The main goal of this paper is to determine the influence of cooling technique on surface roughness during up and down face milling of aluminum alloy ENAW-2011 T6. Along with dry machining, three cooling techniques were observed: cutting fluid (CF), minimum quantity lubricant (MQL) and cold compressed air (CCA). Thirteen experiments were conducted for each technique. Following the defined plan of the experiment, cutting speed and feed per tooth were varied. An optical profilometer was used to analyze arithmetic deviation of the profile (Ra) and arithmetic mean of the absolute height (Sa). Down milling produced up to 24 % lower Ra and Sa values in comparison to up milling. Increasing feed per tooth greatly increased surface roughness while increasing cutting speed led to a 12 % to 14 % decrease in surface roughness. Using the same cutting parameters, CCA produced the lowest, while CF produced the highest Ra and Sa values. Using the test results and regression analysis, mathematical models were generated allowing for precise Ra and Sa predictions. Optimization of the regression models was carried out with the goal of achieving the lowest surface roughness for each milling strategy and applied cooling technique.
This paper discusses an innovative approach to experimentally determine the behaviour of rotational pivot behaviour by photogrammetric measurements. The compliant rotational pivot was selected for research as it is a well understood compliant mechanism whose deformations can be calculated analytically or numerically, allowing easy verification of the results. The mechanism was redesigned for monolithic additive manufacturing with the selection of printing directions that have reduced the influence of material anisotropy. Rapid developments in image processing and computer vision have resulted in integration of photogrammetry and digital image correlation into a wide range of applications. The primary contribution of this article is in the custom designed experimental pure bending load testing setup with an additional optical displacement measurement system, which was used to study the behaviour of additively manufactured compliant mechanisms. A high quality, consumer-grade camera was used for image capture, while image processing was performed using ready-made and custom developed MATLAB tools. Also, a redesigned compliant mechanism with sufficient precision for applications in low-cost, single-use compliant precise positioning systems was developed, and it was determined whether the selected experimental method is applicable to the research of monolithic compliant rotational joints. The experimental results obtained using this method have been compared to results obtained using finite element analysis and analytical calculations, and it was shown that the results are in good concordance. Therefore, it was concluded that photogrammetric analysis aided by feature recognition is applicable to the measurement of parasitic shift of compliant mechanisms.
This paper suggests the data-driven Iterative Learning Control (ILC) costeffective anti-windup Proportional-Integral (PI) fuzzy control for tower crane systems in terms of a novel direct data-driven fuzzy control approach. The presentation is focused on payload position control. The PI fuzzy controller structure includes a back-calculation and tracking anti-windup mechanism to avoid integrator windup and compensate for the process's dead zone and saturation nonlinearity. The lifted form representation specific to ILC is employed using fuzzy basis functions to model the fuzzy control system. The direct data-driven fuzzy control approach is based on experiments conducted on the closed-loop fuzzy control system to compute the Markov coefficients. The anti-windup PI fuzzy controller's parameters are tuned optimally to fit the Markov coefficients in an appropriately defined optimization problem. This problem is solved using the hybrid Particle Filter-Particle Swarm Optimization algorithm, which has been improved by adding an information feedback model. Experiments and comparisons validate the suggested direct data-driven fuzzy control approach and highlight performance enhancement.
An enormous range of industries is covered by the Industrial Internet of Things (IIoT). IIoT covers any device or system with sensors connected to a GSM/GPRS module or network and gathering/sharing data. Any device in the industry can consist of a lowcost sensor connected to a network, allowing them to be monitored and tracked, sharing data on their status, and communicating with other devices. It means identifying possible problems fast, which leads to cost-effective processes. It can be scaled from a single device up to massive platform distribution of embedded technologies and cloud systems connected in real-time. With all that, numerous communication protocols allow devices and servers to communicate with each other in a more interconnected way. This research provides using the MATT protocol for communication between the GSM/GPRS module and the cloud system. The system is made of PIC18F46K22 microcontroller, GSM/GPRS module RS485 serial communication, and DHT11 temperature sensor. The data are being sent to the ThingSpeak server and environmental parameters are being measured for a certain period of time.
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.
