2026-03-23
Chun-Hui He, Ji-Huan He
In addressing the macro-micro nonlinear challenges encountered in mechanical engineering, this article employs a multifaceted approach by integrating non-self-similar fractal theory, fractal-based fractional calculus and fractal-fractional AI. It thereby proposes novel concepts, including scale-dependent two-scale fractal derivatives and fractal-embedded Caputo calculus, which have not been previously documented. The validation of the framework is achieved through the use of a fractal MEMS photoacoustic transducer case, which derives fractal-modified stiffness and pull-in voltage models to balance device stability and efficiency. The work elucidates the three-step integration logic of the three tools, analyses their engineering applications, and outlines future directions in multi-scale modelling, lightweight AI, digital twin integration and standardization. This provides new theoretical and technical support for intelligent mechanical engineering innovation.
2026-02-08
Yufeng Li, Yang Li, Jing Nie, Qi Zhang, Yajie Liu, Jingbin Li
Spraying for plant protection in jujube orchards is typically performed manually, which entails high labor intensity and low operational efficiency. Autonomous navigation of spraying robots in jujube orchards can effectively increase operational efficiency and reduce workload. Navigation solutions based on global navigation satellite systems are prone to signal degradation in jujube orchards because of canopy occlusion. This paper proposes an autonomous navigation scheme for orchard-spraying robots that employs a multi-source information-fusion three-dimensional light detection and ranging (3D LiDAR) simultaneous localization and mapping technique. The autonomous navigation system senses the jujube-orchard environment using a 3D LiDAR and builds an environmental map via a factor graph–based LIO-SAM algorithm. It performs local pose estimation by fusing LiDAR and inertial-measurement-unit measurements, implements global localization on the map via an improved adaptive Monte Carlo localization algorithm, plans spraying-task routes using the A* algorithm, and employs the dynamic window approach for dynamic obstacle navigation during operation. The system meets the practical cruising requirements of plant-protection spraying operations and demonstrates high navigation and localization accuracy, providing an effective reference for autonomous spraying navigation in densely planted jujube orchards.
2025-12-19
Xin Li, Shiliang Guo, Dejie Sun, Lijun Cao, Cong Li, Shuyao Tian, Peng Liu, Yadong Qi
Rolling bearing is one of the most commonly used components in rotating machinery, and researching fault diagnosis techniques for it has important practical significance. In this paper, a fault diagnosis method based on extreme learning machine optimized by improved whale optimization algorithm (IWOA-ELM) is proposed for rolling bearing vibration signals. Firstly, Variational Mode Decomposition (VMD) is used to decompose the vibration signal of the bearing, and the energy entropy is calculated to form the eigenvector. Secondly, based on the original whale optimization algorithm, a hybrid initialization population strategy is adopted to generate an initial population with a certain quality. Selecting convergence factors based on reinforcement learning to improve global search capability, and using adaptive weights and random jumps to update individual positions. In this process, the t-distribution-levy flight variation strategy is introduced to avoid being attracted by local extremum. Then, the improved whale optimization algorithm is used to optimize the input weights and hidden layer thresholds of the Extreme Learning Machine (ELM). Finally, the feature set is input into an improved ELM model for training and testing. Experiments on fault diagnosis of rolling bearings of different types and degrees have shown that the model proposed in this paper can effectively improve the accuracy of fault classification.
2025-12-19
Ilkwang Jang, Yong Hoon Jang
The contact characteristics of rough surfaces play a crucial role in determining friction, wear, thermal resistance, and electrical conductivity. This study proposes a deep learning approach to efficiently predict the contact distribution of rough surfaces based on surface image information alone, and evaluates its effectiveness against numerical methods. A U-Net architecture was employed for predicting contact areas under varying scales and load conditions, using a dataset of 100,000 fractal surfaces generated via the random midpoint displacement (RMD) method. The results indicate that the deep learning model achieved performance comparable to conventional numerical methods in predicting both contact areas and electrical contact resistance, with minimal error observed in electrical contact resistance prediction. The model approached the contact prediction as an image segmentation task, enabling faster and more efficient computations than traditional numerical approaches. High performance across metrics such as Dice coefficient, Jaccard index, Bradford Factor (BF) score, and pixel accuracy highlighted its ability to maintain prediction accuracy while significantly enhancing computational efficiency. Additionally, by leveraging two-dimensional (2D) fast Fourier transform (FFT) techniques, the model effectively captured both low- and high-frequency characteristics, accurately predicting large-scale and fine-scale features of contact areas, while reducing computation time by more than 95% compared to numerical models. These findings demonstrate that the deep learning algorithms can effectively address multiscale contact problems, offering reliable data for various engineering design applications, including friction, wear, and thermal/electrical resistance, as well as enabling real-time analysis and large-scale simulations.
