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Control method of seedbed compactness based on fragment soil compaction dynamic characteristics
Soil & Tillage Research 198 (2020) 104551
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Control method of seedbed compactness based on fragment soil compaction dynamic characteristics
T
Zhan Zhaoa,*, Hongchang Lib, Jinkai Liua, Simon X. Yangc,* a
Institute of Agriculture Engineering, Jiangsu University, Zhenjiang 212013, China College of Vehicle Engineering, Changzhou Vocational Institute of Mechatronic Technology, Changzhou 213164, China c School of Engineering, University of Guelph, Guelph N1G 2W1, Canada b
A R T I C LE I N FO
A B S T R A C T
Keywords: Soil preparation machine Seedbed compaction Discrete element method Dynamic analysis Compaction depth control
Seedbed preparation is the initial and fundamental operation for achieving an optimal soil condition for vegetable production. Seedbed compaction is an important technical criterion for evaluating the operating performance of a soil preparation machine. To analyze the dynamic properties of fragment soil compaction after rotary tillage, a discrete element method (DEM) was used to simulate the compaction process, and the Edinburgh elasto-plastic adhesion model (EEPA) was selected to calculate the interaction force between compact roller and fragment soil. The relationship models between compact force and soil compactness at different machine forward speeds and compaction depths were established. Then, it was proposed to control the soil compaction by adjusting the compaction depth, and the adjustment range of compaction depth was calculated using the fuzzy algorithm which taking the compact force as input parameter. In order to reduce the effect of compact force fluctuations and improve the stability of control system, the adjustment strategy was formulated according to the statistical analysis of fuzzy calculation outputs over a period of time. Field experiment results indicated that the seedbed compaction could be automatically adjusted in real time according to the set compact force. Compared with the traditional method of using a constant compaction depth, the stability of soil compaction was significantly improved. The proposed method is useful for improving the working performance and adaptability of soil preparation machines in different field environments.
1. Introduction In the mechanized vegetable production, seedbed preparation is fundamental for achieving an optimal growing soil environment for crops (Atkinson et al., 2007; Lovarelli and Bacenetti, 2017). It mainly includes operating procedures of ridging through a disc harrow, soil fragmentation performed using a rotary harrow and surface compaction performed using a compact implement. Soil compaction is an important criterion for evaluating the growing condition since it affects soil-seed contact, which is crucial for seed germination. Soil compaction is generally regarded as being negative within agriculture, since it can lead to reduced water infiltration, soil aeration and gas exchange, increased surface runoff and erosion, ultimately affecting plant growth and the crop yields (Batey, 2009; McPhee and Aird, 2013; Rodgers et al., 2018). However, a large number of studies and experiments also showed that some compaction can have positive effects such as increasing soil unsaturated hydraulic conductivity and thereby the capillary flow of water and nutrients to seeds and plants
⁎
(Gemtos et al., 2000; Arvidsson et al., 2012). Different crops and cultivation methods have different requirements for soil compaction. Therefore, it is needed to control the compactness within a reasonable range during the seedbed preparation process. Currently, this objective is achieved mainly by manually adjusting the position of the compact implement according to the variation of soil compaction after preparation, which is obviously lagging. Assessment of agricultural soil compaction is conventionally performed by using invasive vertical sensors such as penetrometers and shear vanes (Donohue et al., 2013; Alaoui and Diserens, 2018). With advances in precision agriculture, soil compaction detection method has been under focused investigation by many researchers, and many new detection sensing systems were developed (Grift et al., 2005; Petersen et al., 2005; Moallemi-Oreh et al., 2010; Hemmat et al., 2014). Most of these approaches are offline measurement methods, which can provide discrete or continuous soil compaction information after soil preparation operation. After rotary tillage operations, the fragmented soil loosely covers
Corresponding authors. E-mail addresses: [email protected] (Z. Zhao), [email protected] (S.X. Yang).
https://doi.org/10.1016/j.still.2019.104551 Received 29 August 2019; Received in revised form 18 December 2019; Accepted 18 December 2019 Available online 07 January 2020 0167-1987/ © 2019 Elsevier B.V. All rights reserved.
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1100 mm.
the surface of the seedbed. By applying a certain force to the fragmented soil through a compact implement, a seedbed soil structure with certain compactness can be formed. The compactness mainly depends on the interaction process between the compact implement and the fragment soil. This is also pivotal for design, optimization, automation and control of soil preparation machines. The existing research mainly focuses on two aspects: one is the analysis of forces developed at the interface of the soil and the operating tools such as draught, rotary harrow and subsoiling shovel; another is the displacement or deformation of soil particles also known as soil disturbance (Conte et al., 2011; Ani et al., 2018). In recent years, the discrete element method (DEM) has been recognized as an effective way to simulate soil–tool interactions (Mak et al., 2012; Ucgul et al., 2015; Wang et al., 2019). Contact models significantly affect the accuracy of DEM simulations, and various models have been introduced into compaction studies of different soils (Defossez and Richard, 2002; Keller and Lamandé, 2010). Vegetable cultivation soils are usually cohesive and compressible. In order to accurately describe the mechanical properties of this type of soil, a new elasto-plastic adhesion (EEPA) model was presented (Thakur et al., 2016; Janda and Ooi, 2016). The objective of this work was to evaluate the compaction mechanical properties of fragment soil after rotary tillage using DEM simulations. The EEPA model was applied to analyze the relationship between the compact force and the soil compactness at different forward speeds and compaction depths. Through the combination of fuzzy calculation and statistical analysis, a real-time control method based on compact force feedback was proposed to improve the uniformity of soil compaction during the soil preparation process. Finally, seedbed preparation tests were carried out to verify the reliability of the control system, and the operating performance was compared with the traditional method of using a constant compaction depth.
