Journal of The Brazilian Society of Mechanical Sciences and Engineering最新文献

The jet characteristics of the nozzles in high-pressure water scale removal systems are crucial to the quality of scale removal. The effects of different turbulence models, Reynolds numbers, and target distances on jet velocity, pressure, water volume, shear force, and descaling width and thickness in the free jet, wall jet, and stagnation zones were studied and compared with experimental data. It is found that the simulation results of the renormalization group (RNG) k-ε model are more closely matched to the experimental results. When the jet reaches approximately z = − 40 de, the velocity and dynamic pressure rapidly decay. The volume of water on the jet axial line gradually decreases at z = 0 to − 40 de. As the jet gradually approaches the surface of the slab, the peak and trough values of the vertical velocity along the striking force line near the wall gradually decrease. As the target distance increases, the volume of jet water, impact pressure, and wall shear gradually decrease, and the jet width gradually increases. As the Reynolds number increases, the volume of the jet water changes less, and the impact pressure and wall shear force increase rapidly. When the descaling pressure is 16 MPa, the impact force of the jet cannot meet the requirements of descaling. Based on research content, a dimensionless functional relationship between descaling width, thickness, strike pressure, target distance, and Reynolds number has been established.

In this paper, the nonlinear large amplitude vibrations of functionally graded material porous annular sector plates have been investigated. The effects of considering nonlinearity in scrutinizing the large amplitude thermally induced vibrations of the annular sector plates have been studied. Additionally, in this work, the effects of moisture distribution and the presence of material porosity are taken into account. Material and temperature distribution in the thickness direction of the plate cause changes in the hygro-thermo-mechanical material properties in the corresponding direction. To define the dependence of material properties on position and temperature, the power law distribution and the Touloukian formula are employed, respectively. Next, according to the classical theory of thermoelasticity, the one-dimensional heat conduction equation along the thickness direction of the plate is solved and the temperature profile is obtained. Afterward, knowing the temperature distribution, the nonlinear equations of motion, which are derived employing Hamilton’s principle, are solved for each time step. To numerically solve the equations, first, using the generalized differential quadrature method (GDQM), the spatial derivatives are discretized and a differential equation with only the time derivatives is achieved, then the time-dependent differential equation is solved utilizing the Newmark implicit time integration method. Finally, a parametric numerical analysis is conducted to investigate the effects of various parameters on the vibrational behavior of the plate.

In the present era dominated by Industry 4.0, the digital transformation and intelligent management of industrial systems is significantly important to enhance efficiency, quality, and the effective use of resources. This underscores the need for a framework that goes beyond merely boosting productivity and work quality, aiming for a net-zero impact from industrial activities. This research introduces a comprehensive and adaptable analytical framework intended to bridge existing gaps in research and technology within the manufacturing sector. It encompasses the essential stages of using artificial intelligence (AI) for modelling and optimizing manufacturing systems. The effectiveness of the proposed AI framework is evaluated through a case study on electric discharge machining (EDM), concentrating on optimizing the electrode wear rate (EWR) and overcut (OC) for aerospace alloy Inconel 617. Utilizing a comprehensive design of experiments, the process modelling through an artificial neural network (ANN) is carried out, accompanied by careful fine-tuning of hyperparameters throughout the training process. The trained models are further assessed using an external validation (Val_ext) dataset. The results of the sensitivity analysis indicated that the surfactant concentration (S_c) has the highest level of influence, accounting for 52.41% of the observed influence on the EWR, followed by the powder concentration (C_p) with a contribution of 33.14%, and the treatment variable with a contribution of 14.43%. Regarding OC, S_c holds the highest percentage significance at 72.67%, followed by C_p at 21.25%, and treatment at 6.06%. Additionally, parametric optimization (PO) shows that EWR and OC overcome experimental data by 47.05% and 85.00%, respectively, showcasing successful performance optimization with potential applications across diverse manufacturing systems.

