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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.

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.

Abstract Hot dry rock (HDR) is regarded as a promising resource of geothermal energy and becomes an important field for future geothermal development due to its advantages of high temperature, wide distribution and huge reserves. At present, HDR research is mainly focused on the modeling and efficiency evaluation of power generation cycle, but its relationship with the source side of the system has not been considered in the field of integrated energy systems. Therefore, this paper proposes a day‐ahead scheduling method for regional integrated energy systems (RIES) with HDR based on information gap decision theory (IGDT). First, the heat transfer system model of HDR is established according to the energy flow model and basic structure of the HDR enhanced geothermal system (EGS). Second, a comprehensive geothermal energy system scheduling model is established from HDR based on the energy hub modeling structure. Then, the IGDT is introduced to analyze the renewable energy output uncertainty in the model. Finally, through a real RIES analysis, the simulation results verified the correctness and effectiveness of the proposed model. The scheduling cost was ¥47,073 when EGS participated in the scheduling. Access to EGS reduced the system's total 24‐h energy purchase by 8305 kW, natural gas consumption by 3051.9 m3, and total carbon emissions by 742.28 kg. The latter emphasized that the proposed model achieves the purpose of reducing the system cost, saving energy and reducing emissions.

Abstract A prompt diagnosis of breast cancer in its earliest phases is necessary for effective treatment. While Computer-Aided Diagnosis systems play a crucial role in automated mammography image processing, interpretation, grading, and early detection of breast cancer, existing approaches face limitations in achieving optimal accuracy. This study addresses these limitations by hybridizing the improved quantum-inspired binary Grey Wolf Optimizer with the Support Vector Machines Radial Basis Function Kernel. This hybrid approach aims to enhance the accuracy of breast cancer classification by determining the optimal Support Vector Machine parameters. The motivation for this hybridization lies in the need for improved classification performance compared to existing optimizers such as Particle Swarm Optimization and Genetic Algorithm. Evaluate the efficacy of the proposed IQI-BGWO-SVM approach on the MIAS dataset, considering various metric parameters, including accuracy, sensitivity, and specificity. Furthermore, the application of IQI-BGWO-SVM for feature selection will be explored, and the results will be compared. Experimental findings demonstrate that the suggested IQI-BGWO-SVM technique outperforms state-of-the-art classification methods on the MIAS dataset, with a resulting mean accuracy, sensitivity, and specificity of 99.25%, 98.96%, and 100%, respectively, using a tenfold cross-validation datasets partition.

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.

Spent engine oil is a major source of environmental pollution that affects the aquatic ecosystem and human health. Cellulose nanofibers (CNF) are natural nanomaterials that can be used as additives in spent engine oil to enhance its performance, especially at high temperatures and pressures. Utilizing sulfur-based unbleached rice straw pulping technique, the rice straw was grinded in a solution with 10 % sodium sulfite and 2 % sodium carbonate, that heated to 160 °C for 2 h. This produces unbleached sulfur pulp, which is then analyzed for ash, cellulose, and lignin content. A subsequent bleaching step at 80 °C with a sodium chlorite/acetic acid mix completes the process. This technique was adopted to produce CNF. In this study, preparation and characteriztion of CNF-additives to spent engine oil was performed using different techniquessuch as; IR spectra analysis, kinematic viscosity, and wear scar width. It was also evaluated on the oil adsorption, vibration, and noise reduction properties of the resulted additive oil. The results showed that CNF improved viscosity, lubrication, and anti-wear properties of the oil, and decreased its friction, vibration, and noise. The optimal concentration of CNF was 2.0 wt%, which increased the kinematic viscosity by 14.5 % and reduced the wear scar width by 31 % compared to the spent oil sample. Moreovere, CNF reduced the environmental impact of oil disposal and recycling. Overall, CNF are promising nanofillers for improving the performance of spent engine oil and lowering its environmental impact.

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.

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.

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.

The Jensen inequality is of fundamental importance because of its influential and interesting consequences. In recent years, the Jensen inequality has been supposed to be the most engaging source for research. We present interesting improvements to the continuous version of Jensen's inequality through the application of the concept of 6-convexity. For real visualization and comparison to other results, some numerical experiments were provided. With the aid of the acquired results, improvements for the Hermite-Hadamard and Hölder inequalities were presented. Some relationships between the means were granted as applications of established improvements. In addition, some estimations of the Csiszár divergence and its associated cases were received as further applications of the obtained results. The major techniques employed in formulating the proposed improvements included the Jensen inequality and the concept of convexity.