2025-12-19
Jaganathan Gokulachandran, Mohanavelu Thenarasu, Bhadrinath Pothkanoori, Madhavarao Seshadri Narassima, Erfan Babaee Tirkolaee
Reliability prediction is an upcoming method used in most industries today to correctly estimate and predict each component’s life in a day-to-day application. This field has proven extremely helpful in evolving various methods such as preventive maintenance and non-destructive testing for various machinery and its parts. In this study, mild steel workpieces are welded together according to three parameters: weld current, weld speed, and weld angle. These parameters are varied based on the Taguchi L27 orthogonal array design of experiments (DOE) to conduct the experiments. The workpieces are then subjected to tensile testing to determine the tensile strength values as well as the failure time. The main objective of this research is to develop a comprehensive, methodical framework to assess the reliability and failure time of welded joints of mild steel material. According to the experimental values, artificial neural network (ANN) and fuzzy logic (FL) models are developed to predict reliability percentage error and failure time. Based on the findings and in the case of FL implementation, the percentage deviation between the experimental and predicted values is vast, while it is calculated small with the use of ANN as a more accurate approach. A sample is also found to have an experimental reliability of 89.5%, the highest among the L27 DOE array wherein the optimum weld strength can be achieved by incorporating 100 A weld current, 55º weld angle, and 1.17mm/s of weld speed, respectively.
2025-12-19
Minjia Chen, Xingqi Luo, Wanbo Chen, Jianjun Feng, Xiaohang Wang
Urban water supply and drainage, the shipbuilding industry, the petrochemical industry and other professional fields rely heavily on mixed-flow pumps. Enhancing the efficiency of mixed-flow pumps is crucial for achieving the ‘dual carbon’ goals and promote energy saving and emission reduction. In this study, the guide vane and impeller of the mixed-flow pump were optimised, focusing on its low head and effectiveness in power plants. The performance of the original and optimised pumps was then evaluated under various flow rate conditions, and their hydraulic performance was compared. Results showed that the efficiency of the optimised mixed-flow pump was improved, resulting in effective enhancement of energy loss in the pump passage. The optimised guide vane facilitated smoother water flow into the outlet pipeline, achieving energy savings, emission reduction and contributing to the realisation of the ‘dual carbon’ goals.
2025-12-19
Stefano Valvano
In this paper, the mechanical analysis of an advanced Body Centred Cubic (BCC) lattice cell has been performed through a homogenisation procedure to obtain an equivalent set of mechanical properties. The mechanical analyses have been carried out with the use of ANSYS software and an original ANSYS Parametric Design Language (APDL) subroutine has been developed for the introduction of the double periodic boundary conditions. The Finite Element Method (FEM) is used for the mechanical model, and 3D elements with reduced integration has been employed to guarantee an accurate description of the lattice geometry. Different BCC cell configurations have been considered: standard metal BCC cell, metal BCC cell with waved struts, standard metal composite BCC cell. Depending on the configuration, the homogenised materials showed isotropic or orthotropic properties. For the evaluation of all the engineering constants, uniaxial traction test and in-plane shear test have been simulated along different loading directions. A parametric study has been conducted varying the struts diameter, the struts waviness and the thickness ratio of the composite struts. Finally, the homogenised materials have been tested through the mechanical analysis of sandwich panels with lattice core; a comparison between sandwich panels with homogenised core and sandwich panels with exact lattice cells has been carried out. The parametric study can be useful for the tailoring and optimisation analysis of an advanced component.
2025-10-25
Ali Mahmoodirad, Dragan Pamucar, Sadegh Niroomand
In this study an assembly line configuration is obtained for the nuzzle production line in petroleum industries. This is an important product which is widely used petroleum industries. Therefore, applying an optimization scheme for obtaining an optimal configuration is necessary. For this aim, a cost-based mathematical formulation is proposed to obtain the optimal assembly line configuration. In this model, overall station establishment cost, fixed salary, and variable wages are optimized simultaneously. In order to be close to real-world situations, the problem is formulated in a triangular fuzzy environment, where the cost- and time-based parameters are represented by fuzzy values. The proposed fuzzy formulation is converted to a crisp form using a ME measure of fuzzy sets and numbers. Then, in order to evaluate the proposed crisp formulation, a case study from the petroleum industries of Iran is considered. Based on the performed experiments and obtained results, the best configuration of the assembly line is obtained, and a sensitivity analysis is performed as well.
