The vibration tests were carried out using a 162kW wheeled tractor to evaluate the Whole-body vibration while the tractor working in the field, driving on concrete road and in-parking state. The tractor ride comfort was analyzed using the joint weighted acceleration of seat surface vibration from 1 to 80 Hz as the evaluation indicator, according to ISO2631 standard suggestions. During field work, the combined acceleration of the driver's whole body vibration is 1.39, and the subjective feeling is extremely uncomfortable. Based on the multi-body dynamics analysis software, a tractor dynamics simulation model including the seat, cab mounts, chassis, tyre and road coupling system was established. By changing the model parameters, the influence of the tractor driver's whole body vibration under the condition of the artificial test track was further analyzed. The analysis shows that the factors that have a greater impact on the driver's whole body vibration when driving on the artificial runway are the vertical stiffness of the tyre, the vertical stiffness of the seat suspension, and the damping coefficient of the tyre.

Obstacle avoidance operations of tractors can cause parts of land to be unavailable for planting crops, which represents a reduction in land utilization. However, land utilization is significant to the increase in agricultural productivity. Traditional obstacle avoidance path planning methods mostly focus on automatic tractor navigation with small errors, ignoring the decrease in land utilization due to obstacle avoidance operations. To address the problem, this paper proposed an obstacle avoidance path planning method based on the Genetic Algorithm (GA) and Bezier curve. In this paper, a third-order Bezier curve was used to plot the obstacle avoidance path, and the range of control points for the third-order Bezier curve was determined according to the global path and the location of the obstacle. To target the navigation error and land utilization problems, GA was used to search for the optimal point from the selection range of the control point under multiple constraints for automatic tractor navigation such as the obstacle collision avoidance, the minimum turning radius, and the maximum turning angle. Finally, the optimal obstacle avoidance path was determined based on the selected control points to minimize the navigation error and maximize land utilization. The algorithm proposed in this paper was compared with existing methods and the results showed that it has generally favorable performance on obstacle avoidance path planning.

To overcome the limitations of the hybrid tractor bumping tests, which include extended cycle times, high costs, and impracticality for single-part reliability verification, this study focuses on the exhaust system mounting bracket of a hybrid tractor. A novel approach that combines multi-objective particle swarm optimization (MOPSO) and wavelet decomposition algorithms was employed to enhance the reconstruction of shock vibration signals. This approach aims to enable the efficient acquisition of input signals for subsequent shaker table testing. The methodology involves a systematic evaluation of the spectral correlation between the original signal and the reconstructed signal at the stent’s response position, along with signal compression time. These parameters collectively constitute the objective function. The multi-objective particle swarm optimization algorithm is then deployed to explore a range of crucial parameters, including wavelet basic functions, the number of wavelet decomposition layers, and the selection of wavelet components. This exhaustive exploration identifies an optimized signal reconstruction method that accurately represents shock vibration loads. Upon rigorous screening based on our defined objectives, the optimal solution vector was determined, which includes the utilization of the dB10 wavelet basic function, employing a 12-layer wavelet decomposition, and selecting wavelet components a12 and d3~d11. This specific configuration enables the retention of 95% of the damage coefficients while significantly compressing the test time to just 46% of the original signal duration. The implications of our findings are substantial as the reconstructed signal obtained through our optimized approach can be readily applied to shaker excitation. This innovation results in a notable reduction in test cycle time and associated costs, making it particularly valuable for engineering applications, especially in tractor design and testing.