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.

Objective: The objectives of this research are twofold. The primary goal is to introduce, investigate, and contrast consolidative multi-criteria decision-making (C-MCDM) approaches. The second objective is the investigation of five alternative additive manufacturing materials. Methods: It integrates the subjective and objective weights using the Bayes hypothesis in conjunction with a normal method. Chang's Extent Analysis Method under fuzzy logic is used to estimate subjective weights and the CRITIC approach is used for assessing objective weights. Ranking techniques, including the simple ranking process (SRP), multi-objective optimization based on ratio analysis (MOORA), measurement alternatives and ranking according to compromise solution (MARCOS), and technique for order preference by similarity to ideal solution (TOPSIS) are applied. It also encompasses sensitivity analysis based on Kendall's coefficient of concordance and rank reversal phenomenon analysis. Spearman's rank correlation coefficient, a weighted rank measure of correlation, and rank similarity coefficient are among the metrics used to evaluate agreement between different approaches. It entails gathering expert opinions regarding the importance of various criteria as well as conducting extensive experiments. Results: The findings of the study indicate that polylactic acid is the best material to use for orthoses. When compared to the other MCDM approaches being discussed, SRP is the most reliable approach. It is also demonstrated that the SRP, MARCOS, and TOPSIS methods are rank reversal-free. Furthermore, SRP exhibits a very poor association with the TOPSIS technique but a strong correlation with the MOORA and MARCOS approaches. Conclusions: To ensure results reliability, it is necessary to consider both the subjectivity and objectivity of weights as well as apply multiple MCDM methodologies in addition to sensitivity analysis.

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.

The preparation and tribological behavior of the titanium metal matrix (Ti-6Al-4V) composite reinforced with tungsten carbide (WCp) and graphite (Grp) particles were investigated in this study. The stir casting procedure was used to fabricate the titanium metal matrix composites (TMMCs), which had 8 weight percent of WCp and Grp. The tribological studies were designed using Taguchi’s L27 orthogonal array technique and were carried out as wear tests using a pin-on-disc device. According to Taguchi’s analysis and ANOVA, the most significant factors that affect wear rate are load and distance, followed by velocity. The wear process was ascertained by scanning electron microscopy investigation of the worn surfaces of the composite specimens. Pearson’s heatmap and Feature importance (F-test) were plotted for data analysis to study the significance of input parameters on wear. Machine learning classification algorithms such as k-nearest neighbors, support vector machine, and XGBoost algorithms accurately classified the wear rate data, giving an accuracy value of 71.25%, 65%, and 56.25%, respectively.

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.

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.

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.

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.

Due to continuous growth in electric demand and increasing connection of renewable energy, the power systems are being operated with smaller stability margins. Therefore, a sufficient loading margin is essential to maintain the system secure and ensure voltage stability. To this end, this paper studies the joint optimization of transmission capacity and wind power investment problem, the unique feature of which is incorporating voltage stability margin (VSM) in the planning model. A bi-level model has been formulated whose upper level minimizes the total investment and operation cost minus the weighted VSM. The lower level evaluates the VSM given the optimal expansion plan from the upper level. In addition, the stochastic nature of wind power and load can impact voltage stability. Thus, uncertainties related to intermittent wind generation and demand must be modeled. We use an approximated linear representation to model the AC power system at both levels of the problem. The duality theory (primal-dual formulation) is utilized to transform bi-level programming into single-level mathematical programming. The validity of the constructed methodology is demonstrated on the IEEE 24-bus RTS, which indicates the efficacy and feasibility of the presented model.

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 .

Thalassemia represents one of the most common genetic disorders worldwide, characterized by defects in hemoglobin synthesis. The affected individuals suffer from malfunctioning of one or more of the four globin genes, leading to chronic hemolytic anemia, an imbalance in the hemoglobin chain ratio, iron overload, and ineffective erythropoiesis. Despite the challenges posed by this condition, recent years have witnessed significant advancements in diagnosis, therapy, and transfusion support, significantly improving the prognosis for thalassemia patients. This research empirically evaluates the efficacy of models constructed using classification methods and explores the effectiveness of relevant features that are derived using various machine-learning techniques. Five feature selection approaches, namely Chi-Square (χ2), Exploratory Factor Score (EFS), tree-based Recursive Feature Elimination (RFE), gradient-based RFE, and Linear Regression Coefficient, were employed to determine the optimal feature set. Nine classifiers, namely K-Nearest Neighbors (KNN), Decision Trees (DT), Gradient Boosting Classifier (GBC), Linear Regression (LR), AdaBoost, Extreme Gradient Boosting (XGB), Random Forest (RF), Light Gradient Boosting Machine (LGBM), and Support Vector Machine (SVM), were utilized to evaluate the performance. The χ2 method achieved accuracy, registering 91.56% precision, 91.04% recall, and 92.65% f-score when aligned with the LR classifier. Moreover, the results underscore that amalgamating over-sampling with Synthetic Minority Over-sampling Technique (SMOTE), RFE, and 10-fold cross-validation markedly elevates the detection accuracy for αT patients. Notably, the Gradient Boosting Classifier (GBC) achieves 93.46% accuracy, 93.89% recall, and 92.72% F1 score.