2025-10-10
Darko Božanić, Adis Puška, Duško Tešić, Anđelka Štilić, Kifayat Ullah, Yousif Raad Muhsen, Ibrahim M. Hezam
The paper presents a multi-criteria decision-making (MCDM) model designed to rank combined construction machines - specifically, Backhoe Loaders - during procurement for military needs. However, the model can also be applied to construction companies. The ranking is based on criteria specifically defined for this research. The study found that most criteria relate to the structural elements of the Backhoe Loader, which is also significant for manufacturers working on improving these types of machines. The MCDM model is built on two methods: Analytic Hierarchy Process (AHP) and Multi-Attributive Border Approximation area Comparison (MABAC), both adapted using fuzzy numbers. The AHP method was modified with type 2 fuzzy numbers to calculate criteria's weight coefficients. The MABAC method, using classic triangular fuzzy numbers, is employed for ranking alternative solutions. Validation of the results involved two steps. First, a sensitivity analysis was performed by modifying the weight coefficients of the criteria. Second, a comparative analysis with other methods was performed. The validation process confirmed the stability of the obtained results.
2025-10-10
Arunodaya Raj Mishra, Pratibha Rani, Dragan Pamucar, Ahmad M. Alshamrani, Adel Fahad Alrasheedi
This study aims to evaluate and prioritize the key interested regions of Circular Economy (CE) in terms of implementing the industry 4.0 technologies for the performance of logistics activities in the agri-food sector. For this purpose, we introduce a hybrid ranking framework based on Relative Closeness Coefficient (RCC)-based objective weighting model, the RANking COMparison (RANCOM) subjective weighting procedure and the Mixed Aggregation by Comprehensive Normalization Technique (MACONT) with Intuitionistic Fuzzy Information (IFI). In this framework, new IF-score function and an improved distance measure are proposed in the context of IFI to evade the limitations of existing ones. A hybrid IF-RCC-RANCOM-MACONT framework is introduced to prioritize the options over defined criteria. To prove the applicability of introduced approach, it is employed on a case study of circular economy interested regions assessment in the agri-food sector, consisting of five alternatives and nine criteria under the dimensions of sustainability. Sensitivity analysis is shown to highlight the impact of used parameters on the final outcomes. At last, a comparison with extant approaches is made to demonstrate the robustness of obtained results.
2025-10-10
Chiranjib Bhowmik, Divya Zindani, Prasenjit Chatterjee, Dragan Marinkovic, Jūratė Šliogerienė
To address the problem of green energy source selection, this paper proposes a novel decision-making framework using fuzzy-TOmada de Decisao Interativa Multicriterio (TODIM) method in an interval-valued intuitionistic environment. The proposed framework integrates the prospect theory approach with Schweizer-Sklar and power averaging operators to evaluate the green energy sources including solid waste, solar, tidal, carbon capture storage, hydrogen, marine, hydel, biogas, wind, concentrating solar, geothermal and biomass under the influence of nine conflicting criteria such as annual generation, capacity factor, mitigation potential, useful life, installation period, energy requirement, CO 2 emission, generating cost and operations and maintenance cost. The vagueness associated with the evaluations as well as biased evaluations is taken care of by Schweizer-Sklar and power averaging operators while TODIM method provides due consideration to the psychological behavior of the decision maker. Solar photovoltaic emerges as the best energy source. Sensitivity analysis has also been performed to assess the robustness of the proposed decision-making framework.
