integrity, performance, and reliability of components and structures operating in cryogenic environments, in this study, plates of AISI 316L and AISI 321, each 5 mm thick, were successfully joined using the pulsed current gas tungsten arc welding (PCGTAW) technique with optimized process parameters. These weld joints are mostly present in rocket engines subjected to a cryogenic environment. Due to the low temperature environment, the metallurgical properties of these joints change, which affects their mechanical properties. As it is a structural part, PCGTAW welding is most common method for joining this kind of material.In this work, Microstructural analysis of the weldment revealed a combination of vermicular, lacy, and acicular ferrite morphologies in the fusion zone at the root, mid, and crown locations.Furthermore, no solidification cracking was detected in the weldments based on the optical micrograph and SEM results. Intergranular corrosion (IGC) testing indicated the absence of a ditch structure, suggesting that the heat-affected zone (HAZ) on both sides of the weld joint was not being susceptible to sensitization. However, the HAZ of the AISI 316L side exhibited coarser grains compared to AISI 321. Analysis of tensile properties revealed a significant influence of the testing environment on the tensile strength of the dissimilar welded joints. At room temperature, the average ultimate tensile strength (UTS) was measured as 621 MPa. Remarkably, at cryogenic conditions, the average tensile properties significantly increased to 1319 MPa.Microhardness analysis showed the highest hardness associated with the AISI 321 side. The fusion zone exhibited a large deviation in the hardness profile (205 ± 10 HV), with the highest average hardness observed in the middle part of the weld. However, the hot cracking behavior of the weld was investigated by using a suutula diagram at various locations of the weld. The investigation revealed that the Creq/Nieq ratio exceeded the critical threshold value, effectively diminishing the propensity for hot cracking in the fusion zone. Overall, these findings underscore the effectiveness of the PCGTAW technique in joining dissimilar materials, as well as the importance of microstructural and mechanical property evaluations, especially under extreme operating conditions such as cryogenic temperatures.Paraphrase.

IntroductionAdditive manufacturing (AM) is a revolutionary technology transforming traditional production processes by providing exceptional mechanical characteristics.MethodsThe present study aims explicitly to predict the hardness of Polycarbonate (PC) parts produced using AM. The objectives of this study are: (1) To investigate the process parameters that impact the ability to estimate the hardness of PC materials accurately, and (2) To develop a best-performing ML model from a range of models that can reliably predict the hardness of additively manufactured PC parts. Initially, fused filament fabrication (FFF), the most affordable AM technique, was used for the manufacturing of parts. Four process parameters, infill density, print direction, raster angle, and layer thickness, are selected for investigation. A heatmap is generated to obtain the influence of process parameters on hardness. Then, machine learning (ML) techniques create a range of predictive models that can predict hardness value considering the level of process parameters.ResultsThe developed ML models include Linear Regression, Decision Tree, Random Forest, K-nearest neighbor, Support Vector Regression, AdaBoost, and Artificial Neural Network. Further, an investigation has been done that includes choosing and improving ML algorithms and assessing the models’ performance.DiscussionPrediction plots, residual plots, and evaluation metrics plots are prepared to gauge the performance of the developed models. Thus, the research enhances AM capabilities by applying predictive modeling to process parameters and improving the quality and reliability of fabricated components.

