Publikační činnost Katedry obrábění, montáže a strojírenské metrologie / Publications of Department of Working and Assembly (346)

Permanent URI for this collectionhttp://hdl.handle.net/10084/71298

Kolekce obsahuje bibliografické záznamy publikační činnosti (článků) akademických pracovníků Katedry obrábění, montáže a strojírenské metrologie (346) v časopisech registrovaných ve Web of Science od roku 2003 po současnost.
Do kolekce jsou zařazeny:
a) publikace, u nichž je v originálních dokumentech jako působiště autora (adresa) uvedena Vysoká škola báňská-Technická univerzita Ostrava (VŠB-TUO),
b) publikace, u nichž v originálních dokumentech není v adrese VŠB-TUO uvedena, ale autoři prokazatelně v době jejich zpracování a uveřejnění působili na VŠB-TUO.

Bibliografické záznamy byly původně vytvořeny v kolekci Publikační činnost akademických pracovníků VŠB-TUO, která sleduje publikování akademických pracovníků od roku 1990.

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Now showing 1 - 20 out of 288 results

Vibration analysis of piping connected with shipboard equipment
(Frontiers Media S.A., 2024) Tripathi, Radharaman; Jadhav, Tushar A.; Gaikwad, Mahesh K.; Naidu, Mithul J.; Gawand, Aishwarya B.; Kaya, Duran; Salunkhe, Sachin; Čep, Robert; Nasr, Emad Abouel
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.
Exploring tribological properties in the design and manufacturing of metal matrix composites: an investigation into the AL6061-SiC-fly ASH alloy fabricated via stir casting process
(Frontiers Media S.A., 2024) Murmu, Sagar Kumar; Chattopadhayaya, Somnath; Čep, Robert; Kumar, Ajay; Kumar, Ashwini; Mahato, Shambhu Kumar; Kumar, Amit; Sethy, Priya Ranjan; Logesh, K.
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.
Performance improvement of set of worm gears used in soot blower through profile modification
(Frontiers Media S.A., 2024) Honkalas, Rahul; Deshmukh, Bhagyesh; Pawar, Prabhakar; Salunkhe, Sachin; Čep, Robert; Nasr, Emad Abouel
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 consumptionofthesootblowerdependsonitsworkingefficiencyandultimately 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.
Multi-response optimization of electrochemical machining parameters for Inconel 718 via RSM and MOGA-ANN
(MDPI, 2024) Saha, Subhadeep; Mondal, Arpan Kumar; Čep, Robert; Joardar, Hillol; Haldar, Barun; Kumar, Ajay; Alsalah, Naser A.; Ataya, Sabbah
Inconel 718’s exceptional strength and corrosion resistance make it a versatile superalloy widely adopted in diverse industries, attesting to its reliability. Electrochemical machining (ECM) further enhances its suitability for intricate part fabrication, ensuring complex shapes, dimensional accuracy, stress-free results, and minimal thermal damage. Thus, this research endeavors to conduct a novel investigation into the electrochemical machining (ECM) of the superalloy Inconel 718. The study focuses on unraveling the intricate influence of key input process parameters—namely, electrolytic concentration, tool feed rate, and voltage—on critical response variables such as surface roughness (SR), material removal rate (MRR), and radial overcut (RO) in the machining process. The powerful tool, response surface methodology (RSM), is used for understanding and optimizing complex systems by developing mathematical models that describe the relationships between input and response variables. Under a 95% confidence level, analysis of variance (ANOVA) suggests that electrolyte concentration, voltage, and tool feed rate are the most important factors influencing the response characteristics. Moreover, the incorporation of ANN modeling and the MOGA-ANN optimization algorithm introduces a novel and comprehensive approach to determining the optimal machining parameters. It considers multiple objectives simultaneously, considering the trade-offs between them, and provides a set of solutions that achieve the desired balance between MRR, SR, and RO. Confirmation experiments are carried out, and the absolute percentage errors between experimental and optimized values are assessed. The detailed surface topography and elemental mapping were performed using a scanning electron microscope (SEM). The nano/micro particles of Inconel 718 metal powder, obtained from ECM sludge/cakes, along with the released hydrogen byproducts, offer promising opportunities for recycling and various applications. These materials can be effectively utilized in powder metallurgy products, leading to enhanced cost efficiency.
