The relationship between machine and soil
B. Golanbari; A. Mardani; A. Hosainpour; H. Taghavifar
Abstract
Due to the numerous variables that may influence the soil-machine interaction systems, predicting the mechanical response of soil interacting with off-road traction equipment is challenging. In this study, deep neural networks (DNNs) are chosen as a potential solution for explaining the varying soil ...
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Due to the numerous variables that may influence the soil-machine interaction systems, predicting the mechanical response of soil interacting with off-road traction equipment is challenging. In this study, deep neural networks (DNNs) are chosen as a potential solution for explaining the varying soil sinkage rates because of their ability to model complex, multivariate, and dynamic systems. Plate sinkage tests were carried out using a Bevameter in a fixed-type soil bin with a 24 m length, 2 m width, and 1 m depth. Experimental tests were conducted at three sinkage rates for two plate sizes, with a soil water content of 10%. The provided empirical data on the soil pressure-sinkage relationship served as the basis for an algorithm capable of discerning the soil-machine interaction. From the iterative process, it was determined that a DNN, specifically a feed-forward back-propagation DNN with three hidden layers, is the optimal choice. The optimized DNN architecture is structured as 3-8-15-10-1, as determined by the Grey Wolf Optimization algorithm. While the Bekker equation had traditionally been employed as a widely accepted method for predicting soil pressure-sinkage behavior, it typically disregarded the influence of sinkage velocity of the soil. However, the findings revealed the significant impact of sinkage velocity on the parameters governing the soil deformation response. The trained DNN successfully incorporated the sinkage velocity into its structure and provided accurate results with an MSE value of 0.0871.
The relationship between machine and soil
H. Mahboub Yangeje; A. Mardani
Abstract
IntroductionSeedbed preparation, seeding, and transplanting are usually based on mechanical soil tillage. Tillage by cutting, mixing, overturning, and loosening the soil can modify the physical, mechanical, and biological properties of soil. These days, because of soil protection, the use of tillage ...
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IntroductionSeedbed preparation, seeding, and transplanting are usually based on mechanical soil tillage. Tillage by cutting, mixing, overturning, and loosening the soil can modify the physical, mechanical, and biological properties of soil. These days, because of soil protection, the use of tillage tools is less and less recommended, and some implements such as cultivators are preferred to primary tillage tools such as plows. Experimental study of soil-tool interaction and field measurements of the mechanics of tillage tools are usually time-consuming and costly. On the other hand, the variety of variables and uncontrolled conditions add other dimensions to the complexity of this method. Also, the experimental and analytical methods do not have a comprehensive view of stress distribution and soil deformation in the soil-tool interaction process.Materials and MethodsThe main purpose of this study is to validate the results of numerical simulations in two phases of experimental tests: in soil bin environment and in finite element computer simulations. Experimental tests were performed in the soil bin environment of the Department of Mechanical Engineering of Biosystems, Urmia University, which has a soil bin facility with dimensions of length and width of 24 and 2 m, respectively, and has clay loam soil. Before experimental tests, soil preparation was performed by using some special tillage implements (harrow, leveler, and roller) which were attached to the soil bin (Figure.1). For experimental tests, a mechanism set consisting of two cultivator blades with a width of 15cm, a length of 20cm, and at a spacing of 35cm from each other was prepared and constructed. The relevant mechanism is designed to have the ability to change the tillage depth. Data were collected at three different soil depth levels of 6, 10, and 14cm in the soil bin with three replications. Data recording was performed using a 10-channel data logger with load cell connectivity and data storage ability. Also, in this study, the Drucker-Prager model as a finite element simulation method was used to calculate the stress during the soil-tool relationship. ABAQUS 6.10.1 software was used to simulate the cultivator tine. To solve the problem, the soil parameters were defined as presented in Table 1, and then the interaction between the soil-tool model and the necessary constraints, including boundary conditions, were defined. In the next step, meshing was applied to the constructed model.Results and DiscussionIn the results section, first, the results related to the amount of traction force required for the tillage tine in the simulation were calculated and then compared with the soil bin experimental tests. The traction