The layered configuration of different material plates is one of the ways of achieving protection against different kinds of kinetic energy ammunitions. The thickness of each plate is one of the most important influencing parameters to prevent the penetration of the projectile. In the present study, a layered configuration of the Al2O3 and Al 7075-T651 is analysed, to prevent the perforation of 7.62 mm Lead core projectile, under normal impact conditions, by using LS-DYNA numerical simulations. Experiments were conducted on Al 7075-T651 plate and Numerical model was validated with experiment results. To achieve the objective, the validated numerical model was used to investigate influence on various Al2O3 and Al 7075-T651 combinations. Three factors led to the selection of Al 7075-T561 and Al2O3 as the target materials. First, the literature review revealed that these materials have already been employed in the construction of armour. Second, Al2O3 is a brittle material whereas Al 7075-T651 is ductile. Consequently, when combined in a layered arrangement, these materials offer the ideal destroyer-absorber arrangement. Thirdly, these materials have lower densities than steel. As a result, these materials offer a lightweight alternative for lead core 7.62 mm bullet defense. From the analysis, it is observed that two layered configurations were found to be effective in the prevention of bullet perforation: a front plate of Al2O3 that was 10 mm thick and had a rear plate of Al 7075-T651 that was 06 mm thick, and a front plate of Al2O3 that was 04 mm thick and had a 12 mm thick layer of Al 7075-T651.

Over the past several decades, there has been a significant surge in interest regarding the use of organic–inorganic hybrid polymers and nanocomposite membranes. The reasons for this are improved attributes, reduced costs, and the additional stability the influence membrane provides. This Review outlines the various techniques and methodologies used to prepare Nafion and its composites, delineating the promising benefits of the electrospinning process. Electrospinning has emerged as a versatile and promising technique for fabricating nanofibers with unique properties and wide-ranging applications. This study explores the electrospinning of Nafion, a perfluorosulfonic acid polymer widely known for its exceptional proton conductivity and chemical stability, into nanofibrous structures, unlocking new possibilities yet unknown features of its inherent properties. The morphology and chemical structure of the resulting nanofibers is analyzed. A thorough bibliographic analysis of electrospun Nafion was presented using the PRISMA approach for methodically presenting the report. Network visualization of connected authors and categorizing application-specific publications are also discussed. Moreover, the electrospinning parameters and blends are systematically investigated to optimize the production of Nafion nanofibers for various applications in fuel cells, water treatment, actuators, sensors, and energy harvesting. The challenges involved in electrospinning Nafion, Nafion nanocomposites, and their variants are also presented, with a discussion delineating the future scope. This work concludes by emphasizing the interdisciplinary character of the Nafion polymer and its composites, connecting materials science and the intricate issues presented by various sectors.

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.

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.

This study aimed to select suitable materials and optimize the thickness of these materials so that they could prevent the perforation of 7.62-mm AP bullets at 830 m/s impact velocity. A numerical method is used to analyze the impact on layered configurations of Al2O3 and Al 7075-T651 to fulfill this aim. In order to optimize the thickness of the armor, normal impact and angular impact conditions were considered. Initially, a 20-mm Al2O3 front plate with a 20-mm Al 7075-T651 back plate is analyzed for layered configuration. Back plate thickness is reduced in steps to 10 mm such that no plastic deformation is observed on the rear side of the target. For further optimization of weight, the thickness of the Al2O3 plate is reduced to 18 mm. The weight of this configuration is 1.77 kg, and the areal density is 97.22 kg/m2. This configuration is analyzed for target orientations such as 80°, 70°, and 60°. In this analysis, the projectile deformed in a mushroom shape for 90° and 80° target orientations, while for 70° and 60° target orientations, the projectile experienced more damage on the shank part. The most effective configuration with the highest degree of ballistic performance is a layered combination of the 18-mm Al2O3 front plate and 10-mm Al 7075-T651 back plate at 70° target orientation.

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.

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 TOPSIS method 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.

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.

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.

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.

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.

This paper focuses on the numerical and experimental investigation of the small-scale power generator. The travelling wave thermoacoustic power generator is numerically analyzed and experimentally tested. The cyclic analysis is used to carry out numerical analysis of the power generator. The system is operated on atmospheric pressure, which allows the manufacturing of an acoustic feedback loop using Polyvinyl chloride piping. The acoustic power generated inside the generator is harnessed by the low-cost linear alternator, i.e., loudspeaker. The effect of regenerator wire mesh on performance of the power generator is numerically analyzed and validated experimentally. The numerical analysis identifies the temperature variation, pressure fluctuation, volume flow rate inside the system, and acoustic power distribution. The maximum electric power experimentally generated by the small-scale power generator is around 45 W with overall efficiency 8.30%. The alternator generates the maximum electric power at the optimum location, i.e. 2.30 m away from the engine core.

