This paper presents a systematic, multidimensional analysis of the historical and engineering evolution in the design of working tools for tillage machines, covering the period from the origins of agriculture to contemporary high-tech solutions. (Research purpose) The study identifies developmental patterns in design approaches, tracing the transition from artisanal production of basic agricultural implements, based on empirical knowledge, to the emergence of scientifically grounded methods of calculation and design, built on the advances in mechanics, materials science, and agrophysics. (Materials and methods) The research demonstrates that the introduction of new materials with improved performance characteristics, combined with the shift toward engineering calculations and virtual modeling (CAD/CAE/CAM), laid the foundation for the transition from universal to adaptive and smart design solutions. (Results and discussion) The study highlights the role of digitalization in improving the reliability, energy efficiency, and environmental sustainability of modern tillage machine components. It identifies key differences between the Soviet engineering school and international design methodologies. The paper underscores the contribution of scientific schools and research institutes to the development of soil-cutting theory and draft resistance calculations. Progress in the hardening of working tools is illustrated through the use of thermal and vibrational treatments, surfacing techniques, and coatings based on hard alloys. These technologies are presented not as isolated processes, but as integral components of the broader evolution in engineering design. Contemporary design trends are examined, including the application of digital twin technology, parametric geometry, precision agriculture technologies, and artificial intelligence. The study also addresses issues related to environmental sustainability, climate-adaptive engineering solutions, and sustainable agricultural practices. (Conclusions) The study concludes that an interdisciplinary approach is essential for the effective design of tillage implements, integrating agronomy, mechanical engineering, materials science, and digital technologies.
Takeshi INOUE, Takahiko SAWADA, Kota NAGANO
et al.
As a simple and highly accurate axial force management method during manufacturing and maintenance of bolt-tightened structures, we examined a method to calculate the axial force from images of the bolt head. In this method, the axial force is calculated using the strain on the bolt head measured by digital image correlation (DIC) technique from images of the bolt head and the relationship between strain on the bolt head and axial force calculated by finite element analysis (FEA). In this report, we have proposed a method for calculating axial force considering bending force on a bolt. Specifically, we have proposed a method for calculating the axial force and bending force ratio using the least squares method, based on a 5th-order approximation curve consisting of two variables: the axial force and the bending force ratio, which is obtained by numerical calculation with the bending force as a parameter, and the strain distribution measured by DIC. In bolt tightening tests using M20 bolt and tapered washers to reproduce bending force, the bending force ratio and direction obtained using the proposed method were in good agreement with those obtained from the strain gauges attached to the bolt shank, confirming that the proposed method can be used to evaluate bending force on bolts. In addition, the axial force calculated with the proposed method was in good agreement with that calculated with the bolt gauge, and the average absolute difference between the two was 4.9 kN for all tests. For test results with a large bending force ratio, the deviation in the axial force calculated before and after considering the bending force was around 14%, and it was confirmed that the proposed method enables more accurate evaluation of axial force in bolted structures where bending force occurs.
Mechanical engineering and machinery, Engineering machinery, tools, and implements
Kateřina Jurajdová Šťastná, Tomáš Dvorský, Vojtěch Václavík
et al.
The aim of this research was to evaluate the impact of revitalization measures implemented in the catchments of the Vortovský Stream and the Valčice Stream, located within the protected landscape area of the Žďárské vrchy in the Pardubice Region, Czech Republic. The assessment was conducted using rainfall–runoff models HEC-HMS 4.13(The Hydrologic Engineering Center’s-Hydrologic Modeling System) and MIKE SHE version 2020 (MIKE System Hydrological European), and hydraulic models HEC-RAS 6.6 and MIKE 11 version 2020. The study focused on comparing the effects of revitalization on flow velocity in the Valčice Stream with its original state, evaluating the ability of the reconstructed ponds Malý Černý and Velký Černý to transform flood waves, and assessing the overall effectiveness of the revitalized areas in water retention within the landscape. The results demonstrate that the reconstruction of the ponds on the Valčice Stream significantly contributed to the safe transformation of flood flows, and that the revitalization of part of the stream resulted in a reduction in flow velocity in the channel. Furthermore, the revitalization measures in the Vortovský Stream catchment were found to have a positive effect on enhancing water retention in the area.