force of the finite element simulation results for three tillage depths of 6, 10, and 14 cm in three principal directions is shown in Figure 4. A comparison of simulation and experimental results showed that there is a good agreement between them. In comparison, the simulation error range of the three depths of 6, 10, and 14 cm has shown 7.3, 5.6, and 4.16% at a speed of 2.5 kmh-1, respectively, as the velocity studied in this research. In the next section, the results of stress distribution contours in the soil and finally the overlap of the blade effect were discussed. Figure 6 shows the status of stress contours at three depths. By increasing the depth of the tine at the three depth levels studied, the stress range is shifted from the soil surface to its depth. For this purpose, at the maximum depth studied in this study (14 cm), it shows that the stress propagation to the soil surface is less than at other depths. Also, with decreasing depth, for a depth of 6 cm, the maximum stress was on the top soil surface, in other words, more deformation was seen on the soil surface.ConclusionComparing the simulation results for predicting traction force with the results of experimental tests has led to relatively acceptable results and the maximum traction force prediction error at different depths has been about 7.3%.The distribution of stress in the soil was observed due to the tine depth. The highest intensity of stress propagation was observed at the soil surface; and the highest soil surface deformation at a depth of 6 cm. With increasing depth, both parameters of stress and soil surface deformation have decreased. According to the results of the studied blades, it is better to use these types of tillage tools only at lower depths. Also, in evaluating the overlap of the soil loosening zone in the side-by-side tines, it proves the superiority of the tine performance at lower depths.
P. Ahmadi Moghaddam; L. Eftekhari; A. Mardani; H. Khodaverdilo
Abstract
Introduction: Monitoring and management of soil quality is crucial for sustaining soil function in ecosystem. Tillage is one of the management operations that drastically affect soil physical quality. Conservation tillage methods are one of the efficient solutions in agriculture to reduce the soil erosion, ...
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Introduction: Monitoring and management of soil quality is crucial for sustaining soil function in ecosystem. Tillage is one of the management operations that drastically affect soil physical quality. Conservation tillage methods are one of the efficient solutions in agriculture to reduce the soil erosion, air pollution, energy consumption, and the costs, if there is a proper management on the crop residues. One of the serious problems in agriculture is soil erosion which is rapidly increased in the recent decades as the intensity of tillage increases. This phenomenon occurs more in sloping lands or in the fields which are lacking from crop residues and organic materials. The conservation tillage has an important role in minimizing soil erosion and developing the quality of soil. Hence, it has attracted the attention of more researchers and farmers in the recent years.
Materials and Methods: In this study, the effect of different tillage methods has been investigated on the crop residues, mechanical resistance of soil, and the stability of aggregates. This research was performed on the agricultural fields of Urmia University, located in Nazloo zone in 2012. Wheat and barley were planted in these fields, consecutively. The soil texture of these fields was loamy clay and the factorial experiments were done in a completely randomized block design. In this study, effect of three tillage systems including tillage with moldboard (conventional tillage), tillage with disk plow (reduced tillage), chisel plow (minimum tillage) and control treatment on some soil physical properties was investigated. Depth is second factor that was investigated in three levels including 0-60, 60-140, and 140-200 mm. Moreover, the effect of different percentages of crop residues on the rolling resistance of non-driving wheels was studied in a soil bin.
The contents of crop residues have been measured by using the linear transects and image processing methods. In the linear transects method, the experiments were replicated three times in each block due to increasing the accuracy and mean of datawas calculated. The tests were randomly performed in each block. Then, the number of nodes, which are located on crop residues of size 25 mm, longitudinally, was counted. So the percentage of crop residue in each block was calculated through the percentage of nodes. The experiments of rolling resistance were also performed in three levels, 10, 50, and 90% of crop residues, inside the soil bin.
Results and Discussion: Result showed that, in comparison with control treatment, tillage operation significantly decreased bulk density (p<0.01), penetration resistance (p<0.01), and aggregates stability (p<0.01), in the soil surface (0-10 cm). Also, the results showed that penetration resistance of soil was increased by depth.