To address the issue of machining errors in large rotary workpieces caused by geometric errors of heavy vertical lathes, this study focuses on measuring and modeling geometric errors of such lathes. By identifying the specific geometric errors that impact the machining accuracy of heavy vertical lathes and extracting the crucial ones, the study investigates the contribution degree of the error of geometric errors during the machining process of the wind turbine bearing raceways. The study combines the geometric error fitting function to predict the position error and profile error of feature points on the bearing raceway cross-sectional curve. It also performs an inverse evaluation of the contact angle of the wind turbine bearing raceways. The study aims to clarify the influence of crucial geometric errors in heavy vertical lathes on machining errors during the machining process of wind turbine bearings. The findings provide theoretical and practical guidance for the subsequent design and manufacture of heavy machine tools and the vertical turning process of large rotary workpieces.

In order to enhance productivity and automate the rough milling process of pockets, it is crucial to select the optimal set of tools that offer reduced cutting time. In this article, a new method for the automatic selection of a toolset has been developed to effectively remove material during the roughing stage of a pocket with any contour, whether formed by line–line or line–arc, convex or concave. The proposed software selects a maximum of three tools based on the pocket's boundary. These algorithms test available tool combinations on the machine tool and provide the suitable toolset that minimizes cutting time further. This is done through a visual test, and if the residue in the corners takes the form of a polygon, an algorithm adds a third tool. In this case, the software will choose the best combination, either two or three tools, that minimizes machining time even more. The good news is that the machining simulation and the result file are obtained in a reduced time. It also provides the toolpath coordinates that can be transferred directly to the machine. To demonstrate the effectiveness of our approach, we also conducted a detailed comparison with conventional methods found in the literature. The results obtained show that our new approach minimizes both toolpath length and cutting time with the optimal toolset compared to other methods.

Graphical Abstract

This study aimed to fabricate hybrid metal matrix composites of AZ91D magnesium reinforced with varying various weight percentages of SiC and constant weight percentages of BN particles through the stir-squeeze casting method. The influence of the particle ratio on the microstructure and wear behaviour of the composites was studied. The dispersion patterns of particles within the matrix and the interactions between the alloy and the particles were thoroughly investigated using a variety of techniques, including optical microscopy, SEM, EPMA, and EDS.XRD analysis of the AZ91D/SiC/BN hybrid composite revealed a significant volume proportion of the strong Mg17Al12 phase. The synthesized magnesium hybrid composites (AZ91D/9%SiC/3%BN) experienced a volume loss reduction of up to 36.16% under a maximum load of 30 N and a maximum speed of 1 m/s when compared with the monolithic material AZ91D. The results of these analyses demonstrated that the resulting composites exhibited an even dispersion of particles, superior grain structure, and strong interfacial bonding between the AZ91 alloy and the reinforcing particles. The newly developed magnesium hybrid composites have better wear performance than monolithic AZ91D alloys. These findings highlight the enhanced wear resistance of the fabricated composites for antiwear applications.

In this study, glass/epoxy (GFRP), carbon/epoxy (CFRP) and glass-carbon/epoxy hybrid (GCFRP) composites were aged in seawater, engine oil and diesel fuel degradation environments for 30, 60 and 90 days. The effect of aging environment and time on the structural strength of the composite was examined by applying tensile, three-point bending and low-velocity impact tests to aged composites. Scanning electron microscopy analyses were compared to detect fracture damage occurring in the internal structure of the composites. It was concluded that the degradation environment that most affects the mechanical strength of composites is seawater. Degradation resistance is improved due to the glass/carbon hybridization effect. It has been determined that the glass-carbon hybridization effect in GCFRP composites significantly changes their mechanical strength compared to GFRP and CFRP composites stacked alone. By comparing the glass-carbon hybridization effect in CFRP composites with GFRP and CFRP composites stacked alone, their advantages under different tests are clearly emphasized.