Performance Of Machinery Research Articles

Effect of Bio-Inspired Cutout Shapes at the Leading Edge of Propellers on Noise and Flight Efficiency

In recent years, the application of bio-inspired structures has garnered attention for enhancing the performance of fluid machinery. In this study, we experimentally investigated the effects of introducing a bio-inspired cutout structure to the propellers of drones, aiming to improve thrust efficiency and reduce noise levels. Our results demonstrated reductions in noise levels compared to conventional propellers. Parametric studies revealed that the roundness of the structure significantly influenced both flight efficiency and noise levels, suggesting its importance for replicating the inherent fluid characteristics found in nature. Additionally, optimal parameters for noise reduction, such as the length of the cutout, angle of incision relative to the flow direction, and the distance between the gap were identified. Although no improvements in flight efficiency were observed, most of the models investigated exhibited only around a 5% reduction in efficiency compared to the standard propellers, suggesting practical applicability for scenarios such as nighttime drone operations in urban areas. The noteworthy reduction in sound pressure levels in the mid- to high-frequency range achieved by the bio-inspired propellers in this study holds the potential to address the issue of drone noise pollution and encourage drone operations in urban areas. Moreover, the confirmed decrease in sound pressure at specific frequencies and the suggested controllability hint at the possibility of enhancing sound source localization performance using drones.

Experimental and numerical studies on the propagation paths of gear root cracks

Gear bending fatigue failure has a significant impact on the operational performance of advanced machinery, such as electric vehicles and wind turbines, potentially leading to failures and catastrophic consequences for transmission systems. Several methods are currently used to predict gear crack propagation; however, a comprehensive comparison of their accuracy and efficiency in predicting crack paths is lacking. In this study, the propagation path of root cracks in commonly used automotive gears was investigated using finite element (FE) analysis and experimental testing. Three mixed crack path prediction criteria were integrated to predict the gear root crack propagation using the commercial software ABAQUS by user-defined Python script. The impacts of gear crack parameters, including the gear initial crack length, on gear root crack propagation behaviour were analysed. The simulations were verified by the gear bending fatigue test using a single tooth bending test device. The results indicate that the simulation outcomes align with the experimental tests, demonstrating that all three criteria are effective in predicting gear root crack propagation behaviours.

Dynamic characteristics of a flexible rotor supported on surface textured journal bearings

Abstract This paper presents the dynamic characteristics of a flexible rotor supported on surface textured journal bearings. Oil lubricated journal bearings exhibit the self-exited whirling of rotor, leading to instability. Understanding the threshold speed of instability is crucial in mitigating excessive vibration in rotating machinery. In this work, a simple model of a flexible rotor with central disc is studied. Modal analysis is carried out to obtain the whirl frequencies of the system, mode shapes, and stability limit speed. The addition of surface textures to the journal bearing stands out as a significant modification aimed at improving performance. Fully textured and partially textured bearing surfaces are considered in the investigation and compared with the performance of smooth journal bearings. Notably, fully textured bearings enhance stability limits. This study offers insights essential for optimizing machinery performance and minimizing operational risks.

Study on the formation and characteristics of unstable sheet cavitation on hydrofoils

Hydrofoils, as fundamental components of hydraulic machinery, directly influence the performance of such machinery. This study conducted an analysis of the cavitation characteristics of hydrofoils at a + 4° angle of attack under various cavitation numbers using numerical simulation and experimental research methods. The focus of the research was to explore the phenomenon of unstable sheet cavitation and its causes, as well as to reveal the characteristics of the re-entrant jet. The large eddy simulation method was employed to calculate the cavitation morphology under three different cavitation numbers. The method is highly consistent with the experimental results in simulating the small-scale detachment at the tail of unstable sheet cavitation and the large-scale shedding of cloud cavitation. The study found that the detachment of unstable sheet cavitation is closely related to the re-entrant jet, which exhibits transient and abrupt characteristics during the unstable sheet cavitation phase. Furthermore, by applying FFT processing to the distribution of maximum reverse velocity and the spatiotemporal changes of Ux on characteristic lines, eigenfrequency of the detachment of unstable sheet cavitation were identified. The research results indicate that cavitation mainly show as sheet cavitation when the cavitation closure point does not exceed the zero-slope point. Beyond this point, it transitions to cloud cavitation. This study provides new insights into the cavitation phenomenon of hydrofoils and offers quantitative research on the phenomenon of unstable sheet cavitation.