In today’s digital era, the need for a qualified workforce in the field of IT Support is in-creasing along with the rapid development of information technology. The selection of the right employees is very important to ensure the efficiency and effectiveness of companies’ operations. This research aims to develop a decision support system (SPK) in the IT Support division employee selection process using the Simple Additive Weighting (SAW) method. This method was chosen because of its ability to process qualitative and quantitative data to produce optimal decisions. The results of this study show that the proposed system can assist managers in selecting the most suitable candidates based on predetermined criteria. Thus, it is expected that this system can improve the quality of the recruitment process in companies.
Vladislav Ivanov, Angel Alexandrov, Dragomir Vrazhilski
et al.
Very few experimental studies of full pinion engagement planetary gear trains have been published; therefore, their behavior under load is little known. In this paper, the results from the experimental studies of the above-mentioned gear trains are presented, whereby the bending stresses in the planet pins are displayed both in time and frequency domains by means of fast Fourier transform (FFT). The experiments are conducted on a mechanical closed-loop test rig, which was designed especially for the experiments. The bending stresses in the pins are measured by strain gauges, which are mounted in a double half-bridge configuration, thus showing the stresses in two perpendicular planes. The torque applied is 200 Nm. The radial run-out errors of the planets are measured and their relation to the pin loads are analyzed.
In contemporary academic settings, end-of-semester student feedback on a lecturer’s teaching abilities often fails to provide a comprehensive, real-time evaluation of their proficiency, and becomes less relevant with each new cohort of students. To address these limitations, an innovative feedback method has been proposed, utilizing image processing algorithms to dynamically assess the emotional states of students during lectures by analyzing their facial expressions. This real-time approach enables lecturers to promptly adapt and enhance their teaching techniques. Recognizing and engaging with emotionally positive students has been shown to foster better learning outcomes, as their enthusiasm actively stimulates cognitive engagement and information analysis. The purpose of this work is to identify emotions based on facial expressions using a deep learning model based on a convolutional neural network (CNN), where facial recognition is performed using the Viola–Jones algorithm on a group of students in a learning environment. The algorithm encompasses four key steps: image acquisition, preprocessing, emotion detection, and emotion recognition. The technological advancement of this research lies in the proposal to implement photonic hardware and create an optical neural network which offers unparalleled speed and efficiency in data processing. This approach demonstrates significant advancements over traditional electronic systems in handling computational tasks. An experimental validation was conducted in a classroom with 45 students, demonstrating that the level of understanding in the class as predicted was 43–62.94%, and the proposed CNN algorithm (facial expressions detection) achieved an impressive 83% accuracy in understanding students’ emotional states. The correlation between the CNN deep learning model and the students’ feedback was 91.7%. This novel approach opens avenues for the real-time assessment of students’ engagement levels and the effectiveness of the learning environment, providing valuable insights for ongoing improvements in teaching practices.
Engineering machinery, tools, and implements, Technological innovations. Automation
Venkata Kavya Vasam, Abida Shaik, Sai Lakshmi Manasa Tolchuri
et al.
Our method is to keep a close eye on the patient’s health and inform the person responsible for their care on a regular basis. Since it is not possible for an individual to always watch over a single patient in a hospital setting, the primary objective is to monitor the patient around-the-clock. There is going to be aWe employed an apparatus that continuously observed the patient and sent frequent updates to the attending physician, regardless of the physician’s location. This device also has an extra feature that allows it to sound an alarm to notify hospital staff members of a patient emergency.
Symeon Fountoukidis, Nick Rigogiannis, Georgios Voltsis
et al.
The concept of more electric aircraft (MEA) has gained popularity over the last few decades. As the power level of electric loads is constantly increasing, the installation of advanced protection systems becomes of paramount importance. In this context, this paper presents the design process and experimental validation of a solid-state circuit breaker (SSCB), utilizing gallium nitride (GaN) semiconductor switches, under various faulty conditions. In addition, a thermal analysis was carried out in the PLECS simulation platform to find the most appropriate design for the heat dissipation system. Experimental results on the developed GaN SSCB hardware prototype verify its functionality and good performance.
