Design and Experimental Study of Key Components of the Samara-Hulling Machine for Eucommia ulmoides Oliver

Abstract

In this article, a hammer-blade hulling machine for Eucommia ulmoides Oliver that solves the current industry problem of low hulling efficiency and high manual input in EUO samaras is described. Its main working components are a hulling device and a screening device. Discrete element simulation was used to simulate the hulling process of a EUO samara hulling machine, and a EUO samara bond model was used to simulate the crushing process. The optimal parameters of the huller were determined as follows: the spindle speed was 2800 r/min, the hammer length was 70 mm and the other mechanism parameters were determined according to the working processes of components. Before the prototype test, EUO samaras were pretreated via soaking and insolation. The soaking and insolation times were used as the influence factors when carrying out the test. Their effect on hulling efficiency was evaluated by calculating the yield rates of the kernels and shell and the loss rate. The results show that under the optimal pretreatment conditions, the parameters of the huller meet the requirements, and the yield rate of kernels is more than 28%, the yield rate of shells is more than 38%, and the loss rate is less than 7%. The test indexes meet the use requirements and improve the efficiency of the hulling of the EUO samara, which has the advantages of high efficiency and high hulling rates.

1. Introduction

Eucommia ulmoides Oliver (EUO) is a plant of the Eucommia genus in the Eucommiaceae family [1]. It is a living fossil plant unique in China, first reported in Nong Ben Cao Jing. Its leaves, bark and male flowers have been used as effective traditional Chinese herbal medicine for thousands of years [2]. EUO is very resistant to its environment and can be grown over a large area, with a vertical distribution generally located at altitudes of 300–1500 m in China, while it is also grown in small quantities in Japan, Taiwan and Korea. In recent years, EUO has been widely used in medicine, food, the military industry, industrial raw materials and construction materials [3,4]. However, EUO trees are mainly located in loess hills and ravine areas where mechanized harvesting is difficult, which results in high harvesting costs associated with EUO resources. In order to improve the yield and overall benefits of EUO, the Chinese National Forestry and Grassland Administration established the National Eucommia Industry Development Plan (2016–2030), which calls for further expansion of EUO tree plantations on an existing basis, bringing the planted area of EUO trees up to approximately 300 hectares by 2030. In particular, EUO trees will be grown in Shaanxi, Sichuan, Yunnan, Guangxi, Zhejiang, Guizhou and other provinces of China, including subtropical and temperate regions [5,6].

Generally, EUO is a deciduous and dioecious tree, and the females bear fruit. The fruit has a dark brown flattened elliptical samara at maturity [7,8]. The EUO samara is of flattened poke-shaped structure or linear structure, about 29.2–39.6 mm long, 8.8–12.5 mm wide, 1–3 mm thick and with a fruit shape index between 2.6–3.8 [9]. Usually, the EUO samara starts to grow in April each year, is harvested in November and has a yield that can reach around 2000 tons every year [10].

Both the shells and kernels of EUO samaras have a high development value, among which the kernels are rich in EUO seed oil, and the content of EUO seed oil is nearly 36%. The major polyunsaturated fatty acids are a-linolenic acid (56.51% of total fatty acids) and linolelaidic acid (12.66% of total fatty acids) [11]. Particularly, α-linolenic acid is an essential fatty acid in the human body and has the therapeutic effects of lowering blood pressure, improving memory, lowering blood lipids and preventing platelet clotting [10,12]. Moreover, the shell is rich in EUO rubber, which is a polymer substance with a structure similar to the molecular structure of rubber. However, EUO rubber has differences in the cis and trans of its chemical structure compared to rubber [13]. EUO rubber is also known as one of the natural sources of rubber, which is highly valuable for sustainable development and applications. EUO rubber has a very wide scope of development mainly in the fields of industrial raw materials, medical devices and construction materials [14,15,16]. At present, studies of EUO mainly focus on the bark, leaves, male flowers and stems [17]. Some studies on the EUO samara also focus on EUO seed oil and EUO rubber, and there are a few studies on the hulling of EUO samaras and the separation of the shell and kernel (Figure 1). As the EUO samara is particularly small, it is difficult to hull, which greatly limits the development and industrialization of EUO seed oil and EUO rubber. Therefore, the hulling and separation of the kernels and shells of EUO samaras has become a significant topic of continued research in the field of the EUO industry.