Linear Active Disturbance Rejection Control System for the Travel Speed of an Electric Reel Sprinkling Irrigation Machine

The uniformity of the travel speed of electric reel sprinkling irrigation machines is a key factor affecting irrigation quality. However, conventional PID control is susceptible to sudden disturbances under complex farmland conditions, leading to reduced speed uniformity. To enhance the robustness of the control system, it is necessary to investigate new disturbance rejection control algorithms and their effects. Therefore, a kinematic model of the reel sprinkling irrigation machine and a brushless DC (BLDC) motor model were established, and a linear active disturbance rejection control (LADRC) strategy based on improved particle swarm optimization (IPSO) was proposed. The simulation results show that under variable speed conditions, the system exhibits no overshoot, with an adjustment time of 0.064 s; under variable load conditions, the speed vibration amplitude is less than 0.3%. The field test results indicate that at travel speeds of 10 m/h and 30 m/h, the maximum absolute deviation rate under IPSO-LADRC control is reduced by 27.07% and 13.98%, respectively, compared to PID control. The control strategy based on IPSO-LADRC effectively improves the control accuracy and robustness under complex farmland conditions, providing a reference for enhancing the control performance of other electric agricultural machinery.

Mao-based solid-like slippery composite coating with superior corrosion resistance and robust machinery performance upon magnesium-lithium alloy LA81

Slippery liquid-infused porous surface exhibits a great potential for corrosion management with super repulsive water properties, whilst their poor adhesion to underlying materials and durability of lubricants remains a challenge. In this work, a micro-arc oxidation (MAO)-based solid-like slippery composite coating (SSC) is developed upon Mg-Li alloy LA81 by combining mechanical pinning effects of MAO and super repulsive water performance of SSC, which maximizes adhesion strength, water repulsion and sustainable corrosion protection. The SSC coatings exhibit liquid-to-solid transition under the curing effect of epoxy resin. Efficient sealing of the MAO layer is yielded through high fluidity of the liquid phase of SSC. Cured SSC can further strengthen the interface bonding with respect to underlying MAO layer, and significantly alleviate lubricant loss.

Ionic Liquids as Extreme Pressure Additives for Bearing Steel Applications

The protection of steel surfaces from wear under extreme pressure conditions is of major importance in several industries as it provides better performance and longer life of machinery. The motivation for this work was to study the lubrication of steel by ionic liquids (ILs), which have recently emerged as greener alternatives to commercial lubricants and additives. Three ILs based on sulfur-containing anions, used as 2-wt% additives in polyethylene glycol base oil (MW 200; PEG 200), were tested in the lubrication of ASTM 52100 bearing steel contacts in extreme pressure conditions (under mixed lubrication with a Hertzian pressure of 1.12 GPa) using a mini traction machine (MTM). Due to the poor resistance to corrosion of bearing steel, a semi-ester of succinic acid derivative corrosion inhibitor (Lanxess RC 4801) was added to the mixtures at a 1 wt% concentration. The ILs 1-hexyl-methylimidazolium trifluoromethanesulfonate ([C6mim][TfO]) and 1-hexyl-4-picolinium trifluoromethanesulfonate ([C6-4-pic][TfO]) revealed promising results in terms of surface protection of bearing steel. In contrast, 4-picolinium hydrogen sulfate ([4-picH][HSO4]) as 2-wt% additive to PEG 200 + 1% RC 4801 did not show any improvement in wear performance compared to neat PEG 200 + 1% RC 4801. PEG 200 + 2% [C6mim][TfO] + 1%RC 4801 allowed for a decrease in wear up to ~ 76% and PEG 200 + 2% [C6-4-pic][TfO] + 1%RC 4801 up to ~ 46% when compared with neat PEG 200 + 1% RC 4801. Optical microscopy images suggest the formation of an adsorbed layer, which was further supported by chemical analysis via x-ray photoelectron spectroscopy (XPS) data for [C6mim][TfO].Graphical abstract