2.2. DEM simulation of soil compact process In the DEM simulations, spherical particles are the most widely used model for describing soil particles (Chen et al., 2013; Bravo et al., 2014; Obermayr et al., 2014). However, the shape of fragment soil aggregates after rotary tillage is usually varied and irregular. Here, the clusteredparticle approach was used to represent the soil particle, in which small sphere body elements are allowed to overlap to form a complex shape (Markauskas et al., 2010; Zhao et al., 2018). In order to describe the irregularity of fragment soil particle shape, five typical models with different shapes were established (Wang et al., 2017; Ma et al., 2019). The primary diameter of sphere element was 10 mm, and the size ratio varied from 60 to 150 % (Fig.2). The normal force-overlap relationship for the EEPA contact model is shown in Fig.3. When two discrete soil particles are pressed together, they undergo elastic and plastic deformations, and the pull-off (adhesive) force increases with an increase of the plastic contact area. The total contact normal force fn is the sum of the hysteretic spring force fhys and the normal damping force fnd, which can be written as
fn = fhys + fnd
(1)
The fhys and fnd can be calculated by n ifk2 (δ n − δpn ) ≥ k1 δ ⎧ f0 + k1 δ ⎪ = f0 + k2 (δ n − δpn ) ifk1 δ n > k2 (δ n − δpn ) > − kadh δ x ⎨ ⎪ f0 − kadh δ x if−kadh δ x ≥ k2 (δ n − δpn ) ⎩
fhys
fnd = −2
5 β Kn m* vnrel 6
(2)
(3)
where δ is the contact overlap, f0 is the constant pull-off force, k1 is the initial loading stiffness, k2 is the unloading/reloading stiffness, kadh is the adhesive stiffness (kadh = fmin / δminx), n is the power value for k1 and k2, x is the power value for kadh, vnrel is the normal component of the relative velocity. The damping coefficient β and the Hertzian normal stiffness Kn are given by
2. Material and methods 2.1. Soil preparation machine The structure of the soil preparation machine is shown in Fig.1. The rotary tillage and ridge components were mounted at the front of the machine. The compact components mainly consisted of a compact roller, two guiding shafts, two force sensors, a horizontal beam and an electric cylinder. The roller was connected to the beam through two guiding shafts, and two force sensors were installed between the connections to measure the compact force acting on roller. The fragment soil produced by rotary tillage was compacted by the roller to form a flat ridge surface. Driven by an electric cylinder, the roller moved in the vertical direction to change the compaction depth. The driving force of the electric cylinder was 5000 N, and the movement speed of the piston rod was 5 mm/s. In order to improve the flatness and shape of the ridge, the roller was driven by a hydraulic motor to rotate during the working process. The diameter and length of the roller were 230 mm and
β=
ln e ln2 e + π 2
(4) (5)
Kn = 2E * R*δ
where m*, E* and R* are the equivalent mass, Young’s modulus and radius, respectively. The tangential force fτ is the sum of tangential spring force fτs and tangential damping force fτd, as given by
fτ = fτ s + fτ d
(6)
The tangential spring force fτs is expressed in incremental terms
fτ s = fτ s(t-1) + Δfτ s
(7)
where fτs(t-1) is the tangential spring force at the previous time step, Δfτs is the increment of tangential force and is given by
Δfτ s = −k τ δτ
(8)
where kτ is the tangential stiffness, and δτ is the increment of tangential displacement. The tangential damping force fτd is calculated by
fτ d = −2
5 β K τ m* vτrel 6
(9)
K τ = 8G * R*δ
(10) vτrel
is the tangential comwhere G* is the equivalent shear modulus, ponent of the relative velocity. The total tangential force fτ needs to be coupled to the normal force
Fig. 1. Structure of soil preparation machine. 2
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Fig. 2. Typical fragment soil particle models.
Fig. 3. Force-overlap relationship for the EEPA contact model.
Fig. 4. Snapshot of DEM simulation: (a) soil compaction; (b) compactness measurement.
through Coulomb’s law, so that the limiting tangential friction force fcτ is calculated by
fcτ ≤ μ |fhys + kadh δ n − f0 |
acting on 19 probes was also recorded with a sampling frequency of 100 Hz.
(11)
2.3. Fuzzy calculation method
where μ is the friction coefficient. Detailed information about the governing equations, contact models and interaction coefficient is given by Thakur et al. (2016) and Wang et al. (2017). Simulations of the compact force variations during the fragment soil compact process were performed using the DEM software (EDEM® 2018, EDEM, Edinburgh, UK), in which the EEPA contact model was implemented through the API. Under the action of gravity, five typical fragment soil particle models were loosely and evenly accumulated to form an initial seedbed. The length, width and height of the seedbed were 3000 mm, 1700 mm and 400 mm, respectively. The material of roller was Steel #45. The density was 7850 kg/m3, the elastic modulus was 2 × 1011 Pa, and the poisson's ratio was 0.3. According to the general mechanical properties of cohesive soil, the density, shear modulus and poisson's ratio were set to 2600 kg/m3, 1 × 106 Pa and 0.28. The friction coefficient, rolling friction coefficient and restitution coefficient of solid particle were 0.33, 0.15 and 0.55, respectively. The restitution coefficient between the roller and the soil particle was 0.45 (Ucgul et al., 2015; Zheng et al., 2016). Other coefficients used in DEM simulation were f0 = 0, Δγ = 30, λp = 0.7, n = 1.5, x = 5, ξtm = 0.2857 (Janda and Jin, 2016; Wang et al., 2017; Ma et al., 2019). The compaction depth h was defined as the distance between the initial soil surface and the bottom of the roller. The constant rotation speed of the roller ω was 175 rpm during the simulations, and the variation of the vertical force Fn acting on roller was recorded with a sampling frequency of 100 Hz. With the forward speed v of 1.0 m/s, compaction depth h of 40 mm, a graphic of the DEM simulation of the seedbed compaction is shown in Fig. 4 (a). In order to obtain the soil compactness, after completion of the compaction operation, 19 measuring probes were vertically and synchronously penetrated into the seedbed. The probes were evenly arranged on the soil surface, which could effectively reduce the random error caused by a single probe measurement and obtain a stable compactness value. The diameter of the probe was 12 mm, and the structural parameters were the same as those of the TJSD-750 Soil Compaction Meter. The penetration speed was 50 mm/s and the penetration depth was 100 mm. The variation of average vertical force Fp
Due to the compaction depth is the key factor in determining the soil compaction, the vertical position of the roller and the compact force were selected as the control variable and monitoring variable respectively. The soil compaction control is a strong non-linear system, which is affected by many factors, such as forward speed, changing operating conditions and random interferences. It is difficult to establish an accurate control model. But, there is a basic control law: when the soil compaction is insufficient, the compaction depth needs to be increased; when the soil compaction is excessive, the compaction depth needs to be reduced. Therefore, the fuzzy theory was used to solve this control problem. A two-dimensional fuzzy controller was designed, which takes compact force error and its change rate as inputs and the compaction depth (movement direction and time period of piston rod) as output. The force error e(t) between the setpoint F0 and the monitoring value F (t), and the corresponding error change rate ec(t) can be calculated as
⎧ e (t ) = F (t ) − F0 ⎨ ⎩ ec (t ) = e (t ) − e (t − 1)
(12)
According to the demand, error e, error change rate ec and compaction depth h were divided into seven fuzzy subsets: PB (far greater than 0), PM (greater than 0), PS (slightly greater than 0), ZO (equal to 0), NS (slightly less than 0), NM (less than 0) and NB (far less than 0). Use triangular-shaped membership function (Fig. 5), and get the solutions of fuzzy by the method of weighted average. According to the experience accumulated, a fuzzy calculation rule table including 49 fuzzy rules was developed, which is expressed by the fuzzy conditional statement “if e and ec then h” (Table 1). 2.4. Control method based on statistical analysis Under practical conditions, it is inevitable that there is some random interference. This may cause the measured e and ec to fluctuate within a large range instantaneously. Therefore, only using fuzzy 3
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Fig. 5. The membership function of e, ec and h. Table 1 Fuzzy control rules. e, ec, h
NB
NM
NS
ZO
PS
PM
PB
NB NM NS ZO PS PM PB
PB PB PM PM PS PS ZO
PB PM PM PM PS ZO ZO
PM PM PM ZO ZO NM NM
PM PM PS ZO NS NM NM
PS PS ZO ZO NS NM NM
ZO ZO NS NM NM NM NB
ZO NS NS NM NM NB NB
Fig. 6. Variation of Fn measured using DEM simulation.