Abstract Stringent emission standards and greater fuel economy have forced scientists to develop an advanced combustion technology (homogeneous charge compression ignition; HCCI) useful in internal combustion engines in the future. Citronella biodiesel prepared through transesterification process which is auspicious an energy source for the CI engine. Cobalt chromium nanoparticles were mixed with the biodiesel at 30 ppm with the help of an ultrasonicator. The present experimental investigation was conducted to analyze various performance (brake thermal efficiency (BTE) and brake‐specific fuel consumption (BSFC)), combustion (pressure and heat release rate (HRR)), and emission (unburnt hydrocarbon (UBHC), CO, NOx, and smoke) parameters of the HCCI engine with neat diesel, CBD 5% (citronella biodiesel 5% + 95% diesel), CBD 10% (citronella biodiesel 10% + 90% diesel), CBD 15% (citronella biodiesel 15% + 85% diesel), CBD 20% (citronella biodiesel 20% + 80% diesel), and CBD 15% + C30 (citronella biodiesel 15% + 85% diesel +30 ppm cobalt). To carry out the experiment, a fuel vaporizer was adopted in a 5.2 kW, 1500 rpm, single‐cylinder, four‐stroke, vertical, direct‐injection diesel engine. Nanoparticles were used to improve surface area/volume ratio, heat conduction, and heat transfer rate within the oil layers. The result showed that CBD 15% had given better results than the other citronella biodiesel‐blended fuels. CBD 15% + C30 fuel increased the BTE and HRR by 5.49% and 6.8%, respectively, due to a shorter ignition delay and greater cetane number of the fuel. The percentage of BSFC, UBHC, CO, NOx, and smoke was decreased by 33.33%, 34.32%, 5.1%, and 17.34%, respectively, compared to neat biodiesel in the HCCI engine at 80% load.

The study proposes a way of developing granular models based on optimized subsets of data with different sampling sizes, in which three generally used models, namely Support Vector Machine, K-Nearest Neighbor, and Long Short-Term Memory, are designed and transformed into granular version for achieving a good performance with sufficient functionality. First, a collection of subsets are determined using different sampling methods, which are subsequently applied to play as an essential prerequisite of the proposed models. Then, the principle of justifiable granularity is utilized to the design of interval information granules based on the subsets of data. The design process is associated with a well-defined optimization problem realized by achieving a sound compromise between two conflicting criteria: coverage and specificity. To evaluate the performance of the granular models, two aspects are considered: (i) sampling methods used in determining suitable subsets of data; (ii) different models applied to be transformed into granular models. A series of experimental studies are conducted to verify the feasibility of the proposed granular models.

Multifunctional membrane technology has gained tremendous attention in wastewater treatment, including oil/water separation and photocatalytic activity. In the present study, a multifunctional composite nanofiber membrane is capable of removing dyes and separating oil from wastewater, as well as having antibacterial activity. The composite nanofiber membrane is composed of cellulose acetate (CA) filled with zinc oxide nanoparticles (ZnO NPs) in a polymer matrix and dipped into a solution of titanium dioxide nanoparticles (TiO2 NPs). Membrane characterization was performed using transmission electron microscopy (TEM), field emission scanning electron microscopy (FESEM), and Fourier transform infrared (FTIR), and water contact angle (WCA) studies were utilized to evaluate the introduced membranes. Results showed that membranes have adequate wettability for the separation process and antibacterial activity, which is beneficial for water disinfection from living organisms. A remarkable result of the membranes’ analysis was that methylene blue (MB) dye removal occurred through the photocatalysis process with an efficiency of ~20%. Additionally, it exhibits a high separation efficiency of 45% for removing oil from a mixture of oil–water and water flux of 20.7 L.m−2 h−1 after 1 h. The developed membranes have multifunctional properties and are expected to provide numerous merits for treating complex wastewater.