OIL DUST INDICATOR

An oil dust indicator is a crucial component in maintaining the efficiency and longevity of machinery and engines. It serves the dual purpose of monitoring both oil level and contamination levels, particularly from dust and particulate matter. The indicator typically consists of a sensor, a processor unit, and a display or alert mechanism. The sensor, often a combination of optical and pressure sensors, continuously monitors the oil. clarity and level. When dust or other contaminants accumulate in the oil, the sensor detects changes in clarity and triggers an alert. The processor unit interprets these signals and activates the display or alert mechanism, informing operators of the need for maintenance. In addition to indicating the need for oil replacement or cleaning, advanced oil dust indicators can also provide data on the severity of contamination, allowing for proactive maintenance planning. This capability can significantly reduce downtime and maintenance costs while optimizing the performance and lifespan of machinery and engines. Oil dust indicators find applications in various industries, including automotive, manufacturing, and power generation. They play a vital role in ensuring the smooth operation of critical machinery and equipment, making them indispensable tools for maintenance professionals and operators alike. This study proposes an advanced oil dust indicator that incorporates sensors for total dissolved solids (TDS), color, and pH levels to provide comprehensive monitoring of oil quality. Keywords: oil dust detector can sense dust in oil.

Performance analysis of agricultural machinery with aid digital agriculture

Digital agriculture has increasingly gained strength in the countryside, as it is considered a byproduct of growing globalization. As the search for production optimization, cost reduction, and productivity increase continues, these tools become the great allies of the producer in the final agricultural production process.Therefore, agricultural operations must also follow this evolution in the development of technological improvements, which carry positive aspects for large-scale production. In this context, the objective of this work was to evaluate the performance of seeders-fertilizers with different input distribution mechanisms. Since one of the biggest expenses in the production process is the acquisition of seeds and fertilizer, any savings in production costs become a significant advantage for the producer, who will have a greater net income. The experimental design used to evaluate the data followed the premises of Statistical Process Control (SPC), and the results were assessed using individual value control charts. It was concluded that the production process was negatively affected by the performance of the seeders-fertilizers, as in one area, more seeds were falling than necessary, increasing the cost per hectare and not adhering to the principles of cost minimization and profit maximization. Conversely, in the second area evaluated, the machinery's performance resulted in fewer seeds being deposited than planned. Consequently, according to the results, the producer lost the opportunity to produce 5.85 sc/ha without reducing costs in the operation, as the seeder simply did not deposit the seeds, thereby reducing the gain per area, which could have been more productive, increasing final profitability.

Static Task Allocation Method for Multi-Machines in Cooperative Operations Combining OGFR-GA and MLW-Prim

Aiming at the operational efficiency of small farm machinery groups in cooperative operations in hilly areas, this study proposes a static task allocation model. This method combines the optimal gene fragment retention method based on a genetic algorithm (OGFR-GA) and the method for generating multi-loop weighted connected graphs based on Prim’s algorithm (MLW-Prim). The collaborative objective function for the harvester group was established by considering factors such as operation time, fuel consumption, and distance. The OGFR-GA was designed and applied multiple times to obtain several optimal gene fragments corresponding to the number of farm machines. These fragments were used as critical paths in the weighted connected graph generated based on farm machinery performance parameters and task parameters. The MLW-Prim method was proposed to construct this weighted connected graph and realize the static task allocation model for multi-machine cooperative operations. Simulation experiments showed that the model combining OGFR-GA and MLW-Prim achieved optimal values with fewer iterations, and reduced both group cost and cost variance compared to traditional algorithms. This method meets the static task allocation needs for multi-machine cooperative operations in agricultural production and can be integrated with intelligent operations in mountainous and hilly regions, laying a theoretical foundation for improving efficiency.