Currently, common hulling devices are mainly divided into two types: one is the extrusion-type hulling device, which usually functions through opposing rollers that rotate and cause squeezing pressure to break the shell material and achieve hulling [18]; the other is the centrifugal hulling machine, which usually uses a high-speed rotating mechanism to produce a large impact force that breaks the material to complete hulling [19]. A low shelling rate and high kernel crushing rate are the main problems of current hulling machines. Li et al. [20] developed a novel vibrating screen via a numerical simulation and theoretical analysis. This screen changes the air velocity and air pressure distribution on the screen surface, so that the particles can be separated to the maximum extent. Chu et al. [21] carried out experiments and simulations on the effects of impact velocity, impact angle and grain orientation on hulling performance. They clarified the mechanism of impact from the hulling of paddy grains, showing that the impact speed and impact angle have significant effects on the grain hulling and crushing rates. Lim et al. [22] developed a hulling machine with integrated multistage cracking units via multiphase modeling using computational fluid dynamics (CFD); this device does not cause significant kernel loss during the hulling process. Arifuzzaman et al. [23] developed a new process model for vibrating screens by combining discrete element method simulation and physics-informed machine learning; this model would be helpful for the smart design and control of industrial screens and other similar particle classification processes. The above research provides very valuable ideas for the design of hulling machines for use on juniper berries. The following should be pointed out: (1) as a special material, the EUO samara needs to be clearly distinguished from common materials to develop a targeted hulling and separation scheme; (2) to date, there is a lack of research on the establishment of a discrete element model for the EUO samara; (3) in addition, the influence of the relevant parameters of the hulling machine on the hulling effect of the EUO samara is unclear and needs further study.

In the field of EUO samara-hulling machines, due to the dense nature of EUO samara shells’ rubber filaments, extrusion-type hulling machines do not have impactful performance because it is difficult for these machines to destroy the rubber filaments. Therefore, for EUO samara-hulling machines, a centrifugal-type hulling machine is a better choice. It can produce a large impact force during operation to achieve the crushing of the shell as well as the rubber filaments; however, because of the large impact, it produces the problem of high-rate kernel crushing. In addition, there is no obvious density difference between the shells and kernels of EUO samaras, and so the separation problem is not effectively solved.

In this study, a hammer-blade-type EUO samara-hulling machine was developed by numerical simulation and theoretical analysis, combined with the material characteristics of the EUO samara, and the design of key parameters of the hulling machine was completed. The particle filling and Hertz–Mindlin models combined with the bonding contact model were used to establish a discrete element model of EUO samaras, and the key parameters of the hulling device were optimized based on the discrete element simulation. The effects of the spindle speed and hammer-blade length on the hulling effect of the hulling device were also investigated. The key parameters of the screening device were designed through theoretical analysis. In addition, the pretreatment method prior to hulling EUO samaras was explored and a prototype test was conducted to verify the feasibility of the design of the EUO samara-hulling machine, and a statistical analysis of the hulling rate and loss rate were carried out. The results show that the equipment can well assist in the industrialization of EUO.

2. Machine Mechanism and Working Principle

2.1. Whole Structure of EUO Samara-Hulling Machine

The samara-hulling machine is composed of a hulling device, a screening device, a transmission system and other main components, among which the hulling device and screening device are the main working components. The structure of the whole machine is shown in Figure 2a, and the main technical parameters are shown in

Table 1. The main technical parameters.

Parameter	Value
Dimensions	1850 mm × 700 mm × 1250 mm
Drive type	Belt drive
Motor power	4 kW
The yield rate of shells	≥38%
The yield rate of kernels	≥28%
The rate of loss	≤7%

.

2.2. Working Principle of EUO Samara-Hulling Machine

During operation, the motor of the hulling device is connected to the principal axis of the hulling device through the belt drive, which drives the hammer to rotate at high speed. Then the hammer beats the samara in the shucking device, and through the effect of hammer force, the shell of the EUO samara is smashed, and after completing the shucking work, the broken samara falls into the screening device. Meanwhile, the motor of the hulling device is connected to the eccentric wheel through the belt drive, driving eccentric wheel rotation, forming a crank-connecting rod mechanism and driving the vibrating screen work. The fan is driven by the motor of the hulling device to rotate and blow the vibration sieve to move and disperse the crushed samara. Combined with the difference in the densities of kernel and shell after the pretreatment of the samaras, the screening work is completed.