The results of variance analysis in crop residue dataset showed that there were significant differences among the treatments in the terms of crop residues (P<0.05). Because of increasing the intensity of tillage and also the different performance of various tillage tools would mix crop residues with the soil and lead to reduce the crop residues. The consequences revealed that the treatments had significant differences in the terms of mechanical resistance of soil at the confidence level of 5%. The mechanical resistance of soil in three levels of depth had the most and the least contents in chisel and disk plows treatments, respectively. Because of disk plows can powder soil more than other treatments and chisel plows can only make narrow in the soil. The results of investigating the effect of crop residues on rolling resistance of wheels showed that there were not any significant differences between the treatments.
Conclusions: It can be concluded that increasing the tillage intensity would reduce the stability of aggregates. Thus, the least stability of aggregates was obtained when using moldboard plows. However, the most stability was achieved using chisel and disk plows. Finally, disk plough is recommended as an appropriate tool in this research due to the high percentage of crop residues, lower mechanical resistance, lower bulk density, and higher stability of aggregates in the soil. Generally, in short-term period, conservation tillage (reduced tillage and minimum tillage) results the improvement of soil physical quality in comparison with tillage operation. Further studies on long-term effects of various tillage systems are suggested in order to select and implement of optimum tillage method in the region.
F. Gheshlaghi; A. Mardani
Abstract
Introduction: Rolling resistance is one of the most substantial energy losses when the wheel moves on soft soil. Rolling resistance value optimization will help to improve energy efficiency. Accurate modeling of the interaction soil-tire is an important key to this optimization and has eliminated the ...
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Introduction: Rolling resistance is one of the most substantial energy losses when the wheel moves on soft soil. Rolling resistance value optimization will help to improve energy efficiency. Accurate modeling of the interaction soil-tire is an important key to this optimization and has eliminated the need for costly field tests and has reduced the time required to test.
Rolling resistance will change because of the tire and wheel motion parameters and characteristics of the ground surface. Some tire design parameters are more important such as the tire diameter, width, tire aspect ratio, lugs form, inflation pressure and mechanical properties of tire structure. On the other hand, the soil or ground surface characteristics include soil type; moisture content and bulk density have an important role in this phenomenon. In addition, the vertical load and the wheel motion parameters such as velocity and tire slip are the other factors which impact on tire rolling resistance. According to same studies about the rolling resistance of the wheel, the wheel is significantly affected by the dynamic load.
Tire inflation pressure impacted on rolling resistance of tires that were moving on hard surfaces. Studies showed that the rolling resistance of tires with low inflation pressure (less than 100 kPa) was too high.
According to Zoz and Griss researches, increasing the tire pressure increases rolling resistance on soft soil but reduces the rolling resistance of on-road tires and tire-hard surface interaction. Based on these reports, the effect of velocity on tire rolling resistance for tractors and vehicles with low velocity (less than 5 meters per second) is usually insignificant.
According to Self and Summers studies, rolling resistance of the wheel is dramatically affected by dynamic load on the wheel.
Artificial Neural Network is one of the best computational methods capable of complex regression estimation which is an advantage of this method compared with the analytical and statistical methods.
It is expected that the neural network can more accurately predict the rolling resistance. In this study, the neural network for experimental data was trained and the relationship among some parameters of velocity, dynamic load and tire pressure and rolling resistance were evaluated.
Materials and Methods: The soil bin and single wheel tester of Biosystem Engineering Mechanics Department of Urmia University was used in this study. This soil bin has 24 m length, 2 m width and 1 m depth including a
single-wheel tester and the carrier.