Energy Efficiency Measurement of Mechanical Crushing Based on Non-Contact Identification Method

The efficiency of mechanical crushing is a key metric for evaluating machinery performance. However, traditional contact-based methods for measuring this efficiency are unable to provide real-time data monitoring and can potentially disrupt the production process. In this paper, we introduce a non-contact measurement technique for mechanical crushing efficiency based on deep learning algorithms. This technique utilizes close-range imaging equipment to capture images of crushed particles and employs deeply trained algorithmic programs rooted in symmetrical logical structures to extract statistical data on particle size. Additionally, we establish a relationship between particle size and crushing energy through experimental analysis, enabling the calculation of crushing efficiency data. Taking cement crushing equipment as an example, we apply this non-contact measurement technique to inspect cement particles of different sizes. Using deep learning algorithms, we automatically categorize and summarize the particle size ranges of cement particles. The results demonstrate that the crushing efficiencies of ore crushing particles, raw material crushing particles, and cement crushing particles can respectively reach 80.7%, 70.15%, and 80.27%, which exhibit a high degree of consistency with the rated value of the samples. The method proposed in this paper holds significant importance for energy efficiency monitoring in industries that require mechanical crushing.

Physicochemical characterization of 10W-40 engine oil irradiated with gamma-rays

Today, the increasing use of oil and grease in industry with the aim of improving the performance of industrial machinery, equipment and protecting against damage to mechanical parts during work is not hidden from anyone. In this experimental study, the effects of gamma-rays on some physicochemical properties including the pour point, flash point, viscosity index, and kinematic viscosity of an engine oil at 40 °C, and 100 °C were investigated at various radiation doses up to 100 kGy. For this purpose, a commercial engine oil of type SAE 10W-40 was selected. To explore the structural changes of the samples at different doses, FTIR, and UV–Vis analyses were performed. These analyses confirmed the deformation of CH2 and CH3 groups in aldehydes at 2976-2838 cm−1, 1468 cm−1, and 1454 cm−1, and also predominance of the chain-scission or degradation up to 50 kGy. The findings indicated that within 0–50 kGy, the viscosity decreased significantly as the dose increased, while at 100 kGy, the hydrocarbons underwent crosslinking, leading to a negligible increase in the viscosity. Therefore, the stability of certain properties of the oil plays a crucial role in ensuring radiation tolerance designed for radiation applications.

Prediction method for maize kernel impact breakage based on high-speed camera and FEM

Processes such as threshing and processing determine the subsequent storage, nutritional value, and economic value of maize kernels. The breakage characteristics of maize kernels in threshing and processing machinery are unknown, which greatly limits the need for new developments in low-loss threshing. In this paper, the breakage characteristics of maize kernels under different impact postures and impact velocities were investigated by a combination of FEM and bench tests. The results showed that the breakage evolution rules of kernel breakage modes were the result of stress concentration and energy conversion. The maize kernel with side posture had the smallest critical breakage velocity of 22.5 m/s for breaking into pieces. A breakage probability model for predicting the degree of maize kernel breakage was proposed, and the energy and velocity thresholds provided a method for the digital design of breakage reduction performance of maize threshing machinery and detection performance of impact breakage machinery.

Numerical study of separation flows in a U-duct using DDES method

Separation flow in a curved duct is a common phenomenon in engineering applications, and it highly contributes to the performance of fluid machinery. Accurate prediction of curved duct flows using the computational fluid dynamics method remains a challenge due to the limitations of turbulence modeling. Hence, the high-fidelity method of the delayed detached eddy simulation (DDES) approach is employed to simulate the U-duct flow with a Reynolds number of 105. The DDES results are compared with experimental data from the study by Monson et al. (1990) and analyzed in detail. The Q-criterion is defined to analyze the vortex structures and study the mechanism in the flow separation region. Discussions are made on turbulence characteristics, including turbulence energy spectra, helicity density, and turbulence anisotropy in the U-duct flow. Results indicate that the regions near the wall and within flow separation are highly anisotropic. The turbulence near the wall region is in a two-dimensional state, and the turbulence within the flow separation region is in a “rod-like” state.

Effect of Soil Texture on Agricultural Machine Performance in Sylhet, Bangladesh.

