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Abstract Monel K-500 is a high-performance superalloy composed of nickel and copper, renowned for its exceptional strength, hardness, and resistance to corrosion. To machine this material more precisely and accurately, Electrical Discharge Machining (EDM) is one of the best choices. In EDM, material removal rate (MRR) and electrode wear rate (EWR) are crucial performance parameters that are often conflicting in nature. These parameters depend on several input variables, including peak current (Ip), pulse on time (Ton), duty cycle (Tau), and servo voltage (SV). Optimizing the EDM process is essential for enhancing performance. In this research, a set of experiments were conducted using EDM on Monel K500 alloy to determine the optimal process parameters. The Box–Behnken design was used to prepare the experimental design matrix. Utilizing the experimental data, a second-order mathematical model was developed using Response Surface Methodology (RSM). R2 value is found to be 99.40% and 96.60% for MRR and EWR RSM-based prediction model, respectively. High value of R2 is indicated is indicated good adequacy for prediction. The mathematical model further used in multi-objective dragonfly algorithm (MODA): a new meta-heuristic optimization technique to solve multi-objective optimization problem of EDM. The MODA is a very useful technique to achieve optimal solutions from the multi decision criteria. Utilizing this technique, a set of non-dominated solutions was obtained. Further, the TOPSIS method was used to determine the most desirable optimal solution, which was found to be 0.0135 mm3/min for EWR and 6.968 mm3/min for MRR. These results were obtained when the optimal process parameters were selected as Ip = 6 A, Ton = 200 µs, Tau = 12, and SV = 41.6 V. Operators can machine Monel K500 by selecting the above-mentioned optimal parameters to achieve the best performance.

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.

Natural fibers have in recent times turned into reasonable to automotive production as a substitute corroboration for synthetic fiber reinforced thermoplastics. This research was motivated by evolving and assessing benzoyl-treated Neem and Pineapple fiber reinforced and particulated by sawdust in hybrid epoxy polymers for automotive. Seven types of composites were fabricated using hand layup technique. The prepared composites were cut according to their ASTM standard. The Hybrid Neem and Pineapple Fiber Reinforced with Saw dust Particulated Epoxy Composite (NPFRSPEC) was studied for thermal analysis like Thermal Conductivity (TC), Coefficient of Linear Thermal Expansion (CLTE), Heat Deflection Temperature (HDT) and Thermal Gravimetric Analysis (TGA). Morphological analysis was done through FESEM on failure surfaces. The results reveal that treatment fibers enhanced the thermal stability among the non-treated composite. The treated fibers exhibited 10% increase in heat deflection temperature and 28% increase in thermal conductivity. From the results, it’s also noticed that the treated hybrid composite delivers better thermal efficiency when compared with other natural fiber-reinforced polymers available in the sector which could potentially replace long fiber reinforced thermoplastic components in car doors and bumpers.

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.

Oil and gas industries are facing a special dilemma when it comes to high-pressure, high-temperature (HPHT) drilling as the accurate forecasting of the drilling fluid density (DFD) is a vital factor for safe and efficient operations. Complicated relationships and inconsistencies in HPHT situations are rarely mapped by current forecasting models, while their buggy performance and safety risks during drilling can be underestimated. In this research, we propose a novel machine learning (ML) approach to enhance the accuracy of DFD anticipation under HPHT conditions: central force search-adaptive extreme gradient boosting (CFS-XGB). This paper uses a dataset that has drilling variables together with the DFD for HPHT situations to examine the accuracy of the CFS-XGB model. Excluding the abnormalities of data or mistakes, the reliability of the original data is maintained by applying min–max normalization. After that, finding the important features with the help of the boosted principal component analysis (BPCA) approach to the normalized data will ensure a major improvement in the CFS-XGB methodology’s prediction efficacy. This research is experimented in the Python platform, and the performance of the proposed CFS-XGB method is analyzed in terms of MSE, R2, and AAPRE metrics. The suggested approach performs better than the current methods in forecasting the drilling fluid concentration in HPHT settings, according to the experimental data. This development in predictive modeling helps increase the productivity and safety of drilling operations, which will eventually help the oil and gas sector manage the challenges posed by HPHT drilling settings.