Lucas Pires Bernardes, Pedro Oliveira Conceição Júnior, Fabio Romano Lofrano Dotto
et al.
The wear of tools in machining is one of the primary issues in manufacturing industries. Direct measurements of tool wear, such as microscopic observation, lead to increased machine downtime and reduced production rates. To improve this situation, real-time tool condition monitoring systems (TCMs) are needed, which utilize indirect measurement of tool wear through sensors and signal processing. This project focuses on the use of acoustic emission (AE) sensors for experimental analysis of tool damage under various milling conditions. The proposed approach involves designing condition indicators to quantify this damage by implementing infinite impulse response (IIR) digital filters, specifically Butterworth filters, and fast Fourier transform (FFT), in addition to root mean square (RMS), using different frequency bands of the acoustic signals collected during the process. The results from implementing this study show promise for optimizing the process through an alternative TCM system in manufacturing operations, avoiding the drawbacks of the direct method, and extending the equipment’s lifespan and efficiency. It’s worth noting that this document presents partial results of this implementation, which is still in progress.
Mohammad Toufiqul Hoque, Kristina Klinkhammer, Boris Mahltig
Polyester is the leading man-made fiber in the field of textiles and clothing. Polyester is usually dyed and finished using a process temperature in the range of 120 to 135 ºC. Such a process is known as a high-temperature (HT) process. The application of chitosan on cellulosic materials is an interesting approach to textile functionalization. In contrast, the application of chitosan by the HT process for the functional treatment of polyester is less investigated. With this background, the present study is related to the surface characteristics of different polyester fabrics with implemented chitosan after performing the HT process.
Textile bleaching, dyeing, printing, etc., Engineering machinery, tools, and implements
In order to increase the quality and quantity of agricultural products from greenhouse cultivation, and to cope with a very competitive market, it is necessary to have an optimal climate inside the greenhouse. To achieve this, the farmer uses expensive and very power-consuming heating and cooling systems. In order to solve this problem, a new system has been developed with a solar thermal collector and a specific heat transfer fluid. The experimental study of this new system has shown that the system was able to keep the temperature inside the greenhouse in an optimal range for the development of the plants.
Toan Khac Nguyen, L. Minh Dang, Truong-Dong Do
et al.
Growth factors affect farm owners, environmental conditions, nutrient adaptation, and resistance to chrysanthemum diseases. Healthy chrysanthemum plants can overcome all these factors and provide farms owners with a lot of income. Chrysanthemum white rust disease is a common disease that occurs worldwide; if not treated promptly, the disease spreads to the entire leaf surface, causing the plant’s leaves to burn, turn yellow, and fall prematurely, reducing the photosynthetic performance of the plant and the appearance of the flower branches. In Korea, chrysanthemum white rust disease most often occurs during the spring and autumn seasons, when temperature varies during the summer monsoon, and when ventilation is poor in the winter. Deep neural networks were used to determine healthy and unhealthy plants. We applied the Raspberry Pi 3 module to recognize white rust and test four neural network models. The five main deep neural network processes utilized for a dataset of non-diseased and white rust leaves include: (1) data collection; (2) data partitioning; (3) feature extraction; (4) feature engineering; and (5) prediction modeling based on the train–test loss of 35 epochs within 20 min using Linux. White rust recognition is performed for comparison using four models, namely, DenseNet-121, ResNet-50, VGG-19, and MobileNet v2. The qualitative white rust detection system is achieved using a Raspberry Pi 3 module. All models accomplished an accuracy of over 94%, and MobileNet v2 achieved the highest accuracy, precision, and recall at over 98%. In the precision comparison, DenseNet-121 obtained the second highest recognition accuracy of 97%, whereas ResNet-50 and VGG-19 achieved slightly lower accuracies at 95% and 94%, respectively. Qualitative results were obtained using the Raspberry Pi 3 module to assess the performance of the seven models. All models had accuracies of over 91%, with ResNet-50 obtaining a value of 91%, VGG-19 reaching a value of 93%, DenseNet-121 reaching 95%, SqueezeNet obtaining over 95%, MobileNet obtaining over 96%, and MobileNetv2-YOLOv3 reaching 92%. The highest accuracy rate was 97% (MobileNet v2). MobileNet v2 was validated as the most effective model to recognize white rust in chrysanthemums using the Raspberry Pi 3 system. Raspberry Pi 3 module was considered, in conjunction with the MobileNet v2 model, to be the best application system. MobileNet v2 and Raspberry Pi require a low cost for the recognition of chrysanthemum white rust and the diagnosis of chrysanthemum plant health conditions, reducing the risk of white rust disease and minimizing costs and efforts while improving floral production. Chrysanthemum farmers should consider applying the Raspberry Pi module for detecting white rust, protecting healthy plant growth, and increasing yields with low-cost.