Tester consists of four horizontal arms and a vertical arm to vertical load. The S-shaped load cells were employed in horizontal arms with a load capacity of 200 kg and another 500 kg in the vertical arm was embedded. The tire used in this study was a general pneumatic tire (Good year 9.5L-14, 6 ply)
In this study, artificial neural networks were used for optimizing the rolling resistance by 35 neurons, 6 inputs and 1 output choices. Comparison of neural network models according to the mean square error and correlation coefficient was used. In addition, 60% of the data on training, 20% on test and finally 20% of the credits was allocated to the validation and Output parameter of the neural network model has determined the tire rolling resistance. Finally, this study predicts the effects of changing parameters of tire pressure, vertical load and velocity on rolling resistance using a trained neural network.
Results and Discussion: Based on obtained error of Levenberg- Marquardt algorithm, neural network with 35 neurons in the hidden layer with sigmoid tangent function and one neuron in the output layer with linear actuator function were selected. The regression coefficient of tested network is 0.92 which seems acceptable, considering the complexity of the studied process. Some of the input parameters to the network are speed, pressure and vertical load which their relationship with the rolling resistance is discussed.
The results indicated that in general trend of changes, the velocity is not affected by rolling resistance. Rolling resistance increases when tire pressure decreases. This is due to energy consumption for creating deflection on the body of the tire at the lower levels of tire inflation pressure. Another variable parameter is the vertical load on the wheel and its logical relation with rolling resistance using neural network. The results showed that increasing the vertical load increases the rolling resistance.
Conclusions: The major purpose of this study was the feasibility of using learning algorithms for interaction between wheel and soil. The parameters of the wheel when clashes with soil are not stochastic and in spite of their complexity follow a specific model, certainly. Artificial neural network trained with a correlation coefficient of 0.92 relatively had a good performance in education, testing and validation parts. To validate the network results, the impact of some factors on the extraction process such as velocity, load and inflation pressure was simulated. The main objective of this article is comparing the network performance with basic principles and other scientific reports.
In this regard, the predictions by trained neural network indicated that rolling resistance is independent of the velocity of the wheel. On the other hand, rolling resistance decreases by increasing tire inflation pressure which is a general trend similar to other studies and reports in the same mechanical condition of the soil tested. Rolling resistance changes are directly proportional to load vertical variations on the wheel in terms of quantity and quality, similar to experimental models such as Wismer and Luth.
H. Taghavifar; A. Mardani
Abstract
Introduction: Tire tractive parameters of the driving wheel are of the most substantial factors for the evaluation of the performance of agricultural tractors. Great tractive efficiency has called the attention of vehicle designers to attain economic efficiency owing to the minimization of fuel consumption. ...
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Introduction: Tire tractive parameters of the driving wheel are of the most substantial factors for the evaluation of the performance of agricultural tractors. Great tractive efficiency has called the attention of vehicle designers to attain economic efficiency owing to the minimization of fuel consumption. At terrain-tire interface, some soil physical-mechanical changes occur that lead to unwanted soil compaction. Of the influential parameters for the creation of soil compaction is the soil stresses formed owing to the wheeled vehicle trafficking. While the increase of tractive efficiency is desired, minimization of soil stresses should also be considered with the same importance to make a trade-off between the aforementioned parameters. There are numerous studies documented in the literature that deal with the measurement of soil stress/strain data due to the wheeled vehicle trafficking and also those works that address the correlation between the soil stress and soil compaction. It is recognized that in order to reduce soil compaction both at topsoil and subsoil levels, the soil stress at the soil-tire interface should be reduced. There are various parameters that affect the tractive efficiency and the soil stress creation such as wheel load, slip, tire inflation pressure, velocity, etc. On the other hand, the wheel is subjected to the torques and forces exerted to the vehicle and the vehicle dynamics are significantly affected by the soil-wheel interactions. Survey of the literature shows that numerous studies have focused on the evaluation of tractive efficiency both in field test and controlled conditions in laboratories with the intention of increasing tractive efficiency. The studies dedicated to the soil mechanical strength are more engaged with the approaches to minimize the soil stress propagation. The present study considers both factors and considers the most influential tire parameters such as wheel, velocity and slip to assess the relationship between traction and the soil vertical stress in a soil profile using a single-wheel tester and a soil bin facility.