Long-term exposure to high levels of vibration and noise can have detrimental effects on the health of tractor drivers. This study aimed to evaluate the subjective comfort experienced by drivers operating large-horsepower tractors. A total of 10 tractors sourced from 5 different manufacturers were subjected to testing. The assessment encompassed three operational conditions, namely, idle, maximum torque, and rated power. Objective measurements, including A-weighted sound pressure level (A-SPL), loudness, sharpness, roughness, articulation index (AI), hand vibration, and seat vibration, were collected. Additionally, subjective comfort evaluations were carried out using a paired comparison test. To predict the subjective comfort of tractor drivers, a novel prediction model was developed by employing a simulated annealing (SA) algorithm to optimize a backpropagation neural network (BPNN). The model successfully achieved accurate predictions of subjective comfort, yielding a maximum prediction error of 4.4%. The study findings revealed that vibration had a more pronounced impact on driver comfort in environments with lower-amplitude noise, whereas high-decibel noise exerted a masking effect on vibration-induced discomfort. In conclusion, the SA-BPNN model, utilizing A-SPL, loudness, sharpness, roughness, AI, hand vibration, and seat vibration as objective parameters, effectively predicted the subjective comfort of tractor drivers. This discovery holds significant implications for tractor manufacturers, who can employ the model to optimize the design of tractor cabs and enhance driver comfort.

A four-column cab is taken as the research object.Firstly, a body-in-white finite element model of the cab is established, and the precision of body-in-white model is verified by modal analysis and comparison with experimental results.The simulation model of cab with interior was established on the basis of body-in-white model, and the vibration acceleration response of the left foot floor and seat bracket of the tractor was obtained through modal based forced response analysis, which was compared with the test results.Finally, the floor of the cab was optimized, and the maximum vertical vibration acceleration of the seat bracket decreased by 47.1%,the total vertical vibration acceleration of the seat bracket decreased by 12.3%.

The noise level in a tractor cab directly affects the driving experience. Reducing the noise level in a tractor cab is an important means of improving the image of the product brand and protecting the physical and psychological health of employees. To reduce medium- and low-frequency noise in a tractor cab, the influence of the installation of coupled resonance acoustic materials (CRAMs) on tractor driver ear noise is investigated in this study. Theories for sound absorption by perforated plates and sound insulation by thin membranes are used to design a CRAM composed of a perforated plate, elastic membrane and additional mass. The acoustic impedance and sound absorption coefficient of the CRAM are obtained by an equivalent circuit method, and the resonance frequency of a local resonance system composed of an elastic membrane and additional mass is calculated. The effects of different hole diameters and porosities of the perforated plate on the CRAM sound absorption coefficient are compared. The results of tests performed using impedance tubes over a wide frequency range of 50 Hz~1600 Hz show that the maximum sound absorption coefficient is 0.99 and several transmission loss peaks of 15 dB~26 dB. Measurement of the acoustic response of the CRAM installed on the cab front panel shows that an ear noise reduction of approximately 1.3 dB(A) for a tractor working speed of 2200 rpm. The results show that the designed CRAM has a relatively good effect on reducing driver ear noise over a wide frequency band and is a feasible scheme for noise reduction in a tractor cab.

Formerly, torsional vibration of crankshaft in off-highway diesel engine (agricultural machinery) were given little attention at their developmental stages, however with increasing agricultural activities, numerous torsional vibration problems have been noted to occur in agricultural machinery, especially in their diesel engines. This results in engine vibration, crankshaft failure and undesirable engine noise. In this paper, a six-cylinder four-stroke inline diesel engine’s crankshaft model was developed using AVL Excite Designer. After experimentally validating the model, it was used to numerically determine the torsional vibrations of a crankshaft. For the reduction of torsional vibration, two methods of crankshaft improvements were proposed based on simulation results. The first method, is to decrease the inertia of the crank pulley while the second, involves the replacement of the crank pulley with a torsional vibration damper. To ensure minimal engine alterations and cost effectiveness, the second improvement method was adopted for improving torsional vibration. Afterwards, engine radiating noise and surface vibration measurements were conducted to ensure that the required limits were achieved by the improved engine. The simulation, experiment and the improvement process of the crankshaft torsional vibration are documented further in this research. These improvements have applicable values in the developmental or quality enhancement stage of diesel engines used in agricultural machinery.