where the fitting coefficients a and b are obtained using nonlinear regression analysis under a certain forward speed v. With v of 0.6, 1.0 and 1.4 m/s, the obtained coefficient a was 4000, 6025 and 8260, and the coefficient b was 0.009026, 0.007269 and 0.006015, respectively (Fig.7(a)). The coefficients of determination R2 established from the regression equations were greater than 0.9810. Increasing the forward speed would linearly increase the work efficiency (area of compacted seedbed per unit time). So, when v was increased from 0.4 to 1.4 m/s, the mean value of compact force Fn0 approximately increased linearly (Fig.7(b)). When the seedbed was compacted with the forward speed v of 1.0 m/s and compaction depth h of 40 mm, the variation of average vertical force acting on one probe Fp during the soil compactness measurement is shown in Fig.8. The Fp gradually increased as the probes penetrated into the seedbed. Since the seedbed was an anisotropic discontinuous medium, there were some fluctuations in the increasing process of Fp. Due to the movement time of probe was 2 s, the peak force Fp max appeared at time of 2 s. Then, due to the elasto-plastic and adhesive interaction between the soil particles and the probes, the Fp began to reduce gradually rather than quickly reduced to 0. The soil compactness SC is calculated by
calculation method may cause frequent adjustment and misadjustment of the piston rod. To avoid this problem, it was proposed to optimize the control system by integrating statistical analysis. The outputs of fuzzy control H = {PB, PM, PS, ZO, NS, NM, NB} were quantized into the weight coefficients C = {c3, c2, c1, 0, −c1, −c2, −c3}. Assume that the statistical time period was ΔT, and the obtained continuous weight coefficient sequence were {Ci, Ci+1, …, CM}. M is the number of statistical samples in one period ΔT, that is, the ratio of ΔT to the sampling period Δt, M = ΔT/Δt. The mean value of the weight coefficient sequence C0 was used to reflect the stable operating status in ΔT, which can be calculated as
C0 =
M
∑i =1 Ci/M
(13)
Obviously, the range of C0 is [c3, −c3]. The weight coefficients c1, c2 and c3 were set to ΔT/4, ΔT/2 and ΔT, respectively. The rolling model was used to update coefficient C0. That is, once the controller receives a new data, the oldest data should be updated simultaneously. The adjustment method of piston rod was determined according to statistical results of C0.
SC = Fp max / S
where S is the sectional area of the probe. With the compaction depth h and forward speed v in the ranges of 0–120 mm and 0.4–1.4 m/s, calculation results of the variations of SC are shown in Fig.9. Increasing the compaction depth h will increase the compact force action on the seedbed surface. When h was in a small range (h < 60 mm), there was a quick increasing of SC with an increasing of h. Meanwhile, the increasing of h would increase the internal damping and plastic deformation of soil particles, resulting in the increase of h on SC was no longer significant when h was greater than 60 mm. Relationship between SC and h could also be fitted using an exponential function Formula 14. With v of 0.6, 1.0 and 1.4 m/s, the obtained fitting coefficient a was −550.3, −517.8 and −497.5, and the coefficient b was −0.03747, −0.03762 and −0.03783, respectively (Fig.9(a)). The coefficients of determination R2 were greater than 0.9880. When the compaction depth h was 20 mm, the effect of forward speed v on SC was not significant. This was mainly due to the relatively small SC after the compaction. As h increased up to 40 and 60 mm, SC presented a linearly decreasing relationship with the increase of v (Fig.9(b)). This was mainly affected by the elasto-plastic properties of soil particles. With the increase of v, the compact time of roll on seedbed linearly decreased. This resulted in an increase in the proportion of elastic deformation to total deformation, and the elastic recovery
3. Results and discussion 3.1. DEM simulation results With the forward speed v of 1.0 m/s and compaction depth h of 40 mm, DEM simulation results of the change process of vertical force Fn acting on roller is shown in Fig.6. At time t0, the roller began to contact with the seedbed surface, and the Fn increased quickly. From time t1 to t2, the roller was fully in contact with the seedbed, and the Fn fluctuated within a certain range. After time t2, the roller began to separate from the seedbed, and the Fn gradually decreased to 0. The mean value of Fn from time t1 to t2 was calculated to describe the stable interaction force between the roller and the soil, which was denoted as Fn0. In order to analyse the effect of compaction depth h and forward speed v on the variation of Fn0, DEM simulations were carried out with h and v in the ranges of 0–120 mm and 0.4–1.4 m/s. Variations of Fn0 are shown in Fig.7. It is obvious that Fn0 increased with the increasing of h. According to simulation results, their relationship can be fitted using an exponential function with the form
Fn0 = a⋅(e b ⋅ k − 1)
(15)
(14) 4
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Fig. 7. Variation of Fn0 measured using DEM simulations: (a) effect of compaction depth; (b) effect of forward speed.
Fig. 10. Relationship between Fn0 and SC. Fig. 8. DEM simulation result of vertical force action on probe.
3.2. Field test results of soil particles would reduce SC. According to the simulation results given in Figs. 7 and 9, the relationship between soil compactness SC and the mean value compact force Fn0 could be established (Fig.10). It was found that SC presented an obvious monotonous increasing tendency with the increase of Fn0, and the increase rate ΔSC/ ΔFn was gradually decreasing. This indicated that, with the soil compactness in a small range (SC < 500 kPa), it was feasible to obtain the change of soil compactness by measuring the compact force action on roller.
In order to verify the fragment soil compaction dynamic characteristics obtained by DEM simulations and the performance of the proposed control method, seedbed preparation tests were carried out (Fig.10). The soil preparation machine was powered by a 65 kW tractor Fig. 11. Two force sensors were installed to measure the compact force, as shown in Fig. 2. The fuzzy control system was developed based on ARM STM32F103. The ARM acquired output signals of the force sensors with the sampling frequency of 40 Hz. The statistical time period ΔT was set as 2 s. According to statistical results, the ARM output signal to adjust the position of piston rod in real time. In Test #1, the forward speed v was 1.3 m/s and the force setpoint
Fig. 9. Variation of soil compactness measured using DEM simulation: (a) effect of compress depth; (b) effect of forward speed. 5
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3.3. Comparative discussion For most soil preparation machines, the position of the compact roller was kept constant during an operation. In order to comparative analysis the operation performance, field tests were carried out with the roller position kept constant. Due to the uneven surface of the fragment soil after rotary tillage, it was difficult to accurately determine the compaction depth. However, changes of piston rod position could reflect changes of compaction depth. According to the variation range of measured compact force, 3 positions of the piston rod z of 260, 285 and 310 mm were selected. Coordinate parameter z was the distance from the base of electric cylinder to the top of piston rod. The corresponding change of compaction depth was 50 mm. With z of 285 mm, v of 0.95 m/s, and z of 260 mm, v of 1.3 m/s, variations of the measured compact force are shown in Fig. 12 (b). Influenced by the variations of fragment soil, field slope and soil mechanical properties, the compact forces were fluctuated within a large range. Soil compactness was measured in the same way, and the results are given in Table 2. Compact force was the primary factor determining soil compaction, and it was increased exponentially with the increasing of compact depth. So, adjustment of seedbed compaction both could be achieved by controlling the compaction depth and controlling the compact force. It was obvious from Table 2 that the stability of the seedbed compaction was effectively improved by using the proposed compact force control system. This was mainly because the fluctuation range of compact force was effectively reduced. DEM simulation results indicated that, the increasing of forward speed increased the compact force and decreased the soil compaction. Results of two groups of field tests verified that the soil compactness was obviously decreased with the forward speed increased from 0.75 to 1.3 m/s, and the corresponding stability of soil compaction was decreased slightly. With the increasing of compact force and compaction depth, the rate of increase in seedbed compaction was decreased gradually. Due to the complexity and uncertainty of the soil environment in the field, there were some differences between the simulation and test results, especially in the specific values. But, the basic compaction dynamic characteristics were consistent, which proved that the DEM simulation method and the EEPA model were reliable.