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.

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.

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.

This investigation delves into the behavior of copper oxide (CuO) as a solid lubricant inside zirconia toughened alumina (ZTA) ceramic composites. The investigation starts with the preparation of ZTA through co-precipitation followed by powder metallurgy to develop CuO (1.5 wt. %)/ZTA composites. In all cases, hot isotactic pressing is applied for densification. The fully densified samples are thoroughly mirror-polished to investigate the mechanical and tribological properties. A 1.8% reduction in micro-hardness and 6% improvement in fracture toughness are observed with incorporation of CuO into the ZTA matrices. The analysis reveals that the presence of ionic copper at the grain boundary leads to the formation of copper-rich phases, causing a decrease in hardness. However, the softer CuO particles contribute to crack bridging and crack deflection, enhancing fracture toughness. Subsequent investigation into the tribological properties highlights the positive influence of the softer CuO phases acting as a secondary component within the ZTA matrix. A significant enhancement of 39.34% in the Coefficient of Friction (COF) is achieved by incorporating CuO into the ZTA matrix. This improvement can be attributed to the formation of a patchy layer through smearing and squeezing actions on wear debris during sliding. The uniform patchy layer results in smoother and more polished surfaces, leading to an improvement in both the COF and specific wear rate. Further wear analysis reveals various phenomena contributing to surface wear, including pullout of grain particles, micro-fracture, high abrasions, and laminar removal of grains. Overall, the introduction of CuO proves to be beneficial, showcasing improved mechanical and tribological properties in the developed composites, with application in dies, inserts, sparkplugs, etc.

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 increase in road accidents underscores the urgent need for effective methodologies to evaluate and prioritize road safety improvements. Traditional decision-making processes in road safety management often confront challenges due to the lack of a comprehensive approach, particularly in handling multiple evaluation criteria. This study introduces a novel Hybrid Multi-Criteria Decision-Making approach that amalgamates the Best-Worst Method (BWM), the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and the Simple Additive Weighting (SAW) method. This approach is designed to prioritize road safety improvements effectively by analyzing various criteria and alternatives in a structured manner. Focusing on a 500-meter road section, the study identifies eight distinct road improvement criteria and divides the road section into five sub-sections for detailed analysis. The BWM is utilized to determine the criteria weights, which are subsequently integrated into the TOPSIS and SAW methodologies for prioritizing improvements in each road subsection. This hybrid approach provides a comprehensive framework for decision makers, including road safety auditors and transportation professionals, facilitating a nuanced and systematic evaluation of safety improvements. The methodology’s efficacy is validated through field expert consultations and comparative analysis with standalone SAW results. The validation underscores the potential of the proposed approach as a robust tool for road safety stakeholders, enabling them to make informed decisions based on a detailed, Chainage-wise analysis of road sections. The Python code for TOPSIS is available at https://github.com/kanak02/TOPSIS.

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 investigates a novel methodology to intricately craft a HAMMC and thoroughly examine its multifaceted mechanical and tribological characteristics. By combining silicon carbide (SiC) and fly ash as reinforcements, a unique identity is bestowed upon this hybrid composite, enhancing its structural integrity and functional attributes. Stir casting is the chosen methodology for fabricating this composite, favored for its economic viability and suitability for large-scale manufacturing. In this research, the emphasis is on developing a cost-effective composite that not only meets stringent economic considerations but also exhibits improved material properties. Within the realm of hybrid metal matrix composites, the well-regarded Al6061 takes on the role of the matrix material, while the synergistic inclusion of fly ash and SiC serves as reinforcing constituents. Three specimens with compostion 90% Al6061 + 5% SiC +5% Fly ash, 90% Al6061 + 10% SiC +6% Fly ash and 90% Al6061 + 15% SiC +7% Fly ash were fabricated. To unravel the intricacies of the fabricated Al6061 metal matrix composite, comprehensive tests are employed. These tests, including the Pin-on Disc test, Scratch test, Rockwell Hardness test, and Charpy Impact test, collectively work to unveil the nuanced tribological and mechanical behaviors encapsulated within this innovative alloy. The results indicated significant improvement in wear resistance in specimen comprising 78% Al6061 + 15% SiC +7% Fly Ash and volumetric loss found to have 0.96 g. Superior hardness characteristics and enhanced abrasion resistance found in 78% Al6061 + 15% SiC +7% Fly Ash than other two specimens. The highest impact strength exhibited in 90% Al 6,061 + 5% SiC +5% Fly ash specimen.