Materials and methods: The soil bin in Department of Mechanical Engineering of Urmia University was used in this study. This soil bin is featured 24 m in length, 2 m in width and 1 m in depth including a single-wheel tester and the carriage. A chain system was used for the power transmission from the electromotor to the carriage. The carriage was able to move alongside the soil bin through four ball bearings which also hold the weight of the carriage. The utilized tire in the study was a 220/65R21 driving wheel. One load cell was situated vertically to measure the wheel load and four S-shaped load cells were horizontally situated between the single-wheel tester and the carriage to measure the traction force. An electric motor was used to empower the carriage while another electric motor was used to empower the wheel tester. The difference between the linear velocities of the carriage and the wheel-tester provided the desired levels of slip. A housing including four load cells situated at the distances of 12.5 cm was used to measure the soil vertical stress transmission in the soil profile. The system was buried at the desired depth in the path of wheel traversal. Under the aforesaid treatments, the experiments were undertaken with the purpose of simultaneous measurement of soil stress propagation and traction force and finally the correlation between these parameters.
Results and discussion: The results were analyzed using the statistical analysis at 1% significance level. The results showed that an increase in traction force leads to an increment of vertical soil stress. It was also recognized that the reduction in the velocity leads to the increase in soil stress which is due to the greater contact duration between the soil and the tire. Also, an increase in wheel load results in an increase of soil stress which has a linear correlation with the traction force. Furthermore, it was deduced that the increase in depth leads to a reduction of soil vertical stresses.
Conclusions: The present study is aimed at investigating the effect of net traction force on the imposed vertical stress under the 220/65R21 driving wheel. Hence, velocity at three levels (i.e. 0.8, 1, 1.2 m s-1), wheel load at three levels (i.e. 2, 3, and 4 kN) and slippage at three levels (i.e. 8, 12, and 15%) were considered to obtain traction force and soil vertical stress at three depths of 0.1, 0.15 and 0.2 m. Experiments were carried out in the complete randomized block design with three replicates on clay loam soil at 12% moisture content. The vertical stress was measured using a manufactured soil stress transducer where the net traction was measured using four horizontally installed load cells between the tester rig and the carriage. A correlation was developed between soil stress and traction force. The results revealed that vertical stress increases with respect to increase of wheel load and slippage, whereas vertical stress decreases by increase in depth and velocity. Additionally, it was found that wheel load and slippage bring about increased traction force while velocity has no significant effect on traction force at 1% significance level. Finally, it was deduced that an increase of traction force results in an increase of vertical stress transmission.
N. Dibagar; A. Mardani; A. Modarres Motlagh; H. Jafari
Abstract
Introduction: Encountering soil from the viewpoint of management and product manufacturing has always been considered important, and an attempt is always made hat the tools and contrasting methods of soil be designed in such a way that itself prevents, as much as possible, the destructive consequences ...
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Introduction: Encountering soil from the viewpoint of management and product manufacturing has always been considered important, and an attempt is always made hat the tools and contrasting methods of soil be designed in such a way that itself prevents, as much as possible, the destructive consequences or energy waste that include economical or environmental limitations. Enhancing the soil encountering methods, quality reformation, and its related equipment, requires performing reliable tests in actual soil conditions. Considering the complexity and variety of variables in soil and machine contrast, this is a hard task. Hence, the numeral simulations are the key of all optimizations that illustrate efficient models by removing the costly farm tests and reducing research time. Tire is one of the main factors engaged with soil, and it is one of those tools that are discussable in both farms, and software environments. Despite the complexities in soil behavior, and tire geometry, modeling, tire movement on the soil has been the researchers’ objective from the past.