Fig. 11. Performance tests of soil preparation.
F0 was 2200 N. At the beginning of the test, the roll was lifted off the seedbed surface. The measured compact force Fn was fluctuated around 0, and the control system automatically drove the roller to move towards the seedbed. At time t1, the roller touched the soil surface, and the compact force began to increase quickly. At time t2, the compact force reached the setpoint, and the roller stopped moving to maintain a certain compact depth. According to the change of measured force and the control strategy, there were two position adjustments of roller at times t3 and t4. The adjustments were mainly affected by the variations of field slope and amount of fragment soil, which was very common in field operation process. During the test, the compact force was generally controlled to fluctuate within 2200 ± 300 N. In Test #2, the forward speed v was 0.95 m/s and the force setpoint F0 was 1000 N. At the beginning, the roller was manually adjusted to a certain position. Then, it was switched to the automatic control mode. Due to the measured compact force was far greater than the setpoint, the roller was continuously lifted to reduce the compaction depth. The compact force was maintained to fluctuate around 1000 N. At time t5, there was a sudden drop in measured force. Since this process was instantaneous, the system did not adjust the position of the roller, which improved the working stability. The measured variations of compact force are shown in Fig. 12 (a). After soil preparation operations, 40 points were selected at equal intervals on the seedbed surface to measure the soil compactness. The measuring instrument was TJSD-750 Soil Compaction Meter. The diameter of the probe was 12 mm, the tip angle was 30°, the effective measurement depth was 250 mm, and the measurement accuracy was 0.1%. With the penetration depth of 100 mm, measurements were carried out with the forward speeds of 0.75, 0.95 and 1.3 m/s, and the force setpoint of 1000, 1600 and 2200 N, the mean value and the standard deviation of soil compactness are given in Table 2.
4. Conclusion The compact dynamic process of fragment soil after rotary tillage was simulated using DEM, and the EEPA contact model was applied to analyze the interaction force between compact roller and fragment soil. Simulation results showed that, the compact force increased exponentially with increasing of compaction depth, and increased linearly with the increasing of forward speed. At the same compaction depth, the soil compactness will decrease with the increasing of forward velocity. The soil compactness presented a monotonous increasing
Fig. 12. Measurement results of compact force: (a) force control; (b) constant position. 6
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Table 2 Comparison of soil preparation performance. Method
Proposed control method
Constant compaction depth
Forward speed (m/s)
0.75 0.95
1.3 0.75 0.95
1.3
Parameter
Compactness
Position (mm)
Force setpoint (N)
Mean value (kPa)
Deviation (kPa)
Relative error (%)
/ / / / / 285 310 285 260 285
1600 1000 1600 2200 2200 / / / / /
435 271 396 475 328 485 280 427 503 372
28.5 25.6 31 35.6 41.3 86.9 62.6 75.7 95.6 98.8
6.55 9.44 7.82 7.49 12.6 17.9 22.3 17.7 19.0 26.5
tendency with the increase of compact force, and the increase rate was gradually decreased. Therefore, it was a feasible way to obtain the soil compaction by measuring the compact force acting on roller. Force sensors were mounted on the soil preparation machine to measure the compact force during operation. The paper proposed to adjust the compaction depth in real time by using a method combining fuzzy calculation and statistical analysis. And the soil compactness could be controlled by changing the setting compact force. Soil preparation performance tests were carried out in field. Compared with the traditional working method using constant compact depth, the proposed control method could significantly improve the uniformity of soil compaction, which was helpful for providing a good growth environment for crops.
evaluation. Soil Tillage Res. 67, 41–64. Donohue, S., Forristal, D., Donohue, L.A., 2013. Detection of soil compaction using seismic surface waves. Soil Tillage Res. 128, 54–60. Chen, Y., Munkholm, L.J., Nyord, T., 2013. A discrete element model for soil–sweep interaction in three different soils. Soil Tillage Res. 126, 34–41. Conte, O., Levien, R., Debiasi, H., Sturmer, S.L.K., Mazurana, M., Muller, J., 2011. Soil disturbance index as an indicator of seed drill efficiency in no-tillage agro-systems. Soil Tillage Res. 114, 137–142. Gemtos, T.A., Goulas, C., Lellis, T., 2000. Sugar beet genotype response to soil compaction stress. Eur. J. Agron. 12, 201–209. Grift, T.E., Tekeste, M.Z., Raper, R.L., 2005. Acoustic compaction layer detection. Trans. ASAE 48, 1723–1730. Hemmat, A., Rahnama, T., Vahabi, Z., 2014. A horizontal multiple-tip penetrometer for on-the-go soil mechanical resistance and acoustic failure mode detection. Soil Tillage Res. 138, 17–25. Janda, A., Ooi, J.Y., 2016. DEM modeling of cone penetration and unconfined compression in cohesive solids. Powder Technol. 293, 60–68. Keller, T., Lamandé, M., 2010. Challenges in the development of analytical soil compaction models. Soil Tillage Res. 111, 54–64. Lovarelli, D., Bacenetti, J., 2017. Seedbed preparation for arable crops: environmental impact of alternative mechanical solutions. Soil Tillage Res. 174, 156–168. Ma, Y.J., Wang, A., Zhao, J.G., Hao, J.J., Li, J.C., Ma, L.P., Zhao, W.B., Wu, Y., 2019. Simulation analysis and experiment of drag reduction effect of convex blade subsoiler based on discrete element method. Trans. Chin. Soc. Agric. Eng. 35 (3), 16–23. Mak, J., Chen, Y., Sadek, M.A., 2012. Determining parameters of a discrete element model for soil–tool interaction. Soil Tillage Res. 118, 117–122. Markauskas, D., Kačianauskas, R., Džiugys, A., Navakas, R., 2010. Investigation of adequacy of multi-sphere approximation of elliptical particles for DEM simulations. Granul. Matter 12, 107–123. McPhee, J.E., Aird, P.L., 2013. Controlled traffic for vegetable production: part 1. Machinery challenges and options in a diversified vegetable industry. Biosyst. Eng. 116 (2), 144–154. Moallemi-Oreh, A., Minaei, S., Sharifi-Malvajerdi, A., Borghaee, A.M., 2010. Multiprobe soil compaction sensor: an acoustical approach. J. Agric. Environ. 8, 747–750. Obermayr, M., Vrettos, C., Eberhard, P., Däuwel, T., 2014. A discrete element model and its experimental validation for the prediction of draft forces in cohesive soil. J. Terramechanics 53, 93–104. Petersen, H., Fleige, H., Rabbel, W., Horn, R., 2005. Applicability of geophysical prospecting methods for mapping of soil compaction and variability of soil texture on farm land. J. Plant Nutr. Soil Sci. 168, 68–79. Rodgers, D., McPhee, J., Aird, P., Corkrey, R., 2018. Soil arthropod responses to controlled traffic in vegetable production. Soil Tillage Res. 180, 154–163. Thakur, S.C., Ooi, Y.J., Ahmadian, H., 2016. Scaling of discrete element model parameters for cohesionless and cohesive solid. Powder Technol. 293, 130–137. Ucgul, M., Fielke, J.M., Saunders, C., 2015. Three-dimensional discrete element modeling (DEM) of tillage: accounting for soil cohesion and adhesion. Biosyst. Eng. 129, 298–306. Wang, X.L., Hu, H., Wang, Q.J., Li, H.W., He, J., Chen, W.Z., 2017. Calibration method of soil contact characteristic parameters based on DEM theory. Trans. Chin. Soc. Agric. Mach. 48 (12), 78–85. Wang, X.Z., Gao, P.Y., Yue, B., Shen, H., Fu, Z.L., Zheng, Z.Q., Zhu, R.X., Huang, Y.X., 2019. Optimisation of installation parameters of subsoiler’ wing using the discrete element method. Comput. Electron. Agric. 162, 523–530. Zhao, Z., Jin, M.Z., Tian, C.J., Yang, S.X., 2018. Prediction of seed distribution in rectangular vibrating tray using grey model and artificial neural network. Biosyst. Eng. 175, 194–205. Zheng, K., He, J., Li, H.W., Diao, P.S., Wang, Q.J., Zhao, H.B., 2016. Research on polyline soil-breaking blade subsoiler based on subsoiling soil model using discrete element method. Trans. Chin. Soc. Agric. Mach. 47, 62–72.