2025-10-10
Alize Yaprak Gül, Emre Cakmak, Atiye Ece Karakas
Forest fires are one of the major causes for deforestation resulting in significant economic and environmental losses. The application of drones has been extended to various areas including disaster management. Since drones offer numerous advantages like real-time surveillance, task planning capabilities and autonomy, they are utilized in early detection systems for forest fires. The selection of a drone type for this purpose involves a complex system of multiple factors and conflicting information, for which the use of multi-criteria decision-making (MCDM) methods have been found to be yielding effective results. The aim of this study is to present a decision framework for drone selection problem in the context of forest fire surveillance and detection. This study contributes by (i) pointing out to the gap that the drone selection problem for forest surveillance and fire detection has been sparsely addressed, (ii) presenting an extensive literature review, (iii) extracting the relevant criteria through a literature review and interviews with the experts in field, (iv) assessing the alternatives by the proposed framework based on interval valued neutrosophic evaluation based on distance from average solution (IVN EDAS) method. The proposed framework is demonstrated by a case study consisting of four drone alternatives and 14 criteria. In accordance with the extant literature, the criteria related to the visual capabilities and diagnosis are evaluated as the most crucial features. A sensitivity analysis is carried out to check for the robustness by varying the criteria weights and a comparative analysis is conducted with interval valued neutrosophic technique for preference by similarity to the ideal solution (IVN TOPSIS) and interval valued neutrosophic combinative distance-based assessment (IVN CODAS) methods to validate the veracity of the method.
2025-08-05
Mohamed Boujelbene, Seyed A.M. Mehryan, Mikhail Sheremet, Mohammad Shahabadi, Nasrin B.M. Elbashir, Mohammad Ghalambaz
Phase change materials (PCMs) are widely used for thermal energy storage systems due to their effective thermal properties for energy accumulation. Simultaneously, this material has poor thermal conductivity, and for the optimization of such systems, many techniques are used. This study focuses on an analysis of PCMs in a vertical cavity with one, two, or three solid fins and differential heating. The finite element procedure has solved governing equations formulated using the power-law approach for the non-Newtonian PCM and enthalpy-porosity method. The developed code has been verified using numerical and experimental data from other authors. Effects of fins number and power-law index on flow and thermal structures within the cavity have been studied. It has been found that a rise in the power-law index illustrates a growth of time for the charging level of the storage system, while the addition of a solid fin from one to three allows for reducing the charging time. The extended heat transfer surface can be applied to optimize the thermal energy storage system.
2025-08-05
Haining Gao, Hongdan Shen, Caixu Yue, Rongyi Li, Steven Y. Liang, Yinlin Wang, Wenfu Liu, Yong Yang
Machining chatter is a self-excited vibration between the cutting tool and the workpiece, which can reduce surface quality and tool life, and even endanger the safety of operators in severe cases. Considering that milling chatter has multi-scale features and the debugging of neural network hyperparameters heavily relies on experience, a milling chatter monitoring method based on an optimized hybrid neural network with an attention mechanism (MISSA-MSCNN-BiLSTM-ATM) is proposed. Firstly, the harmonic of the spindle rotation frequency is filtered out using the spindle rotation frequency removal technique (SFT). Then, an improved sparrow search algorithm (MISSA) is proposed based on multiple strategies including improved circle chaotic mapping, golden sine strategy, and enhanced Lévy flight. Subsequently, MISSA is utilized to optimize the hyperparameters of the milling chatter classification hybrid neural network model, combining multi-scale convolutional neural networks (MSCNN), bidirectional long short-term memory (BiLSTM), and attention mechanism (ATM). In numerical simulations with CEC2005 complex functions, MISSA demonstrates better optimization accuracy, stability, and shorter computation time compared to other intelligent algorithms. Compared with other milling chatter classification models, the proposed method exhibits significant improvements in accuracy and stability.
2025-08-05
Sara Bošković, Stefan Jovčić, Vladimir Simic, Libor Švadlenka, Momčilo Dobrodolac, Nebojša Bacanin
Decision-making is a challenging task for logistics managers when solving the supplier selection problem. It is usually affected by numerous conflicting criteria that are not equally important to all decision-makers. Criteria evaluation is one of the crucial parts here. The primary purpose of this paper is to propose a novel integrated criteria importance assessment method based on objective judgment and group decision-making. The developed method is applied to the criteria importance evaluation for supplier selection. First, we proposed the criteria importance assessment (CIMAS) method based on the expert’s opinion, where the years of experts’ experience were given in the form of an expert’s weight. Second, the obtained criteria weights are further integrated within the well-known CRITIC method, and the hybrid criteria weights are determined. The input data matrix is based on the experts’ criteria evaluation on the one-to-ten-point scale. The data were further analyzed by the novel CIMAS method and were utilized within the CRITIC (objective) method. The paper's main contribution is the proposal of the novel CIMAS method. Another contribution is coupling the subjective (CIMAS) and the objective (CRITIC) methods. The results reveal that the most important criterion for supplier selection is on-time distribution, followed by distribution cost, external image and appearance in public, social responsibility rate, and air pollution, respectively. The sensitivity and comparative analysis were also performed, and the technique confirmed a high level of stability.