This study aimed to investigate the impact of a lubricant derived from trimethylolpropane trioleate on power consumption and surface roughness during high-speed drilling of Al-6061, with the goal of developing an environmentally friendly cutting fluid. The study investigated the impact of additive concentration, spindle speed, and feed rate on energy consumption and surface roughness using a Taguchi L27 orthogonal array. Through analysis of the Taguchi experimental outcomes and single-to-noise ratios, the parameters were ranked accordingly. The results of the ANOVA analysis reveal that spindle speed has the greatest impact on Power (87.89%), followed by followed feed rate (6.96%) and additive concentration (2.98%). However, feed rate (43.51%) has the most significant influence on surface roughness, followed by speed (38.48%) and additive concentration (11.90%). Varying additive concentration affects more on surface quality rather than power consumption. Furthermore, a machine learning algorithm was developed to forecast and compare various key aspects of high-speed drilling machinability, including power and surface roughness. Three different measures of accuracy were used to evaluate the performance of the projected values: coefficient of determination (R2), mean absolute percentage error, and mean square error. The decision tree performed better than other models in accurately predicting power and surface roughness. This research introduces an innovative method for assessing the most effective biodegradable cutting fluid and forecasting power and surface quality by developing an optimal combination.

Fused deposition modeling (FDM) is a layered manufacturing (LM) process in which progressive 2D layers of material are kept making a 3D part. To optimize the building operation, the investigation is necessary to study the effect of process parameters. This study examines the impact of three filling patterns, “triangles,” “cross,” and “cross 3D,” with three filling densities (25%, 50%, and 75%) in three orientations (“flat,” “on long edge,” and “on short edge”) on ultimate tensile strength (UTS), hardness and the printing time of polylactic acid (PLA) material. This work built tensile specimens according to ASTM D638 on an open-source 3D printer. The UTS was collected using a WDW-2000 computer control electronic universal testing machine. The hardiness value was also measured using Shore A hardness durometers DIN 53505 and ASTM D2240. In addition, the building time was conducted by implementing the “Cura 4.6” slicer software. The results show that the filling pattern, orientation, and density at which the part is built significantly affect the strength, hardness, and building time. For light structure parts with 25% density, it is recommended to build parts with “triangles” “on long edge” to obtain the highest strength (31.02 MPa), which improved by 74.3%, with 281 HV at the minimum time (1 h:10 min), which was reduced by 46.1%. For dense structure parts, “triangles” and “on long edge” are recommended to give the highest strength (42.12 MPa), which can be improved by 69.8% (282 HV) at the minimum time (1 h:35 min), which was reduced by 36.2%. For medium construction, 50% of the parts can be built by the following parameters: “cross” and “on long edge” to obtain the highest strength (38.48 MPa) with 283 HV at the minimum possible time (1 h:55 min). The study could help with the growth of producing mechanically functional parts, such as prosthetics, using LM. In addition, the part can be built with high strength, hardness, and minimum building time, which is useful information for the best utilization of the 3DP machine. Also, the chosen parameters optimize the building process with little human intervention.

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.

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.

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.

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.

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.

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.

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

Due to the increasing demand for artificial joint implants, two types of clays, montmorillonite (MMT) and halloysite nanotube (HNT), have been used either in combination as hybrid additives to reinforce the properties of the widely used polymer in the biomedical field (ultra-high molecular weight polyethylene (UHMWPE)). The 5% nanoclay content was incorporated into all manufactured nanocomposite materials. Several tests were conducted to evaluate the mechanical performance of these nanocomposite materials, including Izod, hardness, abrasion, and compression. Additionally, XRD analysis, scanning electron microscopy, and biocompatibility study were investigated to examine these nanocomposite’s capacity to suit orthopedic applications. Compared to pure UHMWPE, the “hybridization” effect successfully increased the resistance of the nanocomposite. Although 5% HNT-UHMWPE composites underwent some degradation in impact strength, the excellent ductility preserved the composites to fail without catastrophic fracture. Based on the MTT assay, all nanocomposites exhibited good biocompatibility, evident by favorable cell growth with no signs of cytotoxicity after up to 96 h of incubation in media with different amounts of material in solution.