This study compares conventional turning (CT) and ultrasonic vibration-assisted turning (UVAT) in machining hardened AISI 52100 steel (62 HRC) with a PVD-coated TiAlSiN carbide tool. UVAT experiments, utilizing an ultrasonic frequency of 20 kHz and vibration amplitude of 20 µm, varied the cutting speed, feed, and depth of cut. Remarkably, UVAT reduced tool wear, extending tool longevity. Surprisingly, power consumption showed no significant differences between CT and UVAT. Mathematical models based on experimental data highlight the substantial impact of the cutting speed on tool wear, followed closely by the depth of cut. For power consumption, the depth of cut took precedence, with the cutting speed and feed rate playing pronounced roles in UVAT. This emphasizes the potential for further research on machinability, particularly exploring different vibration directions on the tool in feed, tangential, and radial aspects.

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.

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.

Electrochemical Drilling (ECD) is an unconventional method aimed at creating holes in metallic workpieces characterized by high hardness and complex structures. This study analyzes the influence of process variables, including machining voltage, electrolyte concentration, electrode rotational speed, electrolyte flushing pressure, and workpiece material, on the novel hole performance index (HPI) in electrical discharge machining (ECD). The HPI was identified as a suitable metric for simultaneously evaluating hole geometry and drilling time across various machining parameters and workpiece materials. The analysis of variance (ANOVA) method was employed to determine the significance of each machining parameter and workpiece material on the HPI. The research employed signal-to-noise ratio analysis to identify the optimal machining parameters. The findings demonstrated that the workpiece material and machining voltage were significant factors influencing HPI. The validation tests demonstrated that the proposed statistical method can significantly reduce HPI.

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.

The current research examines the effectiveness of cryogenically treated (CT) tungsten carbide cutting inserts on Custom450 stainless steel using multi-objective soft computing approaches. The Taguchi-based L27 orthogonal array was employed in the experiments. During milling operations, cutting force, surface roughness, and cutting temperature were measured at different spindle speeds (rpm), feed rates (mm/min), and constant depths of cut (mm). The surface roughness and chip morphology of the Custom 450 stainless steel machined by cryo-treated (CT) and untreated (UT) cutting tool inserts were compared across various responses to cutting temperature and force. This paper also carried out multi-objective optimization, employing algorithm techniques such as Grasshopper Optimization Algorithm (GHO), Grey Wolf Optimization(GWO), Harmony Search Algorithm(HAS), and Ant line Optimization (ALO). The Multi-objective Taguchi approach and TOPSIS were first used to optimize the machining process parameters (spindle speed, feed rate, and cryogenic treatment) with different performance characteristics. Second, to relate the machining process parameters with the performance characteristics (cutting force, cutting temperature, and surface roughness), a mathematical model was developed using response surface analysis. The created mathematical response model was validated using ANOVA. The results showed that in IGD values of GHO, GWO, HSA and ALO module had 2.5765, 2.4706, 2.3647 and 2.5882 respectively, ALO has the best performance indicator. A Friedman’s test was also conducted, revealing higher resolution with the ALO method than with the HSA, GWO, and GHO methods. The results of the scanning test show that the ALO approach is workable.