Engineering machinery, tools, and implements, Technological innovations. Automation
Jelle Rijnsdorp, Annemarie van Zwol, Merle Snijders
Many critical applications highly depend on Global Navigation Satellite Systems (GNSS) for precise and continuously available positioning and timing information. To warn a GNSS user that the signals are compromised, real-time interference detection is required. Additionally, real-time classification of the interference signal allows the user to select the most effective mitigation methods for the encountered disturbance. A compact proof of concept has been built using commercial off-the-shelf (COTS) components to analyse the jamming detection and classification techniques. It continuously monitors GNSS frequency bands and generates warnings to the user when interference is detected and classified. Various signal spectrum analyses, consisting of kurtosis and power spectral density (PSD) calculations, as well as a machine learning model, are used to detect and classify anomalies in the incoming signals. The system has been tested by making use of a COTS GNSS signal simulator. The simulator is used to generate the upper L-band GNSS signals and different types of interferences. Successful detection and classification is demonstrated, even for interference power levels that do not degrade the performance of a commercial reference receiver.
In this day and age, surrounded by innumerable forms of technology, the use of various autonomous systems to recognize various ailments has tremendously benefited the medical industry. An important medical practice is the visual evaluation and counting of white blood cells in microscopic peripheral blood smears. Invaluable details regarding the patient’s health may be revealed, such as the discovery of acute lymphatic leukaemia or other serious disorders. This study provides a paradigm for detecting acute lymphoblastic leukemia from a microscopic vision of white blood cells. Microscopic images must go through a thorough pre-processing phase before being classified. In this study, WBCs are separated from blood smear images using morphological techniques, and the segmented region is then searched for a set of textural, geometrical, and statistical properties. Four different machine learning techniques are used to examine the performance of these algorithms: random forest (RF), support vector machine (SVM), naive Bayes classifier (NB), and K nearest neighbor (KNN). The SVM is effective in classifying and identifying the acute lymphoblastic cell that produces leukemia malignancy, as can be observed after careful comparison. A single classifier is virtually completely useless given the variety of blood smear pictures. As a result, we considered using EMC-SVM to classify leukocytes. The suggested method successfully distinguishes white blood cells from sample blood smear images, and accurately categorizes each segmented cell into the relevant group.
This paper proposes a data generation method for one-dimensional aircraft simulation neural network, which is used for data preprocessing of aircraft real-time control health design, which is the basis of aircraft intelligent health prediction algorithm. According to the methodological research needs, this method generates data of different health levels, and the data generator adjusts the health degree of the data by changing the weight, changing the noise size, changing the frequency, etc.; the recognition ability of the redundant fault-tolerant model for the singularity data is studied, and the experimental results show that the system will not ignore the singularity, and the occurrence of singularity will significantly affect the health of the data. Experimental results show that the model can work effectively under the premise that at least one dimension is a fault feature. The intelligent system trained through this data has a better effect.
The paving effect of the bridge deck directly affects the driving comfort of the bridge deck. The traditional paving method depends on the driver’s level, which is difficult to ensure the paving effect of the bridge deck. In recent years, the state has issued a series of documents such as the outline for building a transportation power, which clearly requires the development goal of intelligent transportation. In order to ensure the paving quality of the bridge deck and improve the intelligent level of the bridge deck, this paper developed the asphalt bridge deck paving control system based on BDS and the high-precision paving technology based on 3D mechanical control, which realized the control of the paving track, paving speed and paving elevation of the paver. Finally, the practical application of the intelligent construction technology based on the actual project verified the effectiveness of this technology.
