Browse Topic: Agricultural vehicles and equipment
Gear noise is a common challenge that all gear manufacturers must contend with. In tractors, while it is often sufficiently low in intensity to not pose a significant issue, there are instances where gear whine may occur which is noticeable. In such cases, identifying the source and effectively addressing the problem can prove to be particularly difficult. This paper addresses the root cause analysis carried out for the evaluation of factors influencing whine noise behavior of Spiral bevel gear pair (SO2) in a tractor transmission system. Numerous publications have been published on gear noise of spiral bevel gear pair, too many to list here. However, once the gearbox assembled into the transmission, such models are of limited practical value. The work explained in this paper is a typical example offers avenues in correcting the issue using more limited means.
One of agricultural tractors most important aspects is operator comfort. In addition to working long hours, tractor operators may be at risk for health problems due to vibrations and mechanical shocks. The tactile vibrations of a tractor are a major consideration when choosing one for agricultural use. This project's mandate includes a study of tractor vibration control problems. It is essential to investigate the governing system in order to determine the cause of the problem. Evaluating the vibrations transmitted via the tractor and using the design of experiments (DOE) approach to lessen vibrations on particular tactile regions were the study's goals. There are several measures currently under investigation which can be used to reduce the vibrations caused by resonance in this paper, these include reducing the natural frequency so as to be able to avoid resonance with the second order engine frequency and the damping coefficient; this will ensure the amplitude of vibration at
Any agricultural operation (such as cultivation, rotavation, ploughing, and harrowing) includes both productive and non-productive activities (like transportation, stops, and idling) in the field. Non-productive work can mislead the actual load profile, fuel consumption, and emissions. In this project, a machine learning-based methodology has been developed to differentiate between effective operations and non-productive activities, utilizing data collected in the field from data loggers installed on the machinery. Measurements were conducted on various machines across the country in all major applications to minimize the influence of any individual sample deviation and to account for variability in customer operating practices. Few critical parameters such as Engine Speed, Exhaust Gas Temperature, Actual Engine Percentage Torque, GPS Speed etc.) were selected after screening and analyzing more than 100 CAN and GPS parameters. The critical parameters were subsequently integrated with
Agrícola Cana Caiana and Grunner have developed an innovative vehicle for sugarcane harvesting, focused on reducing fuel consumption. This optimization is vital and relevant for similar operations in the largest global producers: Brazil (724 mi t - 37%), India (439 mi t - 22%), China (103 mi t - 5.3%), Thailand (92 mi t - 4.7%), Pakistan (88 mi t - 4.5%), Mexico (55 mi t - 2.8%), Colombia (35 mi t - 1.8%), Indonesia (32 mi t - 1.6%), USA (31 mi t - 1.6%), and Australia (28 mi t - 1.4%). In Brazil, São Paulo leads with 383.4 mi t (54.1% of the 23/24 harvest), followed by Minas Gerais (81.3 mi t). This innovative agricultural machinery, a result of the owners' experience, has already sold over a thousand units, proving its impact on the efficiency of the sugar-alcohol sector. The Belei family's expertise generated this solution that optimizes resources and increases harvesting productivity, with the potential to advance sustainability and profitability globally, driving agricultural
Automating harvesters started out as a necessary solution to a severe labor shortage in 1990, Trebro Manufacturing states on its website. The Billings, Montana-based manufacturer has been producing turf harvesting machines since 1999, and its automated sod harvesters and entire harvesting process feature self-driving, automated-control functions. The company's tag line, “The Future of Turf Harvesting,” refers to its position of being the first in the industry to offer automated turf harvesting products. Trebro's AutoStack 3 harvester is an automated combine for turf that steers itself while an operator monitors and performs quality control actions when needed. The harvesting process combines several automated control processes.
As countries race to expand renewable energy infrastructure, balancing clean electricity production with land use for food remains a pressing challenge — especially in Japan, where mountainous terrain limits space. A recent study led by researchers from the University of Tokyo explores a promising solution: integrating solar panels with traditional rice farming in a practice known as agrivoltaics.