LM5 alloy is suitable for metal castings for marine and aesthetic uses due to its admirable resistance to corrosion. In order to make intricate shapes in the LM5 alloy, this study intends to assess the impact of Wire Electric Discharge Machining process variables, like Pulse on Time (Ton), Pulse off Time (Toff), Gap Voltage (GV) and Wire Feed (WF) on responses like Material Removal Rate (MRR), Surface Roughness (SR), and Kerf Width (Kw). The LM5 aluminium alloy plate was produced through stir casting process. SEM, EDAX and XRD images confirm the LM5 Al alloy's microstructure and crystal structure. WEDM studies were conducted using design of experiments approach based on L9 orthogonal array and analysed using Taguchi's Signal to Noise Ratio (S/N) analysis. Pulse on Time has the greatest statistical effects on MRR (68.25%), SR (79.46%) and kerf (81.97%). In order to assess the surface integrity of the WEDM machined surfaces, the SEM study on the topography was conducted using the optimum surface roughness process variables: Ton 110 μs, Toff 50 μs, GV 40 V, and WF 9 m/min. SEM images show the recast layer and its thickness. The average absolute error for MRR is 1.69%, SR is 3.89% and kerf is 0.88%, based on mathematical (linear regression) models. The Taguchi's Signal to Noise ratio analysis is the most appropriate for single objective optimization of responses.

Wear is prominent in sliding components, so tribology property plays a major role in automotive as well as in the aerospace industries. In this work, Aluminium alloy LM6/B4C/Fly Ash hybrid composites with three different weight percentages of reinforcement were fabricated using the low-cost stir casting technique, and the experiments were conducted based on the Design of Experiments (DoE) approach and optimized using Taguchi’s Signal to noise ratio (S/N) analysis. The analysis was conducted with process parameters like Sliding Speed (S), Sliding distance (D), load (L) and reinforcement percentage (R %), the responses are Coefficient of Friction (COF) and Specific wear rate (SWR). Aluminum alloy reinforced with 9 wt% hybrid (LM6 + 4.5% B4C + 4.5% Fly Ash) has a low density and high hardness compared with other composites and base alloys. The optimum parameters for obtaining minimum SWR are S - 1 m/s, D - 500 m, L - 45 N, and R% - 6 wt% Hybrid (3% Fly ash and 3% boron carbide). The optimum parameters for obtaining minimum COF are S - 1.5 m/s, D - 500 m, L - 30 N, and R% −9 wt% Hybrid (4.5% Fly ash and 4.5% boron carbide). Load (28.34%) is the most significant parameter for obtaining minimum SWR, and DL (31.62%) for obtaining minimum COF. SEM images of the worn pins show the various wear mechanisms of the AMCs. The hybrid composite produced is new and these may be used for piston liner and brake pad applications.

In the present work, an investigation of the aerodynamic characteristics of an ejection seat occupant is carried out using the commercially available computational fluid dynamics software ANSYS Fluent. 3D Reynolds-averaged Navier–Stokes equations are solved to obtain the aerodynamic coefficients of the ejection seat system. For this analysis, an unstructured grid is generated for the ejection seat occupant using ANSYS meshing. Validation is carried out and the performance of three different turbulence models is analyzed at Mach 0.6. Based on the most suitable turbulence model, further analysis of the aerodynamic coefficients of the ejection seat occupant is calculated at Mach numbers of 0.35, 0.45, 0.55, 0.65, and 0.75. For all values of Mach, the angle of attack is varied from −15° to 15° in 5° increments and the yaw angle is varied from 0° to 60° in 10° increments. Based on the results, it is observed that the magnitude of the axial force decreases with increasing angle of attack and yaw angle. Similarly, the normal force coefficient and pitching moment coefficient decrease with increasing angle of attack. Finally, the side force coefficient, yawing moment, and rolling moment coefficients increase with increasing yaw angle.