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.

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.

Understanding the temperature–working condition relationship is crucial for optimizing machining processes to ensure dimensional accuracy, surface finish quality, and overall spindle longevity. Monitoring and controlling spindle temperature through appropriate cooling systems and operational parameters are essential for efficient and reliable machining operations. This paper presents an in-depth analysis of the thermal equilibrium and deformation characteristics of a high-speed motorized spindle unit utilized in grinding machine tools. Through a series of thermal equilibrium experiments and meticulous data acquisition, the study investigates the nuanced influence of various working conditions, including spindle speeds, coolant types, and coolant flow rates, on spindle temperatures and thermal deformations. Leveraging the power of Artificial Neural Networks (ANNs), predictive models are meticulously developed to accurately forecast spindle behavior. Subsequently, the models are seamlessly transitioned to a cloud computing infrastructure to ensure remote accessibility and scalability, facilitating real-time monitoring and forecasting of spindle performance. The validity and reliability of the predictive models are rigorously assessed through comparison with experimental data, demonstrating excellent agreement and high accuracy in forecasting spindle thermal behavior. Furthermore, the study underscores the critical role of key working condition variables as precise predictors of spindle temperature and thermal deformation, emphasizing their significance in optimizing overall spindle efficiency and performance. This comprehensive analysis offers valuable insights and practical implications for enhancing spindle operation and advancing the field of grinding machine tools.

Multi-walled carbon nanotubes (MCNTs)-enhanced glass fiber composite (GFC) laminates are among the most promising materials for fulfilling various structural and non-structural requirements. They have also shown exceptional functional applications as excellent electrical and thermal conductors, as well as electromagnetic interference shielding materials. The present work primarily focuses on developing regression models for the drilling process of 0.3 wt% MCNTs-GFC laminates. For experimentation, three different coated drills—carbide, TiCN-coated, and TiAlN-coated—are used under both dry and chilled air cutting environments. The lowest thrust force, torque, and delamination factor were observed at a feed rate of 10 mm min ^−1 and a speed of 1500 RPM using a TiCN-coated drill in a chilled air environment. Regression analysis reveals that feed rate significantly influences thrust force, as justified by the R ^2 value, which is above 90% for the selected cutting conditions. The corresponding t and F statistics values indicate the statistical significance of the relevant explanatory factors. The efficiency of the developed models is further validated by considering the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values, which are 136.9 and 144.7, respectively. These values indicate a good regression fit and likelihood of the models for data prediction. Additionally, there is a strong correlation (coefficient > 0.85) between thrust force and delamination factor under the selected cutting environments. Concurrently, the developed regression models are simulated and evaluated for random experiments (Nos. 87, 125, 187, 243, 244, and 399), and the predicted responses closely match the experimental values.

Domination plays an indispensable role in graph theory. Various types of domination explore various types of applications. Equal-status people work together and interlace with each other easily. In this paper, the paired equitable domination of a graph, its inflated graph, and its complement of an inflated graph were studied. The relationship between the domination number of the graph, the equitable domination number, and the paired equitable domination number of complements of the inflated graph were established. Furthermore, we proved the Nordhaus–Gaddum-type inequality, that is, γpre(H)+γpre(H)≤6 if H is a graph with m nodes where m≡0, 2(mod 8) and d(ai) = (m/2) for all ai. The challenges and limitations of this parameter of paired equitable and equitable domination depends on the degree of the vertex of the graph. Practical applications are discussed in various fields and illustrated using the studied parameter.

Article describes CNC milling center programing during 3 axis finish milling. Article is focused on setting the tolerance of toolpaths during finish milling in CAM systems. Finding of suitable toolpaths during finish milling is very time consuming and can be expensive. Target is to recommend specific settings of tolerance in CAM system with the respect to achieved accuracy, machining time, surface roughness and quantity of NC blocks. The aim is also to compare the practical results of machining with predicted simulation. The methodology evaluating this problem is based on the following steps: experimental sample design for 3axis milling, accuracy prediction of machined samples, production of samples using CNC milling center, analysis of accuracy and surface roughness with respect to shape of workpiece. The result is the variance of shape accuracy deviations from the specified CAD model of the workpiece, focused on individual areas of its shape. The research results show milled surface errors depending on the toolpath tolerances. It is not preferred to set very low tolerance in the CAM system with respect to increasing machining time.