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.

Parallel machines scheduling problems with continuous availability of machines are NP-hardness (non-deterministic polynomial-time hardness) and have become very popular for the last decade; there is still very limited literature on this problem. The purpose of this paper is to focus on the problem of scheduling n independent jobs to be processed on m unrelated identical parallel machines with availability constraints to minimize the maximum completion time of jobs (makespan). For this NP-hard problem, a mixed-integer linear programming (MILP) model is proposed to find an optimal solution for this problem. Two metaheuristics, tabu search (TS) and simulated annealing (SA) are proposed to solve large scale problem. Moreover, the performance of the solution obtained by the proposed metaheuristics is evaluated based on a lower bound, which decreases the time required to find the optimal solution. Extensive experiments are carried out to assess the performance of all proposed metaheuristics. The computational results highlight the ability of the proposed metaheuristics to obtain optimal solutions for most of the instances compared with the solutions of the proposed MILP model and lower bounds. Moreover, SA and TS can provide good efficiency for the problem in any jobs size and any machine size, but TS provides worse CPU time as the size of jobs become large.

Reliability modeling and deadlock control have become important issues in automated manufacturing systems (AMSs) with reliable and unreliable resources. Therefore, this paper develops a reliability modelling approach and deadlock control based on colored resource-oriented timed Petri net (CROTPN) and neural networks to find crucial reliability measures in AMSs. In the first step, a CROTPN with considering resource failures is developed to obtain “sufficient and necessary conditions” for the CROTPN liveness. In the second step, a fault diagnosis and treatment approach is proposed that integrates the obtained unreliable net with neural networks to ensure that the system is reliable. Furthermore, a neural unreliable CROTPN is used to analyze the system reliability. A simulation is carried out to illustrate the approach and compare the results to those found in the literature. The developed approach has been proved to be simpler in structure and can solve the deadlock problem and model AMS reliability.

Intracranial hemorrhage (ICH) can lead to death or disability, which requires immediate action from radiologists. Due to the heavy workload, less experienced staff, and the complexity of subtle hemorrhages, a more intelligent and automated system is necessary to detect ICH. In literature, many artificial-intelligence-based methods are proposed. However, they are less accurate for ICH detection and subtype classification. Therefore, in this paper, we present a new methodology to improve the detection and subtype classification of ICH based on two parallel paths and a boosting technique. The first path employs the architecture of ResNet101-V2 to extract potential features from windowed slices, whereas Inception-V4 captures significant spatial information in the second path. Afterwards, the detection and subtype classification of ICH is performed by the light gradient boosting machine (LGBM) using the outputs of ResNet101-V2 and Inception-V4. Thus, the combined solution, known as ResNet101-V2, Inception-V4, and LGBM (Res-Inc-LGBM), is trained and tested over the brain computed tomography (CT) scans of CQ500 and Radiological Society of North America (RSNA) datasets. The experimental results state that the proposed solution efficiently obtains 97.7% accuracy, 96.5% sensitivity, and 97.4% F1 score using the RSNA dataset. Moreover, the proposed Res-Inc-LGBM outperforms the standard benchmarks for the detection and subtype classification of ICH regarding the accuracy, sensitivity, and F1 score. The results prove the significance of the proposed solution for its real-time application.

COVID-19 (SARS-CoV-2) was declared as a global pandemic by the World Health Organization (WHO) in February 2020. This led to previously unforeseen measures that aimed to curb its spread, such as the lockdown of cities, districts, and international travel. Various researchers and institutions have focused on multidimensional opportunities and solutions in encountering the COVID-19 pandemic. This study focuses on mental health and sentiment validations caused by the global lockdowns across the countries, resulting in a mental disability among individuals. This paper discusses a technique for identifying the mental state of an individual by sentiment analysis of feelings such as anxiety, depression, and loneliness caused by isolation and pauses to the normal chains of operations in daily life. The research uses a Neural Network (NN) to resolve and extract patterns and validate threshold trained datasets for decision making. This technique was used to validate 2,173 global speech samples, and the resulting accuracy of mental state and sentiments are identified with 93.5% accuracy in classifying the behavioral patterns of patients suffering from COVID-19 and pandemic-influenced depression.