The wear behaviour of AISI 316LN austenitic stainless steel has been studied by varying the duration of nitriding. AISI 316LN steel was nitrided using salt bath nitriding which consist of a mixture of 70:30 ratio of alkaline cynates and carbonates. Nitriding was carried out for two time durations namely 60 min and 100 min. Pin-on-Disc wear test were carried out for both the untreated and treated sample at room temperature. The wear parameters were changed to understand the wear mechanism of AISI 316LN steel along with the nitrided sample by varying the sliding distance for 250 m, 500 m and 1000 m. Due to the presence of different wear mechanism the rate of wear varies as a function of sliding distance. For untreated sample the wear mechanism was dominant with adhesion, abrasion and plastic deformation. An optical micrograph, X-ray diffraction analysis, wear morphology, hardness measurement and surface roughness were carried out. Due to the presence of compound layer as a result of nitriding AISI 316LN austenitic stainless steel, the mechanism of wear was restricted to abrasive wear. Untreated specimens have more significant wear loss when compared to treated specimen as work hardening and an increase in hardness of wear track resulted in material pull out. Stability of the compound layer is achieved as a result of longer duration of nitriding. Abrasive wear is resisted to a greater extend due the availability of compound layer. AISI 316LN steel which was nitrided for 100 min exhibited a more stable compound layer when compared to the same nitrided for 60 min.

Recently, additive manufacturing techniques have begun to be implemented extensively in the production of implants. Ti6Al4V alloy is a material of choice for implants due to its low density and high biocompatibility. Recent research, however, has demonstrated that Ti6Al4V alloy emits long-term ions (such as Al and V) that are hazardous to health. Surface modifications, including coating, are therefore required for implants. The electrodeposition method was utilized to deposit Zn-doped TiO _2 onto the surfaces of Ti6Al4V samples, which were manufactured via the selective laser melting method. The effects of processing time, amount of TiO _2 addition, microstructure of anode materials, and resistance to wear and corrosion were investigated. The coating hardness and thickness increased with increasing processing time and TiO _2 concentration. It has been observed that the addition of TiO _2 to zinc anode coatings results in an increase in wear and a decrease in corrosion rate. It was noted that the specimens exhibiting the most significant wear also possessed the highest hardness value. The specimens were generated utilizing a graphite anode, underwent a 30-min processing time, and comprised 10 g l ^−1 of TiO _2 .

The findings of the experimental study into optimizing the heat transfer rate of a PCM-based heat sink for high-power LEDs are presented in this work. The study investigated five heat sink types, with and without PCM. The LED case and junction temperatures, LED module temperatures, heat storage and release rate analyses, analyses of three types of cyclic operation modes, luminous flux, and heat sink thermal resistance were all examined independently. The results indicated that the PCM-based LED heat sink had improved thermal performance. The LED junction temperature of the PCM-equipped E-20 heat sink is nine degrees Celsius lower at 10 W than that of the heat sink without PCM. Furthermore, the E-20 heat sink with PCM extends the LED module’s critical lifespan. As a bonus, the E-20 with PCM had a 38.19 percent lower thermal resistance at 10 W than the E-20 without PCM. According to these results, the heat sink E-20 emits 715 lm at 10 W when operated without a phase-change material (PCM). With the same input power, the luminous flux of an E-20 equipped with a heat sink and a phase-change material (PCM) is 750 lm, a gain of 4.7%. Finally, clearly recommend the heat E-20 sink with PCM suitable for high-power LED thermal management system.