2025-08-05
Cheng Zhang, Songxiao Li, Zhuo Zhang
The dynamic simulation modeling problem of industrial robot arm is solved, and the trajectory planning dynamic simulation is performed in this paper. In response to the lack of trajectory planning and motion controller interfaces in the robotic modelling study, including the lack of dynamic simulation visualization, a Simscape Multibody-based method for building a dynamic model of industrial robot arm is proposed and the effectiveness of the model is verified through dynamic simulation. The simulation model integrates the robotic arm trajectory planning, motion controller and data acquisition module. It has a clear structure and the parameters are easy to modify. It can reasonably simulate the structure and parameters of the research object and facilitate the subsequent research of related algorithms. It provides an innovative and open-source research and development platform for the dynamic simulation study of the robot arm.
2025-08-05
Manik Barman, Tapan Kumar Barman, Prasanta Sahoo
Previously electroless Ni-B (ENB) coatings were analyzed and optimized based on various coating parameters. However, variation of nano-indentation behaviour like nano-hardness, elastic modulus and scratch hardness variation with bath composition and heat treatment temperature has not been reported earlier. An attempt has been made to explore the same in the present study. ENB coating layers are deposited on AISI 1040 steel specimen with varying concentration of sodium borohydride (NaBH 4 ) and heat-treated at 350°C, 450°C and 550°C to investigate the related effects. Nano-hardness and elastic modulus of as-coated specimens are found to improve with NaBH 4 concentration due to increased boron content and nodule size. Both nano-hardness and elastic modulus are observed to improve further upon heat treatment because of incorporation of various boride phases leading to compact morphology and increased size of the nodules. Scratch hardness value also increases with NaBH 4 concentration and it improves further upon heat treatment and reaches to its maximum at 450°C due to presence of compact and hard Ni 2 B phase. Compact homogeneous surface morphology enhances the friction and wear behaviour of the heat-treated coatings even though surface roughness deteriorates after heat treatment.
2025-08-05
Raul-Cristian Roman, Radu-Emil Precup, Emil M. Petriu
This paper presents a comparative analysis of two data-driven algorithm combinations: the first-order Active Disturbance Rejection Control-Fictitious Reference Iterative Tuning (ADRC-FRIT) and the first-order Model-Free Control-Fictitious Reference Iterative Tuning (MFC-FRIT). The objective of both data-driven combinations is to ascertain the tunable parameters through the resolution of an optimization problem and to streamline the heuristic procedures involved. The data-driven algorithms are empirically validated through experimental trials utilizing the 3D laboratory equipment in which the x-, y-, and z-axes are controlled.
2025-08-05
Sayyid H. Hashemi Kachapi, Sayyideh Gh. Hashemi Kachapi
In current work, semi nonclassical controller effects such as strain gradient (SGT), nonlocal (NLT) and Gurtin–Murdoch surface/interface (GMSIT) theories are presented for analyzing of nonlinear vibration in piezoelectric nanoresonator (PENR) compared to classical theory (CT). PENR subjected to nonlinear electrostatic excitation with direct (DC) and alternating (AC) voltages and also visco-pasternak medium. For this analysis, Hamilton’s principle, Galerkin technique, combination of Complex averaging method and arc-length continuation are used to analyze nonlinear frequency response and stability analysis of PENR. The results show that ignoring small-scale and surface/interface effects give inaccurate predictions of vibrational response of the PENR. It is indicated that in different boundary condition, material length scale and nonlocal scale parameters respectively lead to decreasing and increasing of PENR stiffness and also the amplitude of oscillation and the range of instability of non-classic theories of NLT and SGT are greater than that of the classical one. Also changes of surface/interface parameters lead to decreasing or increasing the dimensionless natural frequency, resonant frequency, resonance amplitude, nonlinear behavior and the system's instability of PENR.