Many multi-agent systems (MASs) can be regarded as hybrid systems that contain continuous variables and discrete events exhibiting both continuous and discrete behavior. An MAS can accomplish complex tasks through communication, coordination, and cooperation among different agents. The complex, adaptive and dynamic characteristics of MASs can affect their stability that is critical for MAS performance. In order to analyze the stability of MASs, we propose a stability analysis method based on invariant sets and Lyapunov’s stability theory. As a typical MAS, an unmanned ground vehicle formation is used to evaluate the proposed method. We design discrete modes and control polices for the MAS composed of unmanned ground vehicles to guarantee that the agents can cooperate with each other to reliably achieve a final assignment. Meanwhile, the stability analysis is given according to the definition of MAS stability. Simulation results illustrate the feasibility and effectiveness of the proposed method.

Sperm morphological analysis (SMA) is an essential step in diagnosing male infertility. Using images of human sperm cells, this research proposes a unique sequential deep-learning method to detect abnormalities in semen samples. The proposed technique identifies and examines several components of human sperm. In order to conduct this study, we used the online Modified Human Sperm Morphology Analysis (MHSMA) dataset containing 1540 sperm images collected from 235 infertile individuals. For research purposes, this dataset is freely available online. To identify morphological abnormalities in different parts of human sperm, such as the head, vacuole, and acrosome, we proposed sequential deep neural network (SDNN) architecture. This technique is also particularly effective with low-resolution, unstained images. Sequential deep neural networks (SDNNs) demonstrate high accuracy in diagnosing morphological abnormalities based on the given dataset in our tests on the benchmark. Our proposed algorithm successfully detected abnormalities in the acrosome, head, and vacuole with an accuracy of 89%, 90%, and 92%, respectively. It is noteworthy that our system detects abnormalities of the acrosome and head with greater accuracy than current state-of-the-art approaches on the suggested benchmark. On a low-specification computer/laptop, our algorithm also requires less execution time. Additionally, it can classify photos in real time. Based on the results of our study, an embryologist can quickly decide whether to use the given sperm.

Automated manufacturing systems (AMSs) are prone to be in deadlock states when resource allocation is unreasonable. Reasonably allocating system resources to achieve deadlock control is a primary task of the design of an AMS. This paper proposes a multi-step look-ahead deadlock prediction method to obtain an optimal deadlock avoidance policy for a class Petri nets, without calculating a complete reachability graph. As the reachability graph of a large-scale Petri net model is usually large, the analysis and calculation process to obtain the optimal deadlock avoidance policy is complicated. We first simplify a Petri net model by using the existing Petri net reduction methods or removing non-shared resources to simplify the model structure. Then we calculate the dead zone markings of the simplified model through a reverse generation method, and finally develop a multi-step look-ahead deadlock prediction method to obtain an optimal deadlock avoidance policy for a class of AMSs.

In this paper, a model for integrating production scheduling and maintenance planning is proposed for flow shop production system. The suggested model in this paper is based on the optimal jobs sequence for jobs that will be processed in multiple machines connected in series. The objective of this study is to find the optimal sequence for jobs while reducing the total production and maintenance costs. The model works by generating an initial solution using longest processing time (LPT) dispatching rule. Then, tabu search algorithm is established to obtain the optimal sequence for jobs. Computational experiments are performed on problems with five serially machines which are assigned to process eight diverse jobs from the same product family. The result is compared with the genetic algorithm optimization technique under individual PM scheme for obtaining superior solutions that has been proved in the literature to be one of the best approach. The computational results show that the recommended approach is qualified over the simulation based genetic algorithm optimization technique.

In recent years, system maintenance has become a hot topic that has attracted significant research interests from both academia and industry, and has found applications in many areas. A modern system usually consists of many components, among which there may be different dependencies. The maintenance strategy that ignores the dependencies among components cannot meet the actual engineering requirements and may even affect the usability of the entire system. Therefore, in this paper, we study the economic dependence of equipment cost and duration sharing when components are replaced at the same time. By characterizing the relationship between the effective service age and reliability of components using the Weibull distribution, we establish a selective maintenance model of multi-state complex system with economic dependence. The discrete artificial bee algorithm is used as the global optimization method to solve the dual- objective optimization model and obtain the Pareto solution set of the maintenance level of each component. A case study of the Shooman network system is performed to demonstrate the usefulness of the model and the importance of considering the economic dependencies between components in maintenance decisions.