The materials used in engineering have seen a significant transformation in the contemporary world. Numerous composites are employed to overcome these problems because conventional materials are unable to meet the needs of current applications. For quite some time, professional engineers and researchers have been captivated by the problem of choosing the best machining parameters for new composite materials. Wire electrical discharge machining is a popular unconventional machining process that is often used for making complex shapes. Numerous process parameters influence the WEDM process. Thus, to achieve affordable and high-quality machining, the right set of process parameters must be provided. Finding the wire cut EDM optimized settings for the fabricated LM5/ZrO2/Gr composite is the main aim of this research. The chosen input parameters are the wire feed, pulse on and pulse off times, the gap voltage, and the reinforcing percentage. In this study, LM5/ZrO2/Gr composites were made from stir casting with 6-weight percent ZrO2 as the reinforcement and varying graphite percentages of 2, 3, and 4 wt%. Then they were machined in WEDM using L27 OA to seek the best parameters for machining by adjusting the input parameters. The findings were analysed by means of grey relation analysis (GRA) to achieve the supreme material removal rate (MRR), lowest surface roughness (SR), and a smaller kerf width (Kw) simultaneously. GRA determines the impact of the machining variables on the standard characteristics and tests the impact of the machining parameters. Confirmation experiments were performed finally to acquire the best findings. The experimental findings and GRA show that the ideal process conditions for achieving the highest grey relational grade (GRG) are 6% ZrO2 with 2% graphite reinforcement, a wire feed of 6 m/min, a pulse off time (Toff) of 40 µs, a pulse on time (Ton) of 110 µs, and a gap voltage (GV) of 20 V. The gap voltage (22.87%) has the greatest impact on the GRG according to analysis of variance (ANOVA), subsequent to the interaction between the pulse on time and the gap voltage (16.73%), pulse on time (15.28%), and pulse off time (14.42%). The predicted value of the GRG is 0.679; however, the experimental GRG value is 0.672. The values are well-aligned between the expected and the experimental results. The error is only 3.29%, which is really little. Finally, mathematical models were created for each response.

Aluminum matrix composites (AMCs) are largely used in defense, maritime, and space applications for their excellent properties. LM5 is used where very high resistance to corrosion from seawater or marine atmospheres is required, for equipment used for the manufacture of foodstuffs, cooking utensils, and chemical plants. Zirconia is preferred over other reinforcements as it shows comparatively great refractory properties, high scratch resistance, and thermal shock resistance. Utilizing the stir casting technique, an attempt was made to produce AMCs of LM5 aluminum alloy strengthened with ZrO2. The weight percentage of ZrO2 was changed to 0%, 3%, 6%, and 9%. The specimens were prepared and tested as per ASTM standards to find the density, micro and macro hardness, impact, tensile, and compressive strength. The micrographs and SEM images confirm the uniform distribution of ZrO2 particles in the aluminum matrix. LM5/9%ZrO2 AMC has the highest density value of 2.83 g/cm3 and LM5/3%ZrO2 has the least porosity of 2.55%. LM5/9% ZrO2 has the highest hardness values of 78 VHN and 72 HRE. LM5/6% ZrO2 AMC has the highest tensile strength of 220 MPa, compressive strength of 296 MPa, and toughness of 12 J. LM5/6% ZrO2 AMCs may be used for many structural applications.