2025-06-29
Ruslan Balokhonov, Aleksandr Zemlianov, Artur Shugurov, Diana Gatiyatullina, Ivan Ivashov, Vasilii Balokhonov, Varvara Romanova
A Top-Down approach is proposed for the numerical-experimental determination of local material properties of an additively manufactured AlSi12 alloy possessing clearly expressed hierarchical structure. A thin-walled product was fabricated by wire electron beam additive technology. The alloy multiscale structure is studied experimentally by optical, scanning and transmission electron microscopy. The compression and nanoindentation mechanical tests are carried out. Based on the experimental data, the finite element models of a layered structure at the macrolevel, dendritic and composite cellular structures at the mesolevel, and a composite structure comprising an aluminum matrix and silicon particles at the microlevel are created. The proposed Top-Down analysis assumes sequential macro-meso-micro structure-based numerical simulations to derive the mechanical properties of aluminum in dendrites at the microlevel and aluminum in the eutectic at the submicron level. The stress concentration and the plastic strain localization in dendritic, cellular and composite structures are analyzed. It was found at the mesoscale that the eutectic material experiences more shear stresses than the aluminum dendrites, with the highest stresses being observed in between the closely located dendrites. The volumetrically tensile and pure shear regions, as well as the regions of low elastic strains, are found after 30% compression.
2025-05-15
Hua Cao, Yongshen Fan, Chunya Ma, Peng Li, Wei Zhan, Yinbo Cao, Fuyi Duan
Current agricultural spraying faces issues such as excessive application, pesticide waste, and environmental pollution. This paper analyzes the hydraulic performance of several atomizing micro-sprayers (hollow cone, solid cone, and fan-shaped) used in large-scale irrigation machines, providing a theoretical basis for selecting spraying nozzles. Three types of micro-sprayers were tested at pressures of 0.2MPa, 0.3MPa, and 0.5MPa, and ground heights of 0.5m, 0.8m, 1.2m, and 1.5m. Each test was repeated three times. The results show that: (1) The hollow cone sprayer has a bimodal water distribution, the solid cone is unimodal, and the fan-shaped sprayer is long-strip shaped. As pressure increases, water distribution increases, while height increases reduce water distribution. (2) The droplet size distribution follows a normal distribution. Higher pressure increases the number of larger droplets, while lower pressure increases smaller droplets. Larger aperture sprayers generate more droplets, with the fan-shaped sprayer producing the most. (3) The particle size of the hollow cone sprayer ranges from 0.312mm to 1.187mm, with speeds below 1.4m/s; the solid cone sprayer ranges from 0.312mm to 6.5mm, with speeds below 2.4m/s; and the fan-shaped sprayer ranges from 0.312mm to 2.75mm, with speeds below 2.5m/s. The experimental results provide a theoretical basis for selecting and using atomizing micro-sprayers in large-scale irrigation, offering guidance for reducing pesticide use and improving agricultural efficiency.
2025-04-11
Ladislav Vrsalović, Nikša Čatipović, Senka Gudić, Stjepan Kožuh
The effect of copper content (0.031 wt.% Cu, 0.32 wt.% Cu, 0.51 wt.% Cu and 0.91 wt.% Cu) on the hardness and corrosion properties of ADI was investigated. Samples austenitization were carried out at 850°C for 60 min followed by its austempering at temperatures from 250°C to 420°C for different time (30 to 60 min) in 50% (KNO 3 + NaNO 3 ) salt bath. It was concluded that hardness rises with copper content but decreases with higher austempering temperatures and times. The corrosion properties of the samples with minimum and maximum Cu content were investigated by electrochemical methods in 0.5 M NaCl solution. Samples with a higher copper content have shown higher values of polarization resistance (R p ) and lower values of corrosion current (i corr ). After polarization measurements, corroded surfaces were analyzed with SEM/EDS analysis.
2025-04-11
Xingtong Zhu, Minchuan Huang, Ke Chen
According to the generalized Jaccard coefficient and false degree, an improved approach is proposed by incorporating Dempster-Shafer proofs for determining the level of confidence in the evidence. It also determines the weight of proof in terms of trust and falsity. Then, the base probability of the original evidence is weighted and averaged, followed by the adoption of the combined Dempster's compositional rule. It is evident that the above combination can be applied in condition monitoring of bearings up to rupture. Firstly, the supporting vibration signal is decomposed by applying the empirical mode decomposition, empirical wavelet transformation and variational mode decomposition approaches. All the vectors of the fault characteristic are extracted by combining the sample entropy. Then, the fault probability is obtained by performing preliminary diagnosis using the relevance vector machine, where the obtained preliminary diagnostic result is considered as the primary probability of the Dempster-Shafer evidence theory. Finally, it is revealed that an accurate diagnosis could be achieved by performing fusion using the enhanced evidence combination method. Specifically, the accuracies of the initial condition monitoring based on the EMD, EWT and VMD sample entropies and RVM were found to be 97.5%, 98.75% and 95%, respectively. The closeness and high values of these accuracies show that the selected methods are valid. The obtained condition monitoring results show that the relevance vector machine combined with the Dempster-Shafer evidence could enhance the efficiency. This theory has the least error and better reliability in supporting failure diagnosis.