Dynamic stability of the periodic and aperiodic structures of the Bernoulli-Euler beams
(Polska Akademia Nauk, Instytut Metalurgii i Inżynierii Materiałowej, 2024) Garus, Justyna; Petrů, Jana; Sochacki, Wojciech; Garus, Sebastian
The study analyzed the influence of periodic and aperiodic stiffness distribution for the four-element Bernoulli-Euler beam on the first two eigenfrequencies and the dynamic stability of the system. The influence of increasing the ratio of cross-sections of the analyzed elements was also analyzed. significant differences were found in eigenfrequencies and dynamic stability. using the variational hamilton principle, the equation of motion was derived, on the basis of which the values of the eigenfrequencies were determined, and the transformation into the form of the Mathieu equation made it possible to determine the dynamic stability for the analyzed structures.
Multi-objective liver cancer algorithm: A novel algorithm for solving engineering design problems
(Elsevier, 2024) Kalita, Kanak; Ramesh, Janjhyam Venkata Naga; Čep, Robert; Pandya, Sundaram B.; Jangir, Pradeep; Abualigah, Laith
This research introduces the Multi-Objective Liver Cancer Algorithm (MOLCA), a novel approach inspired by the growth and proliferation patterns of liver tumors. MOLCA emulates the evolutionary tendencies of liver tumors, leveraging their expansion dynamics as a model for solving multi-objective optimization problems in engineering design. The algorithm uniquely combines genetic operators with the Random Opposition-Based Learning (ROBL) strategy, optimizing both local and global search capabilities. Further enhancement is achieved through the integration of elitist non-dominated sorting (NDS), information feedback mechanism (IFM) and Crowding Distance (CD) selection method, which collectively aim to efficiently identify the Pareto optimal front. The performance of MOLCA is rigorously assessed using a comprehensive set of standard multi-objective test benchmarks, including ZDT, DTLZ and various Constraint (CONSTR, TNK, SRN, BNH, OSY and KITA) and real-world engineering design problems like Brushless DC wheel motor, Safety isolating transformer, Helical spring, Two-bar truss and Welded beam. Its efficacy is benchmarked against prominent algorithms such as the non-dominated sorting grey wolf optimizer (NSGWO), multiobjective multi-verse optimization (MOMVO), non-dominated sorting genetic algorithm (NSGA-II), decomposition-based multiobjective evolutionary algorithm (MOEA/D) and multiobjective marine predator algorithm (MOMPA). Quantitative analysis is conducted using GD, IGD, SP, SD, HV and RT metrics to represent convergence and distribution, while qualitative aspects are presented through graphical representations of the Pareto fronts. The MOLCA source code is available at: https://github.com/kanak02/MOLCA.
Estimating the uncertainty of measurements for various methods and 3D printed parts
(MDPI, 2024) Kozior, Tomasz; Bochnia, Jerzy; Bochenek, Aleksandra; Malara, Dominik; Michał Nawotka; Jansa, Jan; Hajnyš, Jiří; Wójtowicz, Adam; Měsíček, Jakub
This paper presents the results of a study on the dimensional accuracy analysis of models produced by 3D printing technology—Fused Filament Fabrication (FFF). Geometric measurements were conducted using a dial caliper, a 3D scanner and a coordinate measuring machine. In addition, a statistical analysis of the test results was carried out, considering the division into different numbers of test samples (3, 5, 10, 20, 30). The analysis of the test results made it possible to assess the influence of the measuring tools used and the number of samples tested on the final measurement result, as well as to determine the consequences associated with it.
Mechanical, corrosion and tribocorrosion resistance of additively manufactured Maraging C300 steel
(Elsevier, 2024) Wieczorek, Daniel; Ulbrich, Dariusz; Stachowiak, Arkadiusz; Bartkowski, Dariusz; Bartkowska, Aneta; Petrů, Jana; Hajnyš, Jiří; Popielarski, Paweł
This article presents the effect of selected parameters of the additive manufacturing process on the structure of Maraging C300 steel and consequently on its properties: hardness and resistance to abrasive wear, corrosion and tribocorrosion in 3.5% NaCl. The samples were produced with laser beams of 300, 350 and 400 W. Materials with different average microhardnesses of 436, 374 and 315 HV0.05 have been manufactured, respectively. Comparative evaluation of wear resistance on a ball-on-plate model node bench was carried out. Tests were performed under tribocorrosion conditions and for only mechanical forcing. Corrosion resistance was estimated from polarization curves. The article summarizes tribocorrosion model for Maraging C300 steel, which takes into account the effect of grain size on the course of elementary wear processes, especially on the formation of friction and corrosion synergy. The results show that reducing the power of the laser beam in the manufacturing process of Maraging steel produces a material with a more fine-grained structure. Smaller grains provide higher hardness and greater resistance to abrasive wear. Unfortunately, at the same time, the corrosion resistance in 3.5% NaCl is worse. The different effect of grain size on abrasion intensity and corrosion wear means that under tribocorrosion conditions, increasing microhardness does not reduce volumetric wear. The least tribocorrosion wear is provided by the material produced by a 350 W laser beam, amounting to 1.34 x 10-3 mm3.