Declaration of Competing Interest We declare that we have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. There is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled, “Control method of seedbed compactness based on fragment soil compaction dynamic characteristics”. Acknowledgements This work is supported by the National Natural Science Foundation of China (No. 51775246) and a Priority Academic Program Development of Jiangsu Higher Education Institutions (No. PADP2018-87). References Alaoui, A., Diserens, E., 2018. Mapping soil compaction – a review. Curr. Opin. Environ. Sci. Health 5, 60–66. Ani, O.A., Uzoejinwa, B.B., Ezeama, A.O., Onwualu, A.P., Ugwu, S.N., Ohagwu, C.J., 2018. Overview of soil-machine interaction studies in soil bins. Soil Tillage Res. 175, 13–27. Arvidsson, J., Bölenius, E., Cavalieri, K.M.V., 2012. Effects of compaction during drilling on yield of sugar beet (Beta vulgaris L.). Eur. J. Agron. 39, 44–51. Atkinson, B.S., Sparkes, D.L., Mooney, S.J., 2007. Using selected soil physical properties of seedbeds to predict crop establishment. Soil Tillage Res. 97, 218–228. Batey, T., 2009. Soil compaction and soil management – a review. Soil Use Manage 25, 335–345. Bravo, E.L., Tijskens, E., Suárez, M.H., Cueto, O.G., Ramon, H., 2014. Prediction model for non-inversion soil tillage implemented on discrete element method. Comput. Electron. Agric. 106, 120–127. Defossez, P., Richard, G., 2002. Models of soil compaction due to traffic and their
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Soil & Tillage Research journal homepage: www.elsevier.com/locate/still
Control method of seedbed compactness based on fragment soil compaction dynamic characteristics
T
Zhan Zhaoa,*, Hongchang Lib, Jinkai Liua, Simon X. Yangc,* a
Institute of Agriculture Engineering, Jiangsu University, Zhenjiang 212013, China College of Vehicle Engineering, Changzhou Vocational Institute of Mechatronic Technology, Changzhou 213164, China c School of Engineering, University of Guelph, Guelph N1G 2W1, Canada b
A R T I C LE I N FO
A B S T R A C T
Keywords: Soil preparation machine Seedbed compaction Discrete element method Dynamic analysis Compaction depth control
Seedbed preparation is the initial and fundamental operation for achieving an optimal soil condition for vegetable production. Seedbed compaction is an important technical criterion for evaluating the operating performance of a soil preparation machine. To analyze the dynamic properties of fragment soil compaction after rotary tillage, a discrete element method (DEM) was used to simulate the compaction process, and the Edinburgh elasto-plastic adhesion model (EEPA) was selected to calculate the interaction force between compact roller and fragment soil. The relationship models between compact force and soil compactness at different machine forward speeds and compaction depths were established. Then, it was proposed to control the soil compaction by adjusting the compaction depth, and the adjustment range of compaction depth was calculated using the fuzzy algorithm which taking the compact force as input parameter. In order to reduce the effect of compact force fluctuations and improve the stability of control system, the adjustment strategy was formulated according to the statistical analysis of fuzzy calculation outputs over a period of time. Field experiment results indicated that the seedbed compaction could be automatically adjusted in real time according to the set compact force. Compared with the traditional method of using a constant compaction depth, the stability of soil compaction was significantly improved. The proposed method is useful for improving the working performance and adaptability of soil preparation machines in different field environments.
1. Introduction In the mechanized vegetable production, seedbed preparation is fundamental for achieving an optimal growing soil environment for crops (Atkinson et al., 2007; Lovarelli and Bacenetti, 2017). It mainly includes operating procedures of ridging through a disc harrow, soil fragmentation performed using a rotary harrow and surface compaction performed using a compact implement. Soil compaction is an important criterion for evaluating the growing condition since it affects soil-seed contact, which is crucial for seed germination. Soil compaction is generally regarded as being negative within agriculture, since it can lead to reduced water infiltration, soil aeration and gas exchange, increased surface runoff and erosion, ultimately affecting plant growth and the crop yields (Batey, 2009; McPhee and Aird, 2013; Rodgers et al., 2018). However, a large number of studies and experiments also showed that some compaction can have positive effects such as increasing soil unsaturated hydraulic conductivity and thereby the capillary flow of water and nutrients to seeds and plants
⁎
(Gemtos et al., 2000; Arvidsson et al., 2012). Different crops and cultivation methods have different requirements for soil compaction. Therefore, it is needed to control the compactness within a reasonable range during the seedbed preparation process. Currently, this objective is achieved mainly by manually adjusting the position of the compact implement according to the variation of soil compaction after preparation, which is obviously lagging. Assessment of agricultural soil compaction is conventionally performed by using invasive vertical sensors such as penetrometers and shear vanes (Donohue et al., 2013; Alaoui and Diserens, 2018). With advances in precision agriculture, soil compaction detection method has been under focused investigation by many researchers, and many new detection sensing systems were developed (Grift et al., 2005; Petersen et al., 2005; Moallemi-Oreh et al., 2010; Hemmat et al., 2014). Most of these approaches are offline measurement methods, which can provide discrete or continuous soil compaction information after soil preparation operation. After rotary tillage operations, the fragmented soil loosely covers
Corresponding authors. E-mail addresses: [email protected] (Z. Zhao), [email protected] (S.X. Yang).
https://doi.org/10.1016/j.still.2019.104551 Received 29 August 2019; Received in revised form 18 December 2019; Accepted 18 December 2019 Available online 07 January 2020 0167-1987/ © 2019 Elsevier B.V. All rights reserved.
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1100 mm.
the surface of the seedbed. By applying a certain force to the fragmented soil through a compact implement, a seedbed soil structure with certain compactness can be formed. The compactness mainly depends on the interaction process between the compact implement and the fragment soil. This is also pivotal for design, optimization, automation and control of soil preparation machines. The existing research mainly focuses on two aspects: one is the analysis of forces developed at the interface of the soil and the operating tools such as draught, rotary harrow and subsoiling shovel; another is the displacement or deformation of soil particles also known as soil disturbance (Conte et al., 2011; Ani et al., 2018). In recent years, the discrete element method (DEM) has been recognized as an effective way to simulate soil–tool interactions (Mak et al., 2012; Ucgul et al., 2015; Wang et al., 2019). Contact models significantly affect the accuracy of DEM simulations, and various models have been introduced into compaction studies of different soils (Defossez and Richard, 2002; Keller and Lamandé, 2010). Vegetable cultivation soils are usually cohesive and compressible. In order to accurately describe the mechanical properties of this type of soil, a new elasto-plastic adhesion (EEPA) model was presented (Thakur et al., 2016; Janda and Ooi, 2016). The objective of this work was to evaluate the compaction mechanical properties of fragment soil after rotary tillage using DEM simulations. The EEPA model was applied to analyze the relationship between the compact force and the soil compactness at different forward speeds and compaction depths. Through the combination of fuzzy calculation and statistical analysis, a real-time control method based on compact force feedback was proposed to improve the uniformity of soil compaction during the soil preparation process. Finally, seedbed preparation tests were carried out to verify the reliability of the control system, and the operating performance was compared with the traditional method of using a constant compaction depth.