2025-04-11
Ali W. Aldeen, Dina Y. Mahdi, Chen Zhongwei, Imad A. Disher, Barhm Mohamad
In this study, the effect of isothermal and isochronal aging is reported to investigate the precipitate evolution and recrystallization of N36 zirconium alloy after β-quenching. Two groups of samples were cut from the as-received sheet of N36 zirconium alloy and subjected to solution treatment and subsequent aging at 580, 640, and 700 °C for 40 and 600 min, respectively. Optical microscopy (OM), scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy dispersive spectroscopy (EDS), and electron backscattering diffraction (EBSD) were utilized to characterize the microstructure and second-phase particle (SPPs) evolution. Results show that the implemented quenching after solution treatment produces fine interlaced α-plates structure conserved inside prior β grain boundaries with 12 variant directions that follow Burger misorientation characteristics. After aging for a short time, initial α-plates conserve their shape and become softer, and SPPs spread along their boundaries. Recrystallizations are finished for specimens aged at a higher temperature or for a longer time. The recrystallized structure exhibits non-uniform grains and a random SPPs distribution. Despite the differences in morphology, some recrystallization grains retain the orientation feature from the initial α-plates. Hardness declines as temperature and time rise, and no hardness peak is seen. Roughness and wettability rise with increasing ageing temperatures.
2025-04-11
Tomasz Szwarc, Włodzimierz Wróblewski, Tomasz Borzęcki
This paper presents a CFD analysis of an air-oil separator of an aircraft gas turbine engine with a focus on the impact of the oil tank filling level on the separator performance for a selected point of the flying mission. The separator efficiency and the oil quality affect the efficiency of the oil system. New design criteria and standards require a better understanding of the phenomena occurring in the separator. To optimize its structure, the flow of the air-oil mixture must be modeled in the design process. Although many papers are addressing the issue of gas-liquid separation, very little knowledge is available on the flow ratio typical of aircraft turbine engines. The separation phenomena were investigated using the volume-of-fluid method. Transient calculations were performed at a selected mission point of the separator and compared with experimental data. A mesh independence study using a structural mesh is included to understand the mesh impact on the analysis results. The current analysis results will support further studies focusing on an optimization analysis where a proper mesh, an adequate turbulence model and an appropriate oil level have to be selected.
2025-04-11
Xiaohua Jin, Jiyu Zheng, Shunheng Hua, Xinru Tong
Coal exploration requires draining the gas from the coal seam. For this purpose, drill holes in the coal seam are required. Under the field conditions, the stress and strain states around a drill hole are not easy to determine and the same is valid for the material law defining their dependence. In this study, by arranging boreholes in coal samples, the effects of loading rate, borehole inclination, borehole diameter and other factors that influence the mechanical properties of porous coal are studied. The results show that the strength of porous coal increases with the increase of loading rate, which is consistent with the behavior of non-porous coal. The porous coal samples with certain inclination angles of the drill hole are easier to destroy compared to the porous coal samples with a horizontal drill hole. Furthermore, the failure surface is consistent with the inclination angle, and a specimen with an upward inclination angle of the drill hole is easier to destroy compared to a specimen with a downward inclination angle. Comparing the samples with the borehole diameters of 5 mm and 10 mm, the axial deformation and transverse deformation of the latter are 1.7 times and 1.63 times that of the former, respectively. Regarding the failure mode, it is shown that the borehole plays a leading role in the process of fracture propagation.