A study of two warehouse inventory systems with a production unit is developed in this article with some constraints which are of practical applicability to optimize the total production cycle and its cost. A production unit evolves in three different states to retain its quality and prolong its lifetime: the state of producing items, the state of reworking the identified defective items, and the state of being idle. It processes the items up to a certain time point. The screening process starts immediately after a product comes out of the production unit. The classified non-defective items are first stored in own warehouse (OW), after filling to its maximum capacity, and the remaining items fill in the first block RW1 of the rental warehouse RW. All identified defective items are stored in the second block RW2 of RW. The holding cost of an item is higher in RW than OW. All defective items are sent to the production unit for re-do processes as a single lot immediately after the stop of the production and re-do items are stored in RW1 to satisfy the demand. The items in the RW1 are of higher priority in satisfying the demands after the stop of the production unit in producing new items as to deduce the total cost. Demand is assumed as both time and advertisement dependent and is encouraged once production starts. The deterioration rate differs in both warehouses. No backlog is entertained. The study is directed to achieve optimum total cycle cost towards the attainment of the optimum production time slot and the entire cycle of the system. We have arrived at explicit expressions for the total cost function of the entire production cycle. An analytic optimization process of the discriminant method is employed in the form of an algorithm to arrive at the optimum total cost. It provides a numerical illustration of a specific environment. The implications of the current research work are as follows. The optimum utility of production units in three different states in arriving at the optimum total cost is extensively studied with respect to deterioration, demand, and production rates. It also examined the influence of fluctuating deterioration, demand, and production parameters in arriving at optimum deterioration cost, holding cost, and total cycle cost, as they have important managerial insights. The effect of rental charges on the optimum total cost is examined as the system is used for multi-purpose storage.

Cryptography is a pivotal application of graph theory in ensuring secure communication systems. Modern cryptography is deeply rooted in mathematical theory and computer science practices. It is widely recognized that encryption and decryption processes are primarily outcomes of mathematical research. Given the increasing importance of safeguarding secret information or messages from potential intruders, it is imperative to develop effective technical tools for this purpose. These intruders are often well-versed in the latest technological advancements that could breach security. To address this, our study focuses on the efficacious combinatorial technique of graph networks using efficient domination and total magic labeling. The introduction of a graph network based on total magic labeling can significantly influence the network’s performance. This research introduces a novel combinatorial method for encrypting and decrypting confidential numbers by leveraging an efficient dominant notion and labeled graph.

The selection of an appropriate number of features and their combinations will play a major role in improving the learning accuracy, computation cost, and understanding of machine learning models. In this present work, 22 gray-level co-occurrence matrix features extracted from magnetic flux leakage images captured in steam generator tubes’ cracks are considered for developing a machine learning model to predict and analyze crack dimensions in terms of their length, depth, and width. The performance of the models is examined by considering R2 and RMSE values calculated using both training and testing data sets. The F Score and Mutual Information Score methods have been applied to prioritize the features. To analyze the effect of different machine learning models, their number of features, and their selection methods, a Taguchi experimental design has been implemented and an analysis of variance test has been conducted. The dynamic population gray wolf algorithm (DPGWO) has been adopted to select the best features and their combinations. Due to the two contradictory natures of performance metrics, Pareto optimal solutions are considered, and the best one is obtained using Deng’s method. The effectiveness of DPGWO is proved by comparing its performance with Grey Wolf Optimization and Moth Flame Optimization algorithms using the Friedman test and performance indicators, namely inverted generational distance and spacing.