2.2. DEM simulation of soil compact process In the DEM simulations, spherical particles are the most widely used model for describing soil particles (Chen et al., 2013; Bravo et al., 2014; Obermayr et al., 2014). However, the shape of fragment soil aggregates after rotary tillage is usually varied and irregular. Here, the clusteredparticle approach was used to represent the soil particle, in which small sphere body elements are allowed to overlap to form a complex shape (Markauskas et al., 2010; Zhao et al., 2018). In order to describe the irregularity of fragment soil particle shape, five typical models with different shapes were established (Wang et al., 2017; Ma et al., 2019). The primary diameter of sphere element was 10 mm, and the size ratio varied from 60 to 150 % (Fig.2). The normal force-overlap relationship for the EEPA contact model is shown in Fig.3. When two discrete soil particles are pressed together, they undergo elastic and plastic deformations, and the pull-off (adhesive) force increases with an increase of the plastic contact area. The total contact normal force fn is the sum of the hysteretic spring force fhys and the normal damping force fnd, which can be written as
fn = fhys + fnd
(1)
The fhys and fnd can be calculated by n ifk2 (δ n − δpn ) ≥ k1 δ ⎧ f0 + k1 δ ⎪ = f0 + k2 (δ n − δpn ) ifk1 δ n > k2 (δ n − δpn ) > − kadh δ x ⎨ ⎪ f0 − kadh δ x if−kadh δ x ≥ k2 (δ n − δpn ) ⎩
fhys
fnd = −2
5 β Kn m* vnrel 6
(2)
(3)
where δ is the contact overlap, f0 is the constant pull-off force, k1 is the initial loading stiffness, k2 is the unloading/reloading stiffness, kadh is the adhesive stiffness (kadh = fmin / δminx), n is the power value for k1 and k2, x is the power value for kadh, vnrel is the normal component of the relative velocity. The damping coefficient β and the Hertzian normal stiffness Kn are given by
2. Material and methods 2.1. Soil preparation machine The structure of the soil preparation machine is shown in Fig.1. The rotary tillage and ridge components were mounted at the front of the machine. The compact components mainly consisted of a compact roller, two guiding shafts, two force sensors, a horizontal beam and an electric cylinder. The roller was connected to the beam through two guiding shafts, and two force sensors were installed between the connections to measure the compact force acting on roller. The fragment soil produced by rotary tillage was compacted by the roller to form a flat ridge surface. Driven by an electric cylinder, the roller moved in the vertical direction to change the compaction depth. The driving force of the electric cylinder was 5000 N, and the movement speed of the piston rod was 5 mm/s. In order to improve the flatness and shape of the ridge, the roller was driven by a hydraulic motor to rotate during the working process. The diameter and length of the roller were 230 mm and
β=
ln e ln2 e + π 2
(4) (5)
Kn = 2E * R*δ
where m*, E* and R* are the equivalent mass, Young’s modulus and radius, respectively. The tangential force fτ is the sum of tangential spring force fτs and tangential damping force fτd, as given by
fτ = fτ s + fτ d
(6)
The tangential spring force fτs is expressed in incremental terms
fτ s = fτ s(t-1) + Δfτ s
(7)
where fτs(t-1) is the tangential spring force at the previous time step, Δfτs is the increment of tangential force and is given by
Δfτ s = −k τ δτ
(8)
where kτ is the tangential stiffness, and δτ is the increment of tangential displacement. The tangential damping force fτd is calculated by
fτ d = −2
5 β K τ m* vτrel 6
(9)
K τ = 8G * R*δ
(10) vτrel
is the tangential comwhere G* is the equivalent shear modulus, ponent of the relative velocity. The total tangential force fτ needs to be coupled to the normal force
Fig. 1. Structure of soil preparation machine. 2
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Fig. 2. Typical fragment soil particle models.
Fig. 3. Force-overlap relationship for the EEPA contact model.
Fig. 4. Snapshot of DEM simulation: (a) soil compaction; (b) compactness measurement.
through Coulomb’s law, so that the limiting tangential friction force fcτ is calculated by
fcτ ≤ μ |fhys + kadh δ n − f0 |
acting on 19 probes was also recorded with a sampling frequency of 100 Hz.
(11)
2.3. Fuzzy calculation method
where μ is the friction coefficient. Detailed information about the governing equations, contact models and interaction coefficient is given by Thakur et al. (2016) and Wang et al. (2017). Simulations of the compact force variations during the fragment soil compact process were performed using the DEM software (EDEM® 2018, EDEM, Edinburgh, UK), in which the EEPA contact model was implemented through the API. Under the action of gravity, five typical fragment soil particle models were loosely and evenly accumulated to form an initial seedbed. The length, width and height of the seedbed were 3000 mm, 1700 mm and 400 mm, respectively. The material of roller was Steel #45. The density was 7850 kg/m3, the elastic modulus was 2 × 1011 Pa, and the poisson's ratio was 0.3. According to the general mechanical properties of cohesive soil, the density, shear modulus and poisson's ratio were set to 2600 kg/m3, 1 × 106 Pa and 0.28. The friction coefficient, rolling friction coefficient and restitution coefficient of solid particle were 0.33, 0.15 and 0.55, respectively. The restitution coefficient between the roller and the soil particle was 0.45 (Ucgul et al., 2015; Zheng et al., 2016). Other coefficients used in DEM simulation were f0 = 0, Δγ = 30, λp = 0.7, n = 1.5, x = 5, ξtm = 0.2857 (Janda and Jin, 2016; Wang et al., 2017; Ma et al., 2019). The compaction depth h was defined as the distance between the initial soil surface and the bottom of the roller. The constant rotation speed of the roller ω was 175 rpm during the simulations, and the variation of the vertical force Fn acting on roller was recorded with a sampling frequency of 100 Hz. With the forward speed v of 1.0 m/s, compaction depth h of 40 mm, a graphic of the DEM simulation of the seedbed compaction is shown in Fig. 4 (a). In order to obtain the soil compactness, after completion of the compaction operation, 19 measuring probes were vertically and synchronously penetrated into the seedbed. The probes were evenly arranged on the soil surface, which could effectively reduce the random error caused by a single probe measurement and obtain a stable compactness value. The diameter of the probe was 12 mm, and the structural parameters were the same as those of the TJSD-750 Soil Compaction Meter. The penetration speed was 50 mm/s and the penetration depth was 100 mm. The variation of average vertical force Fp
Due to the compaction depth is the key factor in determining the soil compaction, the vertical position of the roller and the compact force were selected as the control variable and monitoring variable respectively. The soil compaction control is a strong non-linear system, which is affected by many factors, such as forward speed, changing operating conditions and random interferences. It is difficult to establish an accurate control model. But, there is a basic control law: when the soil compaction is insufficient, the compaction depth needs to be increased; when the soil compaction is excessive, the compaction depth needs to be reduced. Therefore, the fuzzy theory was used to solve this control problem. A two-dimensional fuzzy controller was designed, which takes compact force error and its change rate as inputs and the compaction depth (movement direction and time period of piston rod) as output. The force error e(t) between the setpoint F0 and the monitoring value F (t), and the corresponding error change rate ec(t) can be calculated as
⎧ e (t ) = F (t ) − F0 ⎨ ⎩ ec (t ) = e (t ) − e (t − 1)
(12)
According to the demand, error e, error change rate ec and compaction depth h were divided into seven fuzzy subsets: PB (far greater than 0), PM (greater than 0), PS (slightly greater than 0), ZO (equal to 0), NS (slightly less than 0), NM (less than 0) and NB (far less than 0). Use triangular-shaped membership function (Fig. 5), and get the solutions of fuzzy by the method of weighted average. According to the experience accumulated, a fuzzy calculation rule table including 49 fuzzy rules was developed, which is expressed by the fuzzy conditional statement “if e and ec then h” (Table 1). 2.4. Control method based on statistical analysis Under practical conditions, it is inevitable that there is some random interference. This may cause the measured e and ec to fluctuate within a large range instantaneously. Therefore, only using fuzzy 3
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Fig. 5. The membership function of e, ec and h. Table 1 Fuzzy control rules. e, ec, h
NB
NM
NS
ZO
PS
PM
PB
NB NM NS ZO PS PM PB
PB PB PM PM PS PS ZO
PB PM PM PM PS ZO ZO
PM PM PM ZO ZO NM NM
PM PM PS ZO NS NM NM
PS PS ZO ZO NS NM NM
ZO ZO NS NM NM NM NB
ZO NS NS NM NM NB NB
Fig. 6. Variation of Fn measured using DEM simulation.