Measuring the volume of harvested material behind the machine can be beneficial for various agricultural operations, such as baling, dropping, material decomposition, cultivation, and seeding. This paper aims to investigate and determine the volume of material for use in various agricultural operations. This proposed methodology can help to predict the amount of residue available in the field, assess field readiness for the next production cycle, measure residue distribution, determine hay readiness for baling, and evaluate the quantity of hay present in the field, among other applications which would benefit the customer. Efficient post-harvest residue management is essential for sustainable agriculture. This paper presents an Automated Offboard System that leverages Remote Sensing, IoT, Image Processing, and Machine Learning/Deep Learning (ML/DL) to measure the volume of harvested material in real-time. The system integrates onboard cameras and satellite imagery to analyze the field
The evolution of Autonomous off-highway vehicles (OHVs) has transformed mining, construction, and agriculture industries by significantly improving efficiency and safety. These vehicles operate in high dust, uneven terrain, and potential communication failures, where safety is challenged. To guarantee vehicle safety in such situations, a robust architecture that combines AI-driven perception, fail-safe mechanisms, and conformance to many ISO standards is required. In unstructured environments, AI-driven perception, decision-making, and fail-safe mechanisms are not fully addressed by traditional safety standards like ISO26262 (road vehicles), ISO19014 (earth-moving machinery and it is replacing withdrawn ISO 15998), ISO12100 (Safety of machinery) and ISO25119 (agriculture), ISO 18497 (safety of highly automated agricultural machinery), and ISO/CD 24882 (cybersecurity for machinery).These standards mainly concentrate on the reliability of mechanical and electric/electronic systems
Prognostics and Health Management (PHM) is framework for electrical/mechanical components in heavy machines represents a transformative approach that harnesses cutting-edge sensing technologies and analytics to predict and elevate reliability and efficiency of agricultural/construction machinery. By using advanced data collection and sophisticated analytics, PHM achieves real-time monitoring of critical performance parameters such as voltage, current, temperature, and operational cycles, along with field data mapped with GPS coordinates as well as environmental conditions. This capability allows for the early detection of anomalies and potential failures, thereby enhancing operational reliability. Data collected from the machine will be pushed to the server periodically and whenever any failure is detected advanced AI algorithms on machine and server will analyze the information and link to collected data which will be used to identify possible failures or assess the safety of the
Recent advancements in energy efficient wireless communication protocols and low powered digital sensor technologies have led to the development of wireless sensor network (WSN) applications in diverse industries. These WSNs are generally designed using Bluetooth Low Energy (BLE), ZigBee and Wi-Fi communication protocol depending on the range and reliability requirements of the application. Designing these WSN applications also depends on the following factors. First, the environment under which devices operate varies with the industries and products they are employed in. Second, the energy availability for these devices is limited so higher signal strength for transmission and retransmission reduces the lifetime of these nodes significantly and finally, the size of networks is increasing hence scheduling and routing of messages becomes critical as well. These factors make simulation for these applications essential for evaluating the performance of WSNs before physical deployment of
Large farms cultivating forage crops for the dairy and livestock sectors require high-quality, dense bales with substantial nutritional value. The storage of hay becomes essential during the colder winter months when grass growth and field conditions are unsuitable for animal grazing. Bale weight serves as a critical parameter for assessing field yields, managing inventory, and facilitating fair trade within the industry. The agricultural sector increasingly demands innovative solutions to enhance efficiency and productivity while minimizing the overhead costs associated with advanced systems. Recent weighing system solutions rely heavily on load cells mounted inside baling machines, adding extra costs, complexity and weight to the equipment. This paper addresses the need to mitigate these issues by implementing an advanced model-based weighing system that operates without the use of load cells, specifically designed for round baler machines. The weighing solution utilizes mathematical
In the electrical machines, detrimental effects resulted often due to the overheating, such as insulation material degradation, demagnetization of the magnet and increased Joule losses which result in decreased lifetime, and reduced efficiency of the motor. Hence, by effective cooling methods, it is vital to optimize the reliability and performance of the electric motors and to reduce the maintenance and operating costs. This study brings the analysis capability of CFD for the air-cooling of an Electric-Motor (E-Motor) powering on Deere Equipment's. With the aggressive focus on electrification in agriculture domain and based on industry needs of tackling rising global warming, there is an increasing need of CFD modeling to perform virtual simulations of the E-Motors to determine the viability of the designs and their performance capabilities. The thermal predictions are extremely vital as they have tremendous impact on the design, spacing and sizes of these motors.
Transmission tuning involves adjusting parameters within a vehicle's transmission control unit (TCU) or transmission control module (TCM) to optimize performance, efficiency, and driving experience. Transmission tuning is beneficial for optimizing performance, improving fuel efficiency, smoother shifting and enhancing drivability particularly when a vehicle's power output is increased or for specific driving conditions. Especially in offroad and agricultural machines, transmission tuning is vital to significantly improve vehicle performance during different operations. The process of transmission tuning is quite time consuming as multiple tuning iterations are required on the actual vehicle. A significant reduction in tuning time can be achieved using a simulation environment, which can mimic the actual vehicle dynamics and the real time vehicle behavior. In this paper, tuning during the forward and reverse motion of the tractor is described. A two-level PI control-based shift strategy
Tillage, a fundamental agricultural practice involving soil preparation for planting, has traditionally relied on mechanical implements with limited real-time data collection or adjustment capabilities. The lack of real-time data and implement statistics results in fleet managers struggling to track performance, driver behavior, and operational efficiency of the implements. Lack of data on vehicle performance can result in unexpected breakdowns and higher maintenance costs, ensuring compliance with regulations is challenging without proper data tracking, potentially leading to fines and legal issues. Bluetooth-enabled mechanical implements for tillage operations represent an emerging frontier in precision agriculture, combining traditional soil preparation techniques with modern wireless technology. Implement mounted battery powered BLE (Bluetooth Low Energy) modules operated by solar panel based rechargeable batteries to power microcontroller. When Implement is operational turns
This paper studies an important industrial controls engineering problem statement on mitigating vibrations in a mechanical boom structure for an off-highway agricultural vehicle. The work discusses the implementation of an active force control concept to efficiently dampen out vibrations in a boom. Through rigorous simulation comparison with respect to an existing PID mechanism, the efficacy of the AFC is demonstrated. A notable reduction of 60 % to 70 % in the boom vibrations was observed.
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