This paper deals with material and dimensional bending analysis of bimetallic pipes with defined bending radii. Basic characteristics of selected nickel superalloys with analysis of Inconel 625 and 16Mo3 steel in terms of primary mechanical properties and chemical composition are presented. In the practical part where the bimetallic tube is analyzed in terms of material properties is also performed geometric dimensional analysis, for which test specimens with different bending radii are made. The capillary test preceded dimensional analysis to remove cracks on the outer bend surfaces of the bimetallic tube. The geometric dimensional analysis itself is carried out using a 3D scanner and also includes external dimensional inspection in longitudinal section with changes in the curvature of bimetallic pipe bends for different bending radii. The article concludes with an overall evaluation of the achieved results, especially with regard to the deviations of rounding in individual sections of the external dimension.

In this paper, a practical issue of the application of cubic boron nitride (CBN) cutting tools for the machining of steel 100Cr6 thin-walled bearing rings is addressed. Three geometries of the commercially available CBN cutting inserts were tested at different machining parameters. The effect of geometry was assessed in terms of surface integrity, considering 2D profile parameters, 3D topography of the surface, and residual stresses in the surface layer. The results were sometimes contradictory, since the same cutting inserts provided the largest and the lowest values of the topography parameters, dependent on different cutting conditions. In general, CBN cutting inserts with XCEL geometry ensured the smallest profile parameters Ra and Rz, and the spatial parameters of Smr1 exhibited the largest values among all tested tools, suggesting reduced wear resistance. Residual stresses analysis did not reveal any inacceptable or potentially dangerous surface layer state after machining.

This paper focuses on the study of the oscillatory behavior of fourth-order nonlinear neutral delay difference equations. The authors use mathematical techniques, such as the Riccati substitution and comparison technique, to explore the regularity and existence properties of the solutions to these equations. The authors present a new form of the equation: Δ(a(m)(Δ3z(m))p1−1)+p(m)wp2−1(σ(m))=0, where z(m)=w(m)+q(m)w(m−τ) with the following conditions: ∑s=m0∞1a(1p1−1(s))=∞. The equation represents a system where the state of the system at any given time depends on its current time and past values. The authors demonstrate new insights into the oscillatory behavior of these equations and the conditions required for the solutions to be well-behaved. They also provide a numerical example to support their findings.

The formal concept of lattice plays a vital role in knowledge discovery. Reduction of the attribute has many applications in machine learning technology and data mining fields. In this paper, we introduce an object ranking concept to define a consistency set and the reduction of the attributes by structural features. An incomplete information system works on the three-way concepts using the SE-ISI Context. The granular was emphasized with join (meet) irreducible sets using the object ranking concepts. A dual operator is defined based on the object ranking concepts and its properties and conditions are verified. Hence, this elaborates on the four kinds of reduction of the attributes. The ordered pairs give the knowledge of the attributes that deal with the interval set of both the approximation of rough set theory concerning the objects. Therefore, the relationship between four kinds of reduction of the attribute was appropriate to access the consistency set using the object ranking concepts by some of the theorems and examples.

Consider a simple connected fuzzy graph (FG) G and consider an ordered fuzzy subset H = {(u1, σ(u1)), (u2, σ(u2)), …(uk, σ(uk))}, |H| ≥ 2 of a fuzzy graph; then, the representation of σ − H is an ordered k-tuple with regard to H of G. If any two elements of σ − H do not have any distinct representation with regard to H, then this subset is called a fuzzy resolving set (FRS) and the smallest cardinality of this set is known as a fuzzy resolving number (FRN) and it is denoted by Fr(G). Similarly, consider a subset S such that for any u∈S, ∃v∈V − S, then S is called a fuzzy dominating set only if u is a strong arc. Now, again consider a subset F which is both a resolving and dominating set, then it is called a fuzzy resolving domination set (FRDS) and the smallest cardinality of this set is known as the fuzzy resolving domination number (FRDN) and it is denoted by Fγr(G). We have defined a few basic properties and theorems based on this FRDN and also developed an application for social network connection. Moreover, a few related statements and illustrations are discussed in order to strengthen the concept.