Soil texture is one of the primary soil physical properties that largely affects agricultural machine performance. Before implementing an agricultural machine at the farm level, it is important to test the machine to gain an understanding of its technical functionality and economic viability to get the best output from it. The research was carried out to assess how soil texture influences the performance of machinery, aiming to determine the effectiveness of agricultural equipment on a particular type of land. Soil samples were collected from nine different fields, and their textures were determined using hydrometer analysis. To correlate soil textural classes with machine performance, machinery data—effective field capacity, forward speed, and field efficiency—were calculated by operating a combine harvester on the study areas during the harvesting season of Boro rice. Two types of soil were identified: loamy sand and sandy loam. The findings of the study showed that machine efficiency has a positive correlation with sand proportion and a negative correlation with clay and silt fractions. It was observed that soil with loamy sand-type textural classes demonstrated better field efficiency, higher effective field capacity, and higher forward speed compared to sandy loam soil. Small and irregularly shaped fields also caused variation in performance. The study identified significant variation in speed between these two types of soils but no significant effect on effective field capacity or field efficiency at the 5% level of significance. The outcomes of this research will help farmers make informed decisions when selecting appropriate equipment for their agricultural operations.

Review of Data Processing Methods Used in Predictive Maintenance for Next Generation Heavy Machinery

Vibration-based condition monitoring plays an important role in maintaining reliable and effective heavy machinery in various sectors. Heavy machinery involves major investments and is frequently subjected to extreme operating conditions. Therefore, prompt fault identification and preventive maintenance are important for reducing costly breakdowns and maintaining operational safety. In this review, we look at different methods of vibration data processing in the context of vibration-based condition monitoring for heavy machinery. We divided primary approaches related to vibration data processing into three categories–signal processing methods, preprocessing-based techniques and artificial intelligence-based methods. We highlight the importance of these methods in improving the reliability and effectiveness of heavy machinery condition monitoring systems, highlighting the importance of precise and automated fault detection systems. To improve machinery performance and operational efficiency, this review aims to provide information on current developments and future directions in vibration-based condition monitoring by addressing issues like imbalanced data and integrating cutting-edge techniques like anomaly detection algorithms.

ПРОТИВОКОРРОЗИОННАЯ ЗАЩИТА МЕДИ МАСЛЯНЫМИ КОМПОЗИЦИЯМИ С КОМБИНИРОВАННОЙ ДОБАВКОЙ М-531

The use of anti-corrosion protection is necessary to maintain the performance of agricultural machinery and equipment. The effectiveness of oil compositions with the addition of M-531 in relation to copper was studied. The compositions were obtained on the basis of industrial and motor M10G2k (both fresh and used) oils. Their protective ability was studied using gravimetric and electrochemical methods (polarization and impedance) studies. According to the data obtained from corrosion gravimetric tests in 0.5 M NaCl, it was noted that compositions based on M10G2k oil turned out to be the most effective when 5 wt. % of the additive, the protective effectiveness (Z) was 63%, a further increase in concentration led to a slight increase to Z = 65% at 10 wt. % M-531. The data from electrochemical measurements qualitatively confirm the data from gravimetric tests. Differences in the magnitude of protective effectiveness are associated primarily with different times of exposure to an aggressive environment: the duration of the experiment in the first case was 15 minutes, in the second - 14 days. According to the results of polarization measurements, compositions based on MMO were the most effective, already at a content of 5 wt. % additive value Z = 98%, while compositions containing 5 wt. % M-531 in I20-A and M10G2k had Z = 90%. The results of impedance studies showed that the addition of the M-531 inhibitor to MMO, M10G2K and I20-A oils affects the copper impedance hodographs. The inhibitor concentration had a significant effect on the change in Nyquist diagrams. At 3 wt. % M-531, a moderate change in copper plating impedance was observed, indicating that the inhibitor has some protective ability against corrosion, but its effectiveness may not be sufficient to fully protect copper. When the inhibitor concentration increases to 5 - 7 wt. % noticeable increase in the change in impedance indicated that a higher concentration of the M-531 inhibitor helps to improve the protective properties of the copper coating.