We present an innovative approach for 2D person pose estimation by developing a convolutional neural network for human 2-channel mask prediction and human 2D pose estimation. Conceptually our idea is simple, inspired by prior image segmentation research in general. We establish a perception that explicitly encoded mask data can be served as a critical feature for person pose estimation. We propose a convolution neural network model by combining the image segmentation technique with the bottom-up approach for human pose estimation. We observe that the construction of a two stage-network for training in an end-to-end manner is beneficial to one another: for person mask prediction and 2D person pose estimation. At the pose estimation stage, we detect heat-maps against the person keypoints location from the mask information and their mutual connection relations. They are then used to estimate an ultimate pose in a way to remove the unwanted or occluded keypoints, as those keypoints may propagate across the network and lead to redundant pose estimation. We train and test our system on the MS-COCO dataset, and the experimental results validate the superior efficiency of the proposed methodology.

Previously, different deadlock control strategies for automated manufacturing systems (AMSs) based on Petri Nets with reliable resources have been proposed. However, in real-world applications, resources may be unreliable. Therefore, deadlock control strategies presented in previous research studies are not suitable for such applications. To address this issue, this paper proposes a novel three-step deadlock control strategy for fault detection and treatment of unreliable resource systems. In the first step, a controlled system (deadlock-free) is obtained using the “Maximum Number of Forbidding First met Bad Markings Problem 1” (MFFBMP1), which does not consider resource failures. Subsequently, all obtained monitors are merged into a single monitor based on a colored Petri net. The second step addresses deadlocks caused by resource failures in the Petri net model using a common recovery subnet based on colored Petri nets. The recovery subnet is applied to the system obtained in the first step to ensure that the system is reliable. The third step proposes a hybrid approach that combines neural networks with colored Petri nets obtained from the second step, for the detection and treatment of faults. The proposed approach possesses the advantages of modular integration of Petri nets and can also learn neurons and reduce knowledge, similar to neural networks. Therefore, this approach solves the deadlock problem in AMSs and also detects and treats failures. The proposed approach was tested using an example from literature.

Titanium (Ti) and its alloys are predominant choices for use as biomaterials in human implants. Research has shown the adverse effects of using commercial Ti alloy Ti-6Al-4V in the human body, and this presents a need for viable alternatives. In this study, Ti alloy Ti-17Nb-6Ta was manufactured by laser cladding—a prominent additive manufacturing (AM) technology. Laser cladded specimens were evaluated for their in vitro and electrochemical behavior. A human osteosarcoma cell line (MG-63 cells) was used for in vitro investigations. Cell proliferation was good in the physiological medium, and cells were alive when in contact with the laser cladded alloy, even after two to three weeks, indicating good cell viability and compatibility with this alloy. Electrochemical characterization was carried out in Ringer’s solution, and noticeably lower corrosion current density and corrosion rate values were observed. The lower amounts of these parameters indicated the passivation behavior due to multi-layer Ti, Nb, and Ta alloy oxide films. These oxide films also enhanced osseointegration. Thus, the Ti-17Nb-6Ta alloy can be an ideal biocompatible alternative to Ti-6Al-4V.

Exposure of the engine valve to high temperatures led to the degradation of the valve material due to microstructural instability and deteriorating mechanical properties. Performance enhancement and alteration in microstructures can be attained through the powder metallurgy route which is a viable method to produce near net shape components. In this current study, the development of austenitic stainless steel (21-4N) through the powder metallurgy route as an alternate material for engine valves was investigated. Mechanical alloying was carried out for the pre-alloyed mixtures and consolidated using vacuum hot pressing. Sintering parameters were fixed at 1200 °C, 50 MPa and at a vacuum level of 10-3 Torr. A scanning electron microscope was used to analyze the morphology of the milled powders. Densities for the hot pressed powders were compared with theoretical densities and found to be around 98–99%. Observations regarding grain size, the presence of austenitic grain, heterogeneous distribution of metal carbides and analysis of chemical composition along the metal matrix were determined using both optical and electron microscopes. X-ray diffraction was carried out for both the consolidated and powder samples. The hot pressed samples exhibited a hardness value of 410 ± 10 Hv. An isothermal compression test for the sintered samples was carried out at a temperature of 650 °C and strain rate of 0.001 s−1. It is showed that the compressive strength of 1380 MPa. An analysis between the room temperature yield strength obtained from hardness measurement and the strengthening mechanism based on the microstructure was conducted. Grain size, dislocation and solid solution are the major strengthening mechanisms which strengthen the material. Overall, the development of valve steel material through the powder metallurgy route exhibited improved metallurgical and mechanical properties in comparison to the corresponding cast product.