Accurate prediction of material defects from the given images will avoid the major cause in industrial applications. In this work, a Support Vector Regression (SVR) model has been developed from the given Gray Level Co-occurrence Matrix (GLCM) features extracted from Magnetic Flux Leakage (MFL) images wherein the length, depth, and width of the images are considered response values from the given features data set, and a percentage of data has been considered for testing the SVR model. Four parameters like Kernel function, solver type, and validation scheme, and its value and % of testing data that affect the SVR model’s performance are considered to select the best SVR model. Six different kernel functions, and three different kinds of solvers are considered as two validation schemes, and 10% to 30% of the testing data set of different levels of the above parameters. The prediction accuracy of the SVR model is considered by simultaneously minimizing prediction measures of both Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) and maximizing R2 values. The Moth Flame Optimization (MFO) algorithm has been implemented to select the best SVR model and its four parameters based on the above conflict three prediction measures by converting multi-objectives into a single object using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. The performance of the MFO algorithm is compared statistically with the Dragon Fly Optimization Algorithm (DFO) and Particle Swarm Optimization Algorithm (PSO).

This study deals with the selection of the cutting process using the grey fuzzy relation approach. The analysis was performed using plasma arc machining, laser beam machining, and abrasive waterjet machining on three different workpiece thicknesses with different cutting speeds. The objective was to select the best cutting process considering several performance characteristics such as machining time, dimensional accuracy, kerf width, and surface roughness. Data normalization, grey relation coefficients, fuzzy inference system, and grey fuzzy relation grade are used to evaluate the machining performances of the machining processes. The developed fuzzy model can be used to study the effects of different cutting processes on technological features. The results show that the grey fuzzy technique can be effectively used for the analysis and selection of cutting processes.

In this paper, the concept of a strong n-Connected Total Perfect k-connected total perfect k-dominating set and a weak n-connected total perfect k-dominating set in fuzzy graphs is introduced. In the current work, the triple-connected total perfect dominating set is modified to an n-connected total perfect k-dominating set nctpkD(G) and number γnctpD(G). New definitions are compared with old ones. Strong and weak n-connected total perfect k-dominating set and number of fuzzy graphs are obtained. The results of those fuzzy sets are discussed with the definitions of spanning fuzzy graphs, strong and weak arcs, dominating sets, perfect dominating sets, generalization of triple-connected total perfect dominating sets of fuzzy graphs, complete, connected, bipartite, cut node, tree, bridge and some other new notions of fuzzy graphs which are analyzed with a strong and weak nctpkD(G) set of fuzzy graphs. The order and size of the strong and weak nctpkD(G) fuzzy set are studied. Additionally, a few related theorems and statements are analyzed.

Residual stress occurs in many machined components and parts. Over time, several methods have been developed for the investigation of residual stress in the material – destructive and non-destructive. This article deals with the evaluation and comparison of residual stress in the material when machining steel C45 and 11CrMo9-10, when the tool enters into the cut and stands out in conditions of an interrupted cut. A non-destructive method, based on X-Ray diffraction, was applied to evaluate the residual stress. The points were measured on the last machined slat by using interrupted cut simulator. An irregular interrupted cut was achieved by gradually machining 4, 3, 2 and 1 slat. The experiments were realized in co-operation with the Faculty of Mechanical Engineering, VSB – Technical University of Ostrava, Czech Republic and the Faculty of Mechanical Engineering, the University of Žilina, Slovakia.

One of main issues when evaluating or analysing quality and functional properties of work-piece is identification of status of residual stress. Every treatment or machining process generates residual stress in the surface and subsurface layers of the material structure. The residual stress has a large influence on the functional properties of the components. The article is focused on the method of triaxial measurement of residual stress after machining the surface of sample by high feed milling technology. Significance of triaxial measuring is the capability of measuring in different angles so it is possible to acquire stress tensor containing normal and shear stress components acting in the spot of measuring, using a Cartesian coordinate system.