2025-04-11
Meijing Song, Željko Stević, Ibrahim Badi, Dragan Marinković, Yifei Lv, Kaiyang Zhong
Autonomous vehicles (AVs) have become a tangible presence on roads, indicating the emergence of a promising transportation technology for the future, possibly arriving sooner than anticipated. Nevertheless, the extensive integration of this technology is contingent on various factors, with the foremost being the level of public acceptance and adjustment to this advanced technology. Several factors, including safety, privacy, and cost, play crucial roles in fostering acceptance. Consequently, this research delves into the key determinants shaping individuals' willingness to embrace AVs. In this paper, a novel model, which consists of two methods: PIPRECIA and AROMAN with Interval Rough Numbers (IRNs) has been developed. The IRN PIPRECIA serves to define criterion weights, while the most significant contribution of the paper is the extension of the AROMAN method with IRNs for evaluating the public acceptance of autonomous vehicles and adapting all the necessary conditions for their use. The results show that a rapid implementation with extensive testing strategy represents the best solution.
2025-04-11
Gurpreet Singh, Vivek Gupta, Arnab Chanda
The expansion of the skin grafts plays a key role in treating severe burn injuries. Split-thickness skin grafting, which is a well-known technique for stretching donor skin samples beyond its capacity, typically produces expansions which are insufficient to cover large burn areas. In this work, the expansion potential of skin grafts with novel rotating triangle (RT) shaped auxetic incision patterns were investigated extensively. A skin simulant was employed and a range of RT configurations, with internal angles varying from 0° to 135°, were tested through the development of skin graft simulants. Mechanical testing and digital image correlation (DIC) were used to characterize the Poisson’s effect, meshing ratios, and induced stresses of the skin graft simulants, up to 50% strains. The 0° model produced the highest negative Poisson’s effect and areal expansions. As the internal angle of the auxetic was increased, expansions were observed to decrease significantly. Beyond 60°, positive Poisson’s effect and contractions occurred with an increasing trend, and its peak at 105°. At 15° and 120°, the induced strains were observed to be significant, posing risks of skin rupture. Overall, the expansions were observed to be higher at lower strains. Such experimental findings on expansion potentials and estimations of mechanical properties with auxetic skin grafts simulants have not been reported to date, and would be indispensable for further research in skin graft expansion and severe burn injury treatment.
2025-03-25
Jelena Stanojković, Miloš Madić, Milan Trifunović, Predrag Janković, Dušan Petković
Cutting forces are a critical indicator of the machining process, and their modeling is important for a variety of reasons, including tool life assessment, chatter prediction, tool condition monitoring, assessment of machining strategies and machining process optimization and control. This paper presents a new principle of modeling the main cutting force using dimensional analysis (DA). Dry longitudinal single-pass turning of two different steels (20MnCrS5 and S235JRG2) with two different cutting inserts, was considered. Taguchi's 3 3 ×2 1 design with 6 trials was applied to arrange seven parameters: depth of cut, feed rate, cutting speed, feed velocity, rake angle, cutting edge angle, and workpiece material parameter, i.e., tensile strength. The obtained results, including additional validation tests, showed a very good prediction capacity of the DA-based model in estimating the cutting force during the turning process. The analysis includes the influence of parameters on the cutting force as well as examination of 3D surface diagrams and correlation coefficients. The chip slenderness ratio proved to be the most important dimensionless group for cutting force prediction. By performing additional experimental trials, the correction coefficient for the tool nose radius was estimated and extended models were developed. The well-known Victor-Kienzle model can be used to predict the cutting force if the exact values of m c and k c1.1 coefficients. The proposed DA-based models proved to be valid for predicting all three cutting force components with high accuracy.
2025-02-10
Filip Gorski, Olga Komorowska, Przemysław Zawadzki, Wiesław Kuczko, Magdalena Żukowska, Remigiusz Łabudzki, Răzvan Păcurar
This paper presents the results of automation of the design of a modular upper limb prosthesis. The process of modernization of the existing, standard CAD model in the Autodesk Inventor program was described, introducing structural changes and enriching the geometric form with the knowledge of the design process using the tools of the iLogic module. In addition, the CAD model was combined with special design tables, thus obtaining a KBE class solution. The result is a special, intelligent generative CAD model that allows for the automation of the process of designing various variants of prostheses, tailored to the patient's anthropometric characteristics. The results of the work were then integrated with the AutoMedPrint system developed at the Poznan University of Technology, thanks to which it was possible to test the operation of the developed solution on real data. Based on the measurement results from the 3D scanning process, various variants of the modular prosthesis were automatically prepared for three patients. The final task described in the work is the process of manufacturing the selected variant of the prosthesis in the additive technique. The results of fitting the prosthesis and the opinion of the patient were also presented.