where the fitting coefficients a and b are obtained using nonlinear regression analysis under a certain forward speed v. With v of 0.6, 1.0 and 1.4 m/s, the obtained coefficient a was 4000, 6025 and 8260, and the coefficient b was 0.009026, 0.007269 and 0.006015, respectively (Fig.7(a)). The coefficients of determination R2 established from the regression equations were greater than 0.9810. Increasing the forward speed would linearly increase the work efficiency (area of compacted seedbed per unit time). So, when v was increased from 0.4 to 1.4 m/s, the mean value of compact force Fn0 approximately increased linearly (Fig.7(b)). When the seedbed was compacted with the forward speed v of 1.0 m/s and compaction depth h of 40 mm, the variation of average vertical force acting on one probe Fp during the soil compactness measurement is shown in Fig.8. The Fp gradually increased as the probes penetrated into the seedbed. Since the seedbed was an anisotropic discontinuous medium, there were some fluctuations in the increasing process of Fp. Due to the movement time of probe was 2 s, the peak force Fp max appeared at time of 2 s. Then, due to the elasto-plastic and adhesive interaction between the soil particles and the probes, the Fp began to reduce gradually rather than quickly reduced to 0. The soil compactness SC is calculated by
calculation method may cause frequent adjustment and misadjustment of the piston rod. To avoid this problem, it was proposed to optimize the control system by integrating statistical analysis. The outputs of fuzzy control H = {PB, PM, PS, ZO, NS, NM, NB} were quantized into the weight coefficients C = {c3, c2, c1, 0, −c1, −c2, −c3}. Assume that the statistical time period was ΔT, and the obtained continuous weight coefficient sequence were {Ci, Ci+1, …, CM}. M is the number of statistical samples in one period ΔT, that is, the ratio of ΔT to the sampling period Δt, M = ΔT/Δt. The mean value of the weight coefficient sequence C0 was used to reflect the stable operating status in ΔT, which can be calculated as
C0 =
M
∑i =1 Ci/M
(13)
Obviously, the range of C0 is [c3, −c3]. The weight coefficients c1, c2 and c3 were set to ΔT/4, ΔT/2 and ΔT, respectively. The rolling model was used to update coefficient C0. That is, once the controller receives a new data, the oldest data should be updated simultaneously. The adjustment method of piston rod was determined according to statistical results of C0.
SC = Fp max / S
where S is the sectional area of the probe. With the compaction depth h and forward speed v in the ranges of 0–120 mm and 0.4–1.4 m/s, calculation results of the variations of SC are shown in Fig.9. Increasing the compaction depth h will increase the compact force action on the seedbed surface. When h was in a small range (h < 60 mm), there was a quick increasing of SC with an increasing of h. Meanwhile, the increasing of h would increase the internal damping and plastic deformation of soil particles, resulting in the increase of h on SC was no longer significant when h was greater than 60 mm. Relationship between SC and h could also be fitted using an exponential function Formula 14. With v of 0.6, 1.0 and 1.4 m/s, the obtained fitting coefficient a was −550.3, −517.8 and −497.5, and the coefficient b was −0.03747, −0.03762 and −0.03783, respectively (Fig.9(a)). The coefficients of determination R2 were greater than 0.9880. When the compaction depth h was 20 mm, the effect of forward speed v on SC was not significant. This was mainly due to the relatively small SC after the compaction. As h increased up to 40 and 60 mm, SC presented a linearly decreasing relationship with the increase of v (Fig.9(b)). This was mainly affected by the elasto-plastic properties of soil particles. With the increase of v, the compact time of roll on seedbed linearly decreased. This resulted in an increase in the proportion of elastic deformation to total deformation, and the elastic recovery
3. Results and discussion 3.1. DEM simulation results With the forward speed v of 1.0 m/s and compaction depth h of 40 mm, DEM simulation results of the change process of vertical force Fn acting on roller is shown in Fig.6. At time t0, the roller began to contact with the seedbed surface, and the Fn increased quickly. From time t1 to t2, the roller was fully in contact with the seedbed, and the Fn fluctuated within a certain range. After time t2, the roller began to separate from the seedbed, and the Fn gradually decreased to 0. The mean value of Fn from time t1 to t2 was calculated to describe the stable interaction force between the roller and the soil, which was denoted as Fn0. In order to analyse the effect of compaction depth h and forward speed v on the variation of Fn0, DEM simulations were carried out with h and v in the ranges of 0–120 mm and 0.4–1.4 m/s. Variations of Fn0 are shown in Fig.7. It is obvious that Fn0 increased with the increasing of h. According to simulation results, their relationship can be fitted using an exponential function with the form
Fn0 = a⋅(e b ⋅ k − 1)
(15)
(14) 4
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Fig. 7. Variation of Fn0 measured using DEM simulations: (a) effect of compaction depth; (b) effect of forward speed.
Fig. 10. Relationship between Fn0 and SC. Fig. 8. DEM simulation result of vertical force action on probe.
3.2. Field test results of soil particles would reduce SC. According to the simulation results given in Figs. 7 and 9, the relationship between soil compactness SC and the mean value compact force Fn0 could be established (Fig.10). It was found that SC presented an obvious monotonous increasing tendency with the increase of Fn0, and the increase rate ΔSC/ ΔFn was gradually decreasing. This indicated that, with the soil compactness in a small range (SC < 500 kPa), it was feasible to obtain the change of soil compactness by measuring the compact force action on roller.