This study was designed to examine the effects of a trimethylolpropane trioleate (TMPTO)-based lubricant on thrust force and torque under the high-speed drilling of Al-6061 as an effective environmentally friendly cutting fluid. The tribological performance of three lubricant blends was evaluated based on ASTM standards. TMPTO base oil, notably enhances load-carrying capacity under extreme pressure conditions, with a seizer load of 7848 N. The best-performing oil was further optimized using a Taguchi-based design experiment to investigate the effect of different additive concentrations on thrust force and torque under actual contact conditions. Experiments were conducted using three critical machining parameters: additive concentration, spindle speed, and feed rate. The results of the ANOVA analysis reveal that spindle speed contributes most substantially (62.99%) to torque, with feed rate (23.72%) and additive concentration (7.74%) also showing significant impacts. On the other hand, thrust force is primarily influenced by feed rate (73.52%), followed by spindle speed (16.82%), and additive concentration (6.28%). Furthermore, a machine learning model was developed to predict and compare a few significant aspects of high-speed drilling machinability, including thrust force and torque. Three different error metrics were utilized in order to assess the performance of the predicted values, namely the coefficient of determination (R2), mean absolute percentage error (MAPE) and mean square error (MSE), which are all based on the coefficient of determination. Compared to other models, decision tree produces more accurate prediction values for cutting forces. The present study provides a novel approach for evaluating the most promising biodegradable lube oils and predicting cutting forces by formulating a perfect blend.

Heating of the electrode at the work–piece interface zone in spot welding, leading to degradation of the tip, becomes a significant concern in the high-volume production automotive industry. By recognizing the interrelationship between hardness, wear resistance, and thermal conductivity, the authors emphasize the importance of selecting electrode materials with suitable alloying elements desirable for achieving optimal performance in spot welding applications. This paper studies the wear behaviour of three types of spot-welding electrode materials under dry sliding contact conditions. A pin-on-disc tester was used to investigate Cu–Cd, Cu–Be and Cu–Cr–Zr alloys’ wear behaviour under variable parametric load, temperature and time conditions. Taguchi L9 orthogonal array was used to investigate the significance of parameters and their effect on linear wear. The ranking of the parameters was performed using SN ratio analysis. The wear mechanism was also studied using SEM analysis. Abrasive wear was observed at lower loads, while adhesion, oxidation and plastic deformation were observed under high-load and -temperature conditions. This study suggests an alternative to the presently used electrolytic tough pitch (ETP) Cu electrode involving equally good wear-resistance material. However, a detailed investigation on the effect of plasma on the metallurgical characteristics of selected material is suggested.

Incremental sheet forming (ISF) requires no or partial dies for sheet metal fabrication and is widely used for small batch production. In this process, necking is either suppressed or delayed due to the localized nature of tool–sheet contact; hence, more strains than conventional stamping and deep drawing are obtained. In the present study, two variations of ISF, namely cold ISF (CISF) and warm ISF (WISF), are compared. First, FEA modeling is carried out on ABAQUS to reach the forming forces involved in the process. It is found that WISF reduces the forming forces. The temperature for WISF is maintained at 180 °C. Following the simulation analysis, tests are carried out. The forming force in WISF is 55.77% less than that in CISF. The part fabricated by CISF is slightly more substantial than that by WISF; however, more forming depth can be achieved by WISF. There is a more uniform thickness distribution in the case of CISF than in WISF. However, the surface quality of the CISF product is inferior to that of WISF. It is observed that there is reduced forming force, increased formability, and better strain distribution in WISF compared to CISF. However, post-processing heat treatment and surface polishing of the formed parts is required to restore their mechanical properties.

Lightweight aluminum composite is a class of foam material that finds many applications. These properties make it suitable for many industries, such as the transportation, aerospace and sports industries. In the present work, closed-cell foams of an Al-Si12CuFe alloy and its composite are developed by a stir casting process. The optimization of the foaming temperature for the alloy and composite foams was conducted in terms of the ligament and node size of the alloy and also the volatility of the zircon with the melt, to provide strength to the cell walls. CaCO3 as a blowing agent was homogeneously distributed in the molten metal without adding any thickener to develop the metal foam. The decomposition rate of CaCO3 is temperature-dependent, which is attributed to the formation of gas bubbles in the molten alloy. Cell structure, such as cell size and cell wall thickness, is controlled by manufacturing process parameters, and both the physical and mechanical properties are dependent on the foam cell structure, with cell size being the major variable. The results show that the increase in cell wall thickness with higher temperature leads to a decrease in cell size. By adding the zircon to the melt, the cell size of the composite foam first increases, and then the thickening of the wall occurs as the temperature is increased. The uniform distribution of the blowing agent in molten metal helps in the formation of a uniform cell structure. In this work, a comparative structural study of alloy foam and composite foam is presented regarding cell size, cell shape and foam stability at different temperatures.