3. Hulling Device Design of EUO Samara-Hulling Machine

The hulling device is shown in Figure 2b and is mainly composed of flanges, brackets, hammers and a principal axis. The hammers are evenly distributed on one bracket with a spacing of 21 mm, and the hammers on the adjacent bracket are staggered. When the samara enters the hulling device, the principal axis of high-speed rotation drives the bracket to rotate, and the hammer swings under the action of centrifugal force to hammer the EUO samara into the hulling device, so that the shell of the samara is broken, and the separation of shell and kernel is completed.

3.1. Establishment of DEM Geometrical Model

The discrete element method (DEM) is a numerical method used in the dynamics of complex discontinuous systems. The DEM is suitable for the study of mechanical problems in blocky assemblies or nodal systems under quasi-dynamic and static conditions and is widely cited in the areas of particle flow, powder mechanics, rock mechanics and comminution [24]. The DEM has gradually become an effective means of studying engineering problems such as granular materials, reflecting the overall pattern of particle motion and mechanical properties from a macroscopic perspective by means of particle tracking calculations [25].

Fundamentally, the DEM is an application of Newton’s second theorem that uses relevant iterative analysis methods for the alternate calculation of total systems, describing the physical state of particles by the linear velocity at each point within particles and the angular velocity of particles, usually by integrating over time to complete the calculation of the forces and, thus, the prediction of the material-related response. Applications commonly considered for the DEM are discussed in the context of crushing-based problems [26]. In this section, we simulate the crushing process of the hulling device by the DEM and analyze the effect of structural parameter variation on the hulling quality and efficiency to obtain the optimal structural parameters.

3.2. Contact Model

The contact between particles is modeled using Hertz’s theory in the normal direction and a modified nonlinear model of Mindlin‘s no-slip model in the tangential direction [27]. Hertz’s elastic contact theory provides a tight relationship for the normal direction, which is obtained by integration of the normal pressure distribution over the contact area [28]. The resulting normal-phase contact force $\left(F_n\right)$ is a function of the normal overlap (${\delta }_n$) [29]:

$$F_n=-K_n{\delta }_n+C_n{v}_n^{rel}$$

(1)

All parameters are defined in

Table 2. Spring stiffness and damping coefficients used in the contact model.

	$\mathbf{Normal} \mathbf{Direction} \left(\mathit{n}\right)$	$\mathbf{Tan}\mathbf{gential} \mathbf{Direction} \left(\mathit{t}\right)$
$\mathrm{Spring} \mathrm{stiffness} \mathrm{constant} \left(K\right)$	$K_n=\frac{4}{3}{E}^{*}\sqrt{R^{*}{\delta }_n}$	$K_t=8G^{*}\sqrt{R^{*}{\delta }_n}$
$\mathrm{Damping} \mathrm{coefficient} \left(C\right)$	$C_n=2\sqrt{\frac{5}{6}}\beta \sqrt{S_n{m}^{*}}$	$C_t=2\sqrt{\frac{5}{6}}\beta \sqrt{K_t{m}^{*}}$
$S_n=2E^{*}\sqrt{R^{*}{\delta }_n}$$, \beta =\frac{\ln \epsilon }{\sqrt{{\ln }^2\epsilon +{\pi }^2}}$

[30]. This provides an elastic repulsive force to push away the particles to be collided with and a damping system to counteract some of the relative kinetic energy generated by the collision.

In the tangential direction, the possible force–displacement configurations depend on the normal and tangential loading histories. The relative tangential velocity generated by the relative tangential motion during a collision is expressed as an incremental spring of stored energy, which represents the elastic tangential deformation of the contact surface. The dampers dissipate energy from the tangential motion and simulate the tangential plastic deformation of the contact. The damping force $\left(F_t\right)$ in the tangential direction is given by the following equation [31]:

$$F_t=min\left\{\mu F_n,K_t{\delta }_t+C_t{v}_t^{rel}\right\}$$

(2)

All parameters are defined in Table 2 [30], where $E^{*}$, $R^{*}$, $G^{*}$ and $m^{*}$ are the Young’s modulus, equivalent radius, shear modulus and mass, respectively.

DEM codes for crush modeling typically use linear spring and damper models in the normal and tangential directions and friction sliders in the tangential direction or change direction [32]. Due to its simplicity, this form of model is suitable for tracking the motion and total dynamics of a large number of particles. The energy dissipated in the collision is captured in the damper, allowing it to be interpreted and recorded for later analysis in the crush model. However, the idealized nature of the contact model does not allow for a detailed analysis of the contact events and, in fact, the output should not be over-interpreted in a physical sense. In contrast, the output should provide the rates and frequencies, distributions and