In order to verify the fragment soil compaction dynamic characteristics obtained by DEM simulations and the performance of the proposed control method, seedbed preparation tests were carried out (Fig.10). The soil preparation machine was powered by a 65 kW tractor Fig. 11. Two force sensors were installed to measure the compact force, as shown in Fig. 2. The fuzzy control system was developed based on ARM STM32F103. The ARM acquired output signals of the force sensors with the sampling frequency of 40 Hz. The statistical time period ΔT was set as 2 s. According to statistical results, the ARM output signal to adjust the position of piston rod in real time. In Test #1, the forward speed v was 1.3 m/s and the force setpoint
Fig. 9. Variation of soil compactness measured using DEM simulation: (a) effect of compress depth; (b) effect of forward speed. 5
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3.3. Comparative discussion For most soil preparation machines, the position of the compact roller was kept constant during an operation. In order to comparative analysis the operation performance, field tests were carried out with the roller position kept constant. Due to the uneven surface of the fragment soil after rotary tillage, it was difficult to accurately determine the compaction depth. However, changes of piston rod position could reflect changes of compaction depth. According to the variation range of measured compact force, 3 positions of the piston rod z of 260, 285 and 310 mm were selected. Coordinate parameter z was the distance from the base of electric cylinder to the top of piston rod. The corresponding change of compaction depth was 50 mm. With z of 285 mm, v of 0.95 m/s, and z of 260 mm, v of 1.3 m/s, variations of the measured compact force are shown in Fig. 12 (b). Influenced by the variations of fragment soil, field slope and soil mechanical properties, the compact forces were fluctuated within a large range. Soil compactness was measured in the same way, and the results are given in Table 2. Compact force was the primary factor determining soil compaction, and it was increased exponentially with the increasing of compact depth. So, adjustment of seedbed compaction both could be achieved by controlling the compaction depth and controlling the compact force. It was obvious from Table 2 that the stability of the seedbed compaction was effectively improved by using the proposed compact force control system. This was mainly because the fluctuation range of compact force was effectively reduced. DEM simulation results indicated that, the increasing of forward speed increased the compact force and decreased the soil compaction. Results of two groups of field tests verified that the soil compactness was obviously decreased with the forward speed increased from 0.75 to 1.3 m/s, and the corresponding stability of soil compaction was decreased slightly. With the increasing of compact force and compaction depth, the rate of increase in seedbed compaction was decreased gradually. Due to the complexity and uncertainty of the soil environment in the field, there were some differences between the simulation and test results, especially in the specific values. But, the basic compaction dynamic characteristics were consistent, which proved that the DEM simulation method and the EEPA model were reliable.
Fig. 11. Performance tests of soil preparation.
F0 was 2200 N. At the beginning of the test, the roll was lifted off the seedbed surface. The measured compact force Fn was fluctuated around 0, and the control system automatically drove the roller to move towards the seedbed. At time t1, the roller touched the soil surface, and the compact force began to increase quickly. At time t2, the compact force reached the setpoint, and the roller stopped moving to maintain a certain compact depth. According to the change of measured force and the control strategy, there were two position adjustments of roller at times t3 and t4. The adjustments were mainly affected by the variations of field slope and amount of fragment soil, which was very common in field operation process. During the test, the compact force was generally controlled to fluctuate within 2200 ± 300 N. In Test #2, the forward speed v was 0.95 m/s and the force setpoint F0 was 1000 N. At the beginning, the roller was manually adjusted to a certain position. Then, it was switched to the automatic control mode. Due to the measured compact force was far greater than the setpoint, the roller was continuously lifted to reduce the compaction depth. The compact force was maintained to fluctuate around 1000 N. At time t5, there was a sudden drop in measured force. Since this process was instantaneous, the system did not adjust the position of the roller, which improved the working stability. The measured variations of compact force are shown in Fig. 12 (a). After soil preparation operations, 40 points were selected at equal intervals on the seedbed surface to measure the soil compactness. The measuring instrument was TJSD-750 Soil Compaction Meter. The diameter of the probe was 12 mm, the tip angle was 30°, the effective measurement depth was 250 mm, and the measurement accuracy was 0.1%. With the penetration depth of 100 mm, measurements were carried out with the forward speeds of 0.75, 0.95 and 1.3 m/s, and the force setpoint of 1000, 1600 and 2200 N, the mean value and the standard deviation of soil compactness are given in Table 2.
4. Conclusion The compact dynamic process of fragment soil after rotary tillage was simulated using DEM, and the EEPA contact model was applied to analyze the interaction force between compact roller and fragment soil. Simulation results showed that, the compact force increased exponentially with increasing of compaction depth, and increased linearly with the increasing of forward speed. At the same compaction depth, the soil compactness will decrease with the increasing of forward velocity. The soil compactness presented a monotonous increasing
Fig. 12. Measurement results of compact force: (a) force control; (b) constant position. 6
Soil & Tillage Research 198 (2020) 104551
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Table 2 Comparison of soil preparation performance. Method
Proposed control method
Constant compaction depth
Forward speed (m/s)
0.75 0.95
1.3 0.75 0.95
1.3
Parameter
Compactness
Position (mm)
Force setpoint (N)
Mean value (kPa)
Deviation (kPa)
Relative error (%)
/ / / / / 285 310 285 260 285
1600 1000 1600 2200 2200 / / / / /
435 271 396 475 328 485 280 427 503 372
28.5 25.6 31 35.6 41.3 86.9 62.6 75.7 95.6 98.8
6.55 9.44 7.82 7.49 12.6 17.9 22.3 17.7 19.0 26.5
tendency with the increase of compact force, and the increase rate was gradually decreased. Therefore, it was a feasible way to obtain the soil compaction by measuring the compact force acting on roller. Force sensors were mounted on the soil preparation machine to measure the compact force during operation. The paper proposed to adjust the compaction depth in real time by using a method combining fuzzy calculation and statistical analysis. And the soil compactness could be controlled by changing the setting compact force. Soil preparation performance tests were carried out in field. Compared with the traditional working method using constant compact depth, the proposed control method could significantly improve the uniformity of soil compaction, which was helpful for providing a good growth environment for crops.
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Declaration of Competing Interest We declare that we have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. There is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled, “Control method of seedbed compactness based on fragment soil compaction dynamic characteristics”. Acknowledgements This work is supported by the National Natural Science Foundation of China (No. 51775246) and a Priority Academic Program Development of Jiangsu Higher Education Institutions (No. PADP2018-87). References Alaoui, A., Diserens, E., 2018. Mapping soil compaction – a review. Curr. Opin. Environ. Sci. Health 5, 60–66. Ani, O.A., Uzoejinwa, B.B., Ezeama, A.O., Onwualu, A.P., Ugwu, S.N., Ohagwu, C.J., 2018. Overview of soil-machine interaction studies in soil bins. Soil Tillage Res. 175, 13–27. Arvidsson, J., Bölenius, E., Cavalieri, K.M.V., 2012. Effects of compaction during drilling on yield of sugar beet (Beta vulgaris L.). Eur. J. Agron. 39, 44–51. Atkinson, B.S., Sparkes, D.L., Mooney, S.J., 2007. Using selected soil physical properties of seedbeds to predict crop establishment. Soil Tillage Res. 97, 218–228. Batey, T., 2009. Soil compaction and soil management – a review. Soil Use Manage 25, 335–345. Bravo, E.L., Tijskens, E., Suárez, M.H., Cueto, O.G., Ramon, H., 2014. Prediction model for non-inversion soil tillage implemented on discrete element method. Comput. Electron. Agric. 106, 120–127. Defossez, P., Richard, G., 2002. Models of soil compaction due to traffic and their
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