Additive manufacturing (AM) has started to unfold diverse fields of applications by providing unique solutions to manufacturing. Laser cladding is one of the prominent AM technologies that can be used to fulfill the needs of custom implants. In this study, the wear resistance of the laser cladded titanium alloy, Ti-17Nb-6Ta, has been evaluated under varied loads in Ringer’s solution. Microstructural evaluation of the alloy was performed by SEM and EDX, followed by phase analysis through XRD. The wear testing and analysis have been carried out with a tribometer under varied loads of 10, 15, and 20 N while keeping other parameters constant. Abrasion was observed to be the predominant mechanism majorly responsible for the wearing of the alloy at the interface. The average wear rate and coefficient of friction values were 0.016 mm3/Nm and 0.22, respectively. The observed values indicated that the developed alloy exhibited excellent wear resistance, which is deemed an essential property for developing biomedical materials for human body implants such as artificial hip and knee joints.

The present research investigates the wear properties of a continuous carbon fiber-reinforced additively manufactured polymer composite under dry sliding conditions. The effect of load and fiber orientation is examined on polymer composite specimens. The wear test of the additively manufactured polymer composite specimens is conducted on pin-on-disk test equipment. The result shows that the applied load and fiber orientation significantly affect the composite specimen’s wear properties. The wear and coefficient of friction (COF) increase with load. The minimum effect on wear and COF is observed for 0° fiber orientation, and the maximum effect is observed for 90° fiber orientation. Finally, morphological analysis is conducted using an optical micrograph of the worn-out surfaces to understand the failure type for different fiber orientations.

To meet the global requirements of the industry, there is a need for innovative light weight, low cost, eco-friendly, high quality engineering materials with superior performance at a wide range of temperatures. Aluminum Matrix Composites (AMCs) are lightweight materials with isotropic properties, a suitable choice for modern industry. Low-cost aluminum alloys reinforced with minerals have found a special place in the automotive industry for the manufacture of automobile parts. Rutile particles improve the mechanical properties of the aluminum matrix, making it attractive for structural applications as well as providing greater wear resistance to the composite during sliding. In the present studies, a rutile mineral reinforced LM27 aluminum alloy composite was developed through a stir casting route with 6, 9 and 12 wt.% reinforcements. To study the effect of particle size on the mechanical and tribological properties of composite samples reinforced with fine-sized (50–75 μm) and coarse-sized (106–125 μm) rutile particles were prepared From the results of the experiment, it was found that the hardness, compressive strength and wear resistance increase with increasing rutile wt.% addition. The worn surface and re-groove of the new surface are restored during sliding with the help of oxide films. The analysis of the fractured surface clearly indicates with the help of energy dispersive X-ray spectroscopy (EDS) that with the increase in the volume fraction of the reinforced particles, the porosity increases, which could be the main reason for the compression failure of the composite material.

This work addresses an evolutionary algorithmic approach to reduce the surplus pieces in selective assembly to increase success rates. A novel equal area amidst unequal bin numbers (EAUB) method is proposed for classifying the parts of the ball bearing assembly by considering the various tolerance ranges of parts. The L16 orthogonal array is used for identifying the effectiveness of the proposed EAUB method through varying the number of bins of the parts of an assembly. Because of qualities such as minimal setting parameters, ease of understanding and implementation, and rapid convergence, the moth–flame optimization (MFO) algorithm is put forward in this work for identifying the optimal combination of bins of the parts of an assembly toward maximizing the percentage of the success rate of making assemblies. Computational results showed a 5.78% improvement in the success rate through the proposed approach compared with the past literature. The usage of the MFO algorithm is justified by comparing the computational results with the harmony search algorithm.