Aluminium metal matrix composites (Al-MMCs) are extensively used in various industrial sectors, including aerospace, automotive, construction, and electronics, owing to higher hardness, low density, higher fatigue and specific strength. Powder metallurgy is an effective method for manufacturing composite materials. Compared to pure metals and alloys, the mechanical characteristics of the SiCreinforced MMCs are enhanced. The Al-MMC surface can undergo metallurgical changes due to the laser treatment, which can also strengthen the binding between the matrix material and the reinforcement particles. Therefore, the current work investigates the impact of SiC particle addition and laser surface treatment on the hardness and wear characteristics of aluminium metal matrix composite (Al-MMC). The Al-MMC is initially fabricated using a powder metallurgy process, and then the MMC is treated with a laser. Compared to the untreated MMC, the laser surface treatment increased the hardness by almost 12%. Additionally, the addition of SiC content by 10%, 15%, 20%, and 25% in laser-treated Al-MMC resulted in increased hardness by 12%, 14%, 15%, and 16%, respectively, compared to untreated Al-MMC. Furthermore, the wear resistance improved as the reinforcement particles increased. The laser-treated samples exhibited lower wear than untreated ones due to the formation of a new layer on the treated surface, preventing the release of SiC particles. The surface treatment ofMMC through the laser is a novel approach to fabricating wear-resistant Al-MMCs.
Evaluating the Digital Transformation Capability (DTC) of logistics enterprises is crucial for informing policy support and guiding strategic decisions. To address the inherent uncertainties in evaluation process, this study propounds a novel hybrid evaluation framework within a spherical fuzzy (SF) environment. The framework integrates the Symmetry Point of Criterion (SPC), the Stepwise Weight Assessment Ratio Analysis (SWARA) model, and a Regret Theory (RT)-based Alternative Ranking Order Method accounting for two-step normalization (AROMAN). In the following, a new spherical fuzzy score function and Sugeno-Weber operators are introduced to ascertain expert weighting and information fusion. This integrated methodology is empirically applied to assess and rank five logistics enterprises against twelve criteria, successfully identifying the top performer. Comprehensive sensitivity and comparative analyses confirm the proposed evaluation framework's robustness, practicality, and superiority over previous approaches.
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.
The durability of natural fiber reinforced composites (NFRCs) in severe environmental conditions is a relevant concern due to their sensitivity to water absorption and related degradation phenomena. This study investigated the ability of hybridized NFRCs, combining flax and glass fibers, to recover their mechanical performance after exposure to salt-fog exposition. The composites were subjected to a humid/dry cycling process, simulating the alternating wet/dry cycles experienced in outdoor marine applications. Three-point bending tests were performed to assess the effects of aging on the composites' strength, stiffness, and toughness. The experimental results revealed that hybrid composites show superior recovery capabilities compared to full flax composites. Hybridization stabilized performance, with only a 10% residual strength reduction after the humid/dry cycle. Furthermore, a simplified toughness map was used with suitable results to effectively evaluate the performances degradation and recovery processes in hybridized NFRCs. This tool can assist to optimize the composite laminates and their durability in critical environmental conditions.
Production of three-dimensional parts in 3D printing process gains growing importance in various fields, such as: aviation and car industry, architecture, medicine, dentistry, etc. Mechanical performance is an important users' requirement for manufacturers of 3D printed parts. Furthermore, printed part highly depends on process parameters, position and orientation of the printed part, and performances of the 3D printer which prints the part. In this paper, based on experimental results, an artificial neural network has been used for modeling the dependence ofprocess parameters and object orientation during printing, on the one side, and tensile strength as very important mechanical performance, on the other side. After establishing abovementioned dependence, the developed neural network has been used as a fitness function for the genetic algorithm while the genetic algorithm has been created for the optimization process. The result of optimization process was a set of optimal process parameters and part orientation giving the maximum tensile strength. The results have shown acceptable potential of the developed methodology for optimizing the 3D printing process as a complex engineering problem.
A circular sector is commonly used in a linkage mechanism, and its frequency property plays an important role in optimization of the linkage mechanism. Fast insight into its vibration property with simple calculation is very meaningful in scientific research. This paper studies the vibration of the circular sector in a porous medium (e.g. water), and a fractal-fractional oscillator is established using the two-scale fractal derivative. He’s frequency formula and Ma’s modification are used to elucidate the circular sector’s periodic property in a porous medium, the results show that the fractal dimension of the porous medium plays an important role in vibration attenuation.
This paper presents a novel approach for formulating a variational principle tailored to microelectromechanical systems (MEMS) through the utilization of the semiinverse method. The resulting variational principle is that of least action, which is of considerable significance. The newly presented variational principle has the potential to be applied in a number of advantageous ways. One of the primary applications of this approach is the determination of the pull-in voltage. The application of this principle allows for a more accurate and efficient determination of the pull-in voltage. This is of paramount importance for the optimal functioning and optimization of MEMS devices. The enhanced accuracy in determining the pull-in voltage enables more precise design and greater reliability of MEMSbased systems. Additionally, the enhanced computational efficiency allows for the saving of valuable time and resources during the design process. Furthermore, the paper addresses the topic of fractal MEMS and puts forth a novel approach to fractional differentiation based on two-scale fractal differentiation, which is anticipated to facilitate the discovery of new insights and optimization strategies for MEMS devices.