Metal borides have excellent chemical and electrochemical stability, high electrochemical sensitivity, and abundant active sites. According to experiments, metal borides can greatly reduce the shuttle effect in lithium-sulfur batteries and boost their electrochemical performance when used as sulfur host materials. In this thesis, we use polar lanthanum boride materials as materials for lithium-sulfur batteries that modify separators, and take advantage of its strong chemical bonding with polar polysulfides to reduce the shuttle effect. The experimental data demonstrate that the polar lanthanum boride can significantly improve the overall reaction kinetic performance as well as cyclic stability of the cell, resulting in excellent electrochemical performance.
Graham Wild, Luke Pollock, Ayah Khalid Abdelwahab
et al.
Aircraft accidents involving catastrophic fatigue failure have the potential for significant loss of life. The aim of this research was to investigate trends in aircraft fatigue failure accidents to inform aerospace Structural Health Monitoring (SHM) system Research and Development (R&D). The research involved collecting 139 aircraft fatigue failure accident reports from the Aviation Safety Network database, which were coded using a directed content analysis. The trends and features of the categorical data were then explored using an ex-post facto study. The results showed that fatigue failure accidents have increased at a rate of (3.4 ± 0.6)×10-2 per year since the 1920’s. Over the period of the study there were 2098 fatalities in 57 fatal accidents, giving (15.1 ± 1.6) fatalities per accident and a fatal accident percentage of (45 ± 10)%. The data indicates that engine failures combined with smaller aircraft and operators should be the focus of SHM R&D. While there is a desire to further improve safety for large transport category aircraft, results indicate that smaller aircraft and operators have seen a relative increase in fatigue failure accidents, and hence are also in need of SHM systems. Engine and undercarriage systems have the greatest number of fatigue failure accidents associated with them, suggesting these should be the focus of SHM R&D.
Engineering machinery, tools, and implements, Systems engineering
Abstract The development of economic globalization makes the cooperation relationship between enterprises and suppliers closer. How to optimize suppliers becomes a research hotspot. This paper studies the selection process and selection method of quotient evaluation indicators based on the analysis of supplier data by using information visualization tools. The multi-dimensional data and analytic hierarchy process are used to evaluate the supplier’s commercial value and product information. The example of z agricultural machinery company is used to verify the effectiveness and feasibility of the new scheme.
Koki HOJO, Noritsugu UMEHARA, Takayuki TOKOROYAMA
et al.
In order to clarify the effect of Ta (tantalum) content on the friction and wear characteristics of the ta-CNx (tetrahedral amorphous carbon nitride) coating, an IBA-FAD (Ion Beam Assisted Filtered Arc Deposition method) was applied to generate hard carbonaceous coating to achieve low friction coefficient in ambient air. Following research works clarified that Ta containing carbon nitride (CNx:Ta) showed friction coefficient lower than 0.05 in ambient air, however, the coating only had around 10 GPa hardness which was assumed to be soft in industrial fields. To confirm Ta containing to the ta-CNx coating on friction and wear properties, ta-CNx:Ta coating was synthesized by IBA-FAD and an arc plasma gun which supplied different amount of Ta by using pulse discharge technique (100 or 200 pulse/min.). The hardness of ta-CNx was approximately 54 GPa, then the hardness of Ta containing ta-CNx became softer than ta-CNx such as approximately 33 GPa (ta-CNx:Ta200). After the ball-on-disk friction test between those coatings and SUJ2 disk, a specific wear rate of those coatings did not along with its hardness. From the XPS (x-ray photoelectron spectroscopy) analysis, oxygen/carbon ratio of the topmost coating surface decreased by containing Ta. Those results implied that Ta in the coating may have a possibility to prevent oxidation of carbonaceous coating. The friction coefficient of ta-CNx, ta-CNx:Ta100 and ta-CNx:Ta200 showed around 0.15 during friction test. To confirm the importance of Ta transformed layer on low friction property, friction test between ta-CNx coating and thin Ta coated SUJ2 disk was conducted. The friction coefficient of the pair became the lowest among them which value was approximately 0.08.
Mechanical engineering and machinery, Engineering machinery, tools, and implements