Machine learning for monitoring hobbing tool health in CNC hobbing machine
(Frontiers Media S.A., 2024) Tambake, Nagesh; Deshmukh, Bhagyesh; Pardeshi, Sujit; Mahmoud, Haitham A.; Čep, Robert; Salunkhe, Sachin; Nasr, Emad Abouel
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.
Tribological analysis of titanium alloy (Ti-6Al-4V) hybrid metal matrix composite through the use of Taguchi’s method and machine learning classifiers
(Frontiers Media S.A., 2024) Jatti, Vijaykumar S.; Sawant, Dhruv A.; Deshpande, Rashmi; Saluankhe, Sachin; Čep, Robert; Nasr, Emad Abouel
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.
Experimental investigation of tungsten–nickel–iron alloy, W95Ni3.5Fe1.5, compared to copper monolithic bullets
(Frontiers Media S.A., 2024) Abhishek, T.; Sundeep, Dola; Chandrasekhara Sastry, C.; Eswaramoorthy, K. V.; Kesireddy, Gagan Chaitanya; Reddy, Bobbili Veera Siva; Verma, Rakesh Kumar; Salunkhe, Sachin; Čep, Robert; Nasr, Emad Abouel
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.
Application of Instrumented Indentation Test and Neural Networks to determine the constitutive model of in-situ austenitic stainless steel components
(Springer Nature, 2024) Ma, Quoc-Phu; Basterrech, Sebastian; Halama, Radim; Omacht, Daniel; Měsíček, Jakub; Hajnyš, Jiří; Platoš, Jan; Petrů, Jana
Over the last few decades, Instrumented Indentation Test (IIT) has evolved into a versatile and convenient method for assessing the mechanical properties of metals. Unlike conventional hardness tests, IIT allows for incremental control of the indenter based on depth or force, enabling the measurement of not only hardness but also tensile properties, fracture toughness, and welding residual stress. Two crucial measures in IIT are the reaction force (F) exerted by the tested material on the indenter and the depth of the indenter (D). Evaluation of the mentioned properties from F-D curves typically involves complex analytical formulas that restricts the application of IIT to a limited group of materials. Moreover, for soft materials, such as austenitic stainless steel SS304L, with excessive pile-up/sink-in behaviors, conducting IIT becomes challenging due to improper evaluation of the imprint depth. In this work, we propose a systematic procedure for replacing complex analytical evaluations of IIT and expensive physical measurements. The proposed approach is based on the well-known potential of Neural Networks (NN) for data-driven modeling. We carried out physical IIT and tensile tests on samples prepared from SS304L. In addition, we generated multiple configurations of material properties and simulated the corresponding number of IITs using Finite Element Method (FEM). The information provided by the physical tests and simulated data from FEM are integrated into an NN, to produce a parametric mapping that can predict the parameters of a constitutive model based on any given F-D curve. Our physical and numerical experiments successfully demonstrate the potential of the proposed approach.
Experimental investigation and optimization of nano Al2O3 mixed FSWed joint between AA2024-T351 and AA7075-T651 by response surface approach
(Frontiers Media S.A., 2024) Gebreamlak, Getachew; Palani, Sivaprakasam; Sirahbizu, Belete; Čep, Robert
Additive mixed friction stir welding can be an innovative and novel method for enhancing the friction stir welding process. Thus, this research aimed to investigate nano Al2O3 effects on the mechanical and microstructure of FSWed joints using Al alloys AA2024-T351/AA7075-T651. The experiments were performed based on response surface approach based CCD twenty run with varying three factors: tool rotational speed (A: 800-1,200 rpm), welding speed (B: 20-60 mm/min), tool plunge depth (C: 0.2-0.4 mm) and fixed volume percentages of Al2O3 nano-particles (8%). Mechanical performances such as tensile, yield, and hardness tests have been performed and microstructural properties have been analyzed through SEM and microscopy. The statistical analysis shows that the tensile strength can be significantly affected by rotational speed (A), welding speed (B), tool plunge depth (C), interaction (AB, BC, AC), and quadratic term A(2), B-2 in the FSW process; yield strength was influenced considerably by main, interaction, and quadratic terms; main factors and quadratic terms A(2), B-2 and C-2 significantly influenced hardness values. The fracture test revealed that the joints with Al2O3-reinforced AA2024-T351/AA7075-T651 alloys were more ductile and less brittle. The optimal conditions for FSW, tool rotational at 1,146 rpm, weld speed at 60 mm/min, and 0.4 mm plunge depth were responsible for higher tensile strength of 169 MPa, yield strength of 145 MPa, and micro-hardness values of 89 HRB due to the uniform nano-particle dispersions and better material mixing.