Systems Reliability and Data Driven Analysis for Marine Machinery Maintenance Planning and Decision Making

Understanding component criticality in machinery performance degradation is important in ensuring the reliability and availability of ship systems, particularly considering the nature of ship operations requiring extended voyage periods, usually traversing regions with multiple climate and environmental conditions. Exposing the machinery system to varying degrees of load and operational conditions could lead to rapid degradation and reduced reliability. This research proposes a tailored solution by identifying critical components, the root causes of maintenance delays, understanding the factors influencing system reliability, and recognising failure-prone components. This paper proposes a hybrid approach using reliability analysis tools and machine learning. It uses dynamic fault tree analysis (DFTA) to determine how reliable and important a system is, as well as Bayesian belief network (BBN) availability analysis to assist with maintenance decisions. Furthermore, we developed an artificial neural network (ANN) fault detection model to identify the faults responsible for system unreliability. We conducted a case study on a ship power generation system, identifying the components critical to maintenance and defects contributing to such failures. Using reliability importance measures and minimal cut sets, we isolated all faults contributing over 40% of subsystem failures and related events. Among the 4 MDGs, the lubricating system had the highest average availability of 67%, while the cooling system had the lowest at 38% using the BBN availability outcome . Therefore, the BBN DSS recommended corrective action and ConMon as maintenance strategies due to the frequent failures of certain critical parts. ANN found overheating when MDG output was above 180 kVA, linking component failure to generator performance. The findings improve ship system reliability and availability by reducing failures and improving maintenance strategies.

Chatter detection in simulated machining data: a simple refined approach to vibration data

Vibration monitoring is a critical aspect of assessing the health and performance of machinery and industrial processes. This study explores the application of machine learning techniques, specifically the Random Forest (RF) classification model, to predict and classify chatter—a detrimental self-excited vibration phenomenon—during machining operations. While sophisticated methods have been employed to address chatter, this research investigates the efficacy of a novel approach to an RF model. The study leverages simulated vibration data, bypassing resource-intensive real-world data collection, to develop a versatile chatter detection model applicable across diverse machining configurations. The feature extraction process combines time-series features and Fast Fourier Transform (FFT) data features, streamlining the model while addressing challenges posed by feature selection. By focusing on the RF model’s simplicity and efficiency, this research advances chatter detection techniques, offering a practical tool with improved generalizability, computational efficiency, and ease of interpretation. The study demonstrates that innovation can reside in simplicity, opening avenues for wider applicability and accelerated progress in the machining industry.

Incorporating the Time-synchronous Averaging Method into Vibration Analysis Methodologies for the Detection and Localization of Bearing Defects

Objective: The objectives of this paper are to highlight the significance of vibration analysis, especially in predictive maintenance for rotating machinery, and to emphasize the importance of detecting bearing defects that may result in machinery failure. Methods: The proposed methodology combines the use of time-synchronous averaging (TSA) with existing vibration analysis techniques. TSA involves aligning vibration data with specific events or phases in the machinery's operation, such as shaft rotation. By synchronizing the data in this way, the methodology aims to reduce noise and enhance the signal related to bearing defects, making them more distinguishable. Additionally, the methodology incorporates well-established vibration analysis techniques. These techniques may include frequency analysis, amplitude modulation analysis, waveform analysis, and others commonly used in the field of condition monitoring and predictive maintenance. Results: The results of the analysis begin with waveform analysis, which involves examining the shape and pattern of vibration signals captured from the pinion. This analysis provides valuable insights into the dynamic behavior of the pinion gear, including any variations or abnormalities in its motion. Moreover, the use of synchronized waveforms is crucial in this analysis. By aligning the vibration data with specific events or phases in the gear mesh cycle, such as tooth engagement, the analysis can pinpoint moments when potential faults or wear in the machinery may occur. This synchronization allows for a more precise assessment of the vibration signals, enabling the detection of irregularities that may indicate underlying issues with the pinion or other components of the machinery. Conclusion: A pivotal aspect of the methodology involves envelope spectra analysis, significantly enhancing diagnostic capabilities. This analysis identifies fault patterns that might not be readily apparent in conventional vibration analysis. The incorporation of envelope spectra proves instrumental in proactive maintenance, enabling early detection of potential issues. This, in turn, contributes to the overall reliability and optimization of machinery performance.