In this work, we deliver a novel measure of similarity between Gaussian mixture models (GMMs) by neighborhood preserving embedding (NPE) of the parameter space, that projects components of GMMs, which by our assumption lie close to lower dimensional manifold. By doing so, we obtain a transformation from the original high-dimensional parameter space, into a much lower-dimensional resulting parameter space. Therefore, resolving the distance between two GMMs is reduced to (taking the account of the corresponding weights) calculating the distance between sets of lower-dimensional Euclidean vectors. Much better trade-off between the recognition accuracy and the computational complexity is achieved in comparison to measures utilizing distances between Gaussian components evaluated in the original parameter space. The proposed measure is much more efficient in machine learning tasks that operate on large data sets, as in such tasks, the required number of overall Gaussian components is always large. Artificial, as well as real-world experiments are conducted, showing much better trade-off between recognition accuracy and computational complexity of the proposed measure, in comparison to all baseline measures of similarity between GMMs tested in this paper.

The article deals with evaluating of the resulting surface state of the three plastic materials and identification of suitable conditions for laser cutting with CO2 tube. As representative were chosen polypropylene, polymethylmethacrylate and polyamide. When cutting these types of materials it could melt eventually their re-sintering. A suitable combination of parameters is possible to achieve of sufficient quality of the cut. The samples were cut at different feed speed and laser power. Then they was compared on the basis of the measured roughness parameters Ra a Rz by using a portable touch roughness Hommel-Etamic W5 and dates was processed according to ČSN EN ISO 4287. Cutting of samples was realized at the Department of Machining, Assembly and Engineering Metrology, VŠB-TUO.

Computer-aided process planning represents the main link between computer-aided design and computer-aided manufacturing. One of the crucial tasks in computer-aided process planning is an operation sequencing problem. In order to find the optimal process plan, operation sequencing problem is formulated as an NP hard combinatorial problem. To solve this problem, a novel genetic crow search approach (GCSA) is proposed in this paper. The traditional CSA is improved by employing genetic strategies such as tournament selection, three-string crossover, shift and resource mutation. Moreover, adaptive crossover and mutation probability coefficients were introduced to improve local and global search abilities of the GCSA. Operation precedence graph is adopted to represent precedence relationships among features and vector representation is used to manipulate the data in the Matlab environment. A new nearest mechanism strategy is added to ensure that elements of machines, tools and tool approach direction (TAD) vectors are integer values. Repair strategy to handle precedence constraints is adopted after initialization and shift mutation steps. Minimization of total production cost is used as the optimization criterion to evaluate process plans. To verify the performance of the GCSA, two case studies with different dimensions are carried out and comparisons with traditional and some modern algorithms from the literature are discussed. The results show that the GCSA performs well for operation sequencing problem in computer-aided process planning.

Thin-walled parts made of aluminum alloy are mostly used as structural elements in the aerospace, automobile, and military industries due to good homogeneity, corrosion resistance, and the excellent ratio between mechanical properties and mass. Manufacturing of these parts is mainly performed by removing a large volume of material, so it is necessary to choose quality machining parameters that will achieve high productivity and satisfactory quality and accuracy of machining. Using the Taguchi methodology, an experimental plan is created and realized. Based on its results and comparative analysis of multi-criteria decision making (MCDM) methods, optimal levels of machining parameters in high-speed milling of thin-walled parts made of aluminum alloy Al7075 are selected. The varying input parameters are wall thickness, cutting parameters, and tool path strategies. The output parameters are productivity, surface quality, dimensional accuracy, the accuracy of forms and surface position, representing the optimization criteria. Selection of the optimal machining parameter levels and their ranking is realized using 14 MCDM methods. Afterward, the obtained results are compared using correlation analysis. At the output, integrative decisions were made on selecting the optimal level and rank of alternative levels of machining parameters.