Surface roughness prediction of AISI D2 tool steel during powder mixed EDM using supervised machine learning
(Springer Nature, 2024) Kaigude, Amreeta R.; Khedkar, Nitin K.; Jatti, Vijaykumar S.; Salunkhe, Sachin; Čep, Robert; Nasr, Emad Abouel
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 mu s, Toff = 12 mu s, 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.
Effect of the shape of flapping airfoils on aerodynamic forces
(Elsevier, 2024) Butt, Fahad; Talha, Tariq; Khan, Rehan; Mazhar, Abdur Rehman; Butt, Mahad; Petrů, Jana; Seikh, Asiful H.
The rapid exhaustion of fossil fuels and the ozone depletion caused by the excessive usage of the fossil fuels has prompted researchers to look towards bioinspired designs for both propulsion and energy extraction purposes. Limited amount of work has been done to present the effects of airfoil shape on the aerodynamic forces on flapping foils. In this paper, we examine in detail the effect of airfoil camber and its position on flapping foil performance in both energy extraction and propulsion regimes. We also examine the effect of reflex camber on flapping foil performance in both flow regimes. In total, 42 airfoils are analyzed using the NACA 4 and 5 -series cross -sections. The man objective of this research is to identify a trend, between airfoil shape and aerodynamic forces. The database created as a result will be used in the future work for designing a hydrokinetic turbine and a bio-inspired unmanned aerial vehicle. The results from the numerical simulations indicate that the airfoil shape has significant effects on the time averaged drag force on the airfoil in both flow regimes. However, the time averaged lift force remains negligible for all cases.
A study on optimizing the maximal product in cubic fuzzy graphs for multifaceted applications
(MDPI, 2024) Meenakshi, Annamalai; Mythreyi, Obel; Čep, Robert; Karthik, Krishnasamy
Graphs in the field of science and technology make considerable use of theoretical concepts. When dealing with numerous links and circumstances in which there are varying degrees of ambiguity or robustness in the connections between aspects, rather than purely binary interactions, cubic fuzzy graphs (CFGs) are more adaptable and compatible than fuzzy graphs. To better represent the complexity of interactions or linkages in the real world, an emerging CFG can be very helpful in achieving better problem-solving abilities that specialize in domains like network analysis, the social sciences, information retrieval, and decision support systems. This idea can be used for a variety of uncertainty-related issues and assist decision-makers in selecting the best course of action through the use of a CFG. Enhancing the maximized network of three cubic fuzzy graphs' decision-making efficiency was the ultimate objective of this study. We introduced the maximal product of three cubic fuzzy graphs to investigate how interval-valued fuzzy membership, fuzzy membership, and the miscellany of relations are all simultaneously supported through the aspect of degree and total degree of a vertex. Furthermore, the domination on the maximal product of three CFGs was illustrated to analyze the minimum domination number of the weighted CFG, and the proposed approach is illustrated with applications.
A comprehensive review of parametric optimization of electrical discharge machining processes using multi-criteria decision-making techniques
(Frontiers Media S.A., 2024) Pendokhare, Devendra; Kalita, Kanak; Chakraborty, Shankar; Čep, Robert
Optimization of electrical discharge machining (EDM) processes is a critical issue due to complex material removal mechanism, presence of multiple input parameters and responses (outputs) and interactions among them and varying interest of different stakeholders with respect to relative importance assigned to the considered responses. Multi-criteria decision making (MCDM) techniques have become potent tools in solving parametric optimization problems of the EDM processes. In this paper, more than 130 research articles from SCOPUS database published during 2013-22 are reviewed extracting information with respect to experimental design plans employed, materials machined, dielectrics used, process parameters and responses considered and MCDM tools applied along with their integration with other mathematical techniques. A detailed analysis of those reviewed articles reveals that the past researchers have mostly preferred Taguchi's L 9 orthogonal array as the experimental design plan; EDM oil as the dielectric fluid; medium and high carbon steels as the work materials; peak current and pulse-on time as the input parameters; material removal rate, tool wear rate and surface roughness as the responses; and grey relational analysis as the MCDM tool during conducting and optimizing EDM operations. This review paper would act as a data repository to the future researchers in understanding the stochastic behaviour of EDM processes and providing guidance in setting the tentative operating levels of varying input parameters along with achievable response values. The extracted dataset can be treated as an input to any of the machine learning algorithms for subsequent development of appropriate prediction models. This review also outlines potential future research avenues, emphasizing advancements in EDM technology and the integration of innovative multi-criteria decision-making tools.