The paper describes the concept of a new internal combustion engine. Presented IC engine concept possesses a variable displacement, variable compression ratio, combustion at constant volume, improved intake and exhaust processes, as well as more complete expansion of the working fluid. A comparison of some performance of this new concept with conventional IC engine and electric motors is provided. The results indicate that the performance of IC engine can still be improved. The achieved efficiency of the engine is over 40%. The Ricardo Wave program was used for simulating the characteristics of the engine. CAD model of concept engine was made with CATIA V5.
Understanding the behavior of nonlinear vibrations in stringer-stiffened shell structures is crucial for enhancing the stability and efficiency of advanced aerospace and marine systems. These systems often exhibit complex responses due to geometric and material intricacies, which require analytical methods capable of effectively capturing critical dynamics. This study presents three efficient analytical methods for deriving closed-form expressions for the nonlinear frequency of such systems: the adaptive location point-based He's formulation (ALPF), the square error minimizing-based frequency formulation (SEMF), and the Hamiltonian-based frequency-amplitude formulation (HFAF). These methods offer efficient and straightforward solutions for analyzing nonlinear oscillators without requiring complex iterative procedures. The nonlinear frequency of the stringer-stiffened shell is determined using each method and validated against both exact analytical solutions and numerical results. The results show that the first two methods yield high accuracy for small amplitudes, while their accuracy decreases at higher amplitudes. In contrast, the Hamiltonian-based method maintains high accuracy over a wider range of amplitudes. The original He's formulation is recognized for its simplicity and computational efficiency, making it a practical tool for rapid frequency estimation in stringer-stiffened shell systems. This comparative study offers guidance for selecting appropriate analytical tools for nonlinear vibration analysis of complex mechanical and physical systems.
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.
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.
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.
Three-dimensional integrated circuit (3D IC) technology is essential for ultrahigh-density integration, and Cu-Cu direct mechanical bonding has emerged as a promising alternative due to the limitations of traditional soldering methods. This technology offers excellent power efficiency, high-density packaging, faster processing speeds, and improved heat dissipation. However, existing research has limitations in systematically analyzing the relationship between key parameters affecting Cu-Cu bonding quality such as bonding layer thickness, temperature, grain size, and surface roughness. A comprehensive review, particularly under the low-temperature conditions necessary for 3D IC processes, has been lacking. Therefore, this review introduces the fundamental theory of Cu-Cu bonding and systematically discusses the effects of the parameters on bonding quality. By integrating these factors with a specific focus on low-temperature Cu-Cu interfaces, this review aims to provide a practical and up-to-date guide on Cu-Cu bonding technologies.
In the present work, the heat generation during the plastic deformation of a multiphase material is studied using machine learning (ML) methods. The aim was to predict the temperature increase from the structure-property relationships (SPR) of a microstructure considering various Taylor-Quinney coefficients (TQCs), with the aim of achieving precision and computational efficiency suitable for industry. Using automatic microstructure generation to create datasets and finite element analysis (FEA) to obtain temperature increase-strain curves, the dataset facilitated the training of an ML model. A 3D convolutional neural network (CNN) was developed using the microstructural configuration and TQC value as input and the temperature increase-strain curve as output. The model demonstrated high prediction accuracy. The results indicated that the hard phase fraction significantly impacts the temperature increase, much more than the TQC values. This underlines the potential of the model for a better understanding of material behavior during deformation and its industrial applicability.
This study investigates the stability of periodic solutions of a nonlinear nonlocal strain gradient functionally graded Euler-Bernoulli beam model resting on a visco-Pasternak foundation and subjected to external harmonic excitation. The nonlinearity of the beam arises from the von Karman strain-displacement relation. Nonlocal stress gradient theory combined with the strain gradient theory is used to describe the stress-strain relation. Variations of material properties across the thickness direction are defined by the power-law model. The governing differential equation of motion is derived by using Hamilton's principle and discretized by the Galerkin approximation. The methodology for obtaining the steady-state amplitude-frequency responses via the incremental harmonic balance method and continuation technique is presented. The obtained periodic solutions are verified against the numerical integration method and stability analysis is performed by utilizing the Floquet theory.