Materials and methods: A non-linear finite element (FE) model of the interaction of a non-driving tire with soil surface was developed to investigate the influence of the forward speed, tire inflation pressure and vertical load on rolling resistance using ABAQUS/Explicit code. In this research numerical and experimental tests were done under different conditions in order to estimate tire rolling resistance. In numerical tests, the soil part was simulated as a one-layer viscous-elastic material with a Drucker-Prager model by considering realistic soil properties. These properties included elastic and plastic properties which were obtained in the soil laboratory using relevant tests. The soil samples were prepared from the soil which was inside the soil bin. The same soil was utilized in experimental tests. Finite strain hyper elasticity model is developed to model nearly incompressible rubber materials for the tire. Tire model consisted of three components: tread, rubber and ring. Using a soil bin and one wheel tester with their related equipment, experimental tests were carried out in the workstation of mechanics of bio system engineering department of the Urmia University. This system includes various sections such as soil storage in dimensions of 22×2×1 meter, tools carrier or tracker, soil processing equipment, dynamic system, evaluation tools and controlling systems. In order to launch the collection and supply required power for wheel carrier, an industrial three phase electromotor with 22 kW (30 hp) was used. Both numerical and experimental tests were done at three levels of wheel dynamic load (1, 2, 3, 4 and 5) kN, tire inflation pressure (100, 200 and 300) kPa and four levels of speed (0.25, 0.45, 0.65, 0.9 and 1.15) m s-1 to obtain the rolling resistance of the tire.
Results and discussion: In order to evaluate the performance of final non driving tire-soil model to estimate the rolling resistance, numerical results were compared with preliminary experimental data obtained from the soil-bin tests. The comparison showed reasonably good agreement between the computed and measured general pattern of the rolling resistance at the tire-soil interface under different speeds, vertical loads and inflation pressures. In both tests, a specified relation was not seen between tire velocity and its rolling resistance, as it was not seen in empirical models such as Wismar and Luce. Correlation coefficient between experimental and numerical data, in the minimum and maximum value of tire inflation pressure was computed to be 0.06 and 0.016 percent, respectively. The amount of tire rolling resistance significantly increased with increase of tire vertical load. Correlation coefficient between experimental and numerical data, in the minimum and maximum vertical loads was computed to be 80 and 87 percent, respectively. Gent and Walter obtained the same results. The tire inflation pressure and rolling resistance variables had inverse relation to each other in both numerical and experimental tests. Correlation coefficient between experimental and numerical data was computed to be 97 and 73 percent in the minimum and maximum tire inflation pressure, respectively. The gradient of changes in tire inflation pressure - rolling resistance diagram was less in numerical tests. This was because of differences between real properties and the properties entered into the software.
Conclusions: To conclude, in this investigation a new 3D tire-soil model was simulated which has specific features. The experimental results showed that the numerical data of estimation of non-driven tire rolling resistance were reliable. In both tests, the effect of changes in tire forward speed on rolling resistance was not significant.The amount of the tire rolling resistance significantly increased with increasing tire vertical load. Changes in tire inflation pressure and rolling resistance had an inverse relation with each other in both numerical and experimental tests. The slope of rolling resistance - inflation pressure diagram in numerical tests was less than the same diagram in the experimental tests.
H. Mohammadzadeh; A. Mardani; A. Modarres Motlagh
Abstract
The tire-mechanics models have been developed for the study of wheel movement on the road or soil surface while these models are unlikely to describe the motion of wheels on uneven surfaces. Due to dynamical complexity of this phenomena and the importance of this subject for farm conditions and the wheel ...
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The tire-mechanics models have been developed for the study of wheel movement on the road or soil surface while these models are unlikely to describe the motion of wheels on uneven surfaces. Due to dynamical complexity of this phenomena and the importance of this subject for farm conditions and the wheel carrier devices, the present research aimed to investigate the effects of several parameters on the wheel passing the obstacle. The experiments were carried out using single wheel tester in soil bin condition. The results indicated a relatively linear relationship between the impact force applied on tire and forward speed of wheel and also the height of rectangular obstacle. The effect of inflation pressure was inversed in the range of complete formed tire’s body on impact force and in low levels of tire inflation pressure; tire’s body damps the maximum impact forces. The medium levels of pressure (about 150-200 kPa) resulted in less horizontal force that applied on the wheel for different levels of forward speed and obstacle’s height. Tractive force for passing obstacle was increased by raising forward speed and the obstacle’s height.