Multi-objective optimization of machining variables for wire-EDM of LM6/fly ash composite materials using grey relational analysis
(De Gruyter, 2024) Rubi, Charles Sarala; Prakash, Jayavelu Udaya; Juliyana, Sunder Jebarose; Čep, Robert; Salunkhe, Sachin; Gawade, Sharad Ramdas; Nasr, Emad S. Abouel
With the enhancement in science and technology, necessity of complex shapes in manufacturing industries have become essential for more versatile applications. This leads to the demand for lightweight and durable materials for applications in aerospace, defense, automotive, as well as sports and thermal management. Wire electric discharge machining (WEDM) is an extensively utilized process that is used for the exact and indented shaped components of all materials that are electrically conductive. This technique is suitable in practically all industrial sectors owing to its widespread application. The present investigation explores WEDM for LM6/fly ash composites to optimize different process variables for attaining performance measures in terms of maximum material removal rate (MRR) and minimum surface roughness (SR). Taguchi's L27 OA design of experiments, grey relational analysis, and analysis of variance (ANOVA) were employed to optimize SR and MRR. It has been noted from ANOVA that reinforcement (R) percentage and pulse on time are the most influential aspects for Grey Relational Grade (GRG) with their contributions of 28.22 and 18.18%, respectively. It is found that the best process variables for achieving the highest MRR and lowest SR simultaneously during the machining of the composite are gap voltage of 30 V, pulse on time of 10 mu s, pulse off time of 2 mu s, wire feed of 8 m/min, and R of 9%. The predicted GRG is 0.84, and the experimental GRG value is 0.86. The validation experiments at the optimized setting show close agreement between predicted and experimental values. The morphological study by optical microscopy revealed a homogenous distribution of reinforcement in the matrix which enhances the composite's hardness and decreases the density.
Numerical study on the optimized thickness of layer configuration against the 7.62 APM2 projectile
(Frontiers Media S.A., 2024) Morghode, Divyanshu S.; Thakur, D. G.; Salunkhe, Sachin; Čepová, Lenka; Nasr, Emad Abouel
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 degrees, 70 degrees, and 60 degrees. In this analysis, the projectile deformed in a mushroom shape for 90 degrees and 80 degrees target orientations, while for 70 degrees and 60 degrees 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 degrees target orientation.
Machinability studies of Al–4Mg/in-situ MgAl2O4 nano composites: measurement of cutting forces and machined surface roughness
(IOP Publishing, 2024) Kumar, T. Satish; Thankachan, Titus; Čep, Robert; Kalita, Kanak
The present research aims to study the dry turning machinability characteristics of in situ Al-4Mg/MgAl2O4 nanocomposite by High-Speed Steel tool. The influence of various machining process parameters, such as feed rate, depth of cut and cutting speed on the surface roughness and cutting force of the nanocomposites was measured while performing dry turning. From the turning operation results, it is noticed that up to 100 m min(-1), the cutting force increased and with further increases in cutting speed, the cutting force starts decreasing up to 150 m min(-1). The type of chips and built-up edge (BUE) development were studied using a scanning electron microscope. BUE formations were higher at low cutting speeds (50 m min(-1)) and lower at high cutting speeds (150 m min(-1)). At a given depth of cut and feed rate, with an increase in cutting speed, the length of the chip and chip curls increased. Further, higher 2 wt% of in situ MgAl2O4 addition changes long-curled chips to segmental-type chips. With a feed rate of 0.14 mm/rev, the Al-4Mg/1 wt% MgAl2O4 nanocomposite showed the lowest surface roughness value of 2.4 mu m proving usage of high speed steel can provide a better surface finish while turning Al-4Mg/MgAl2O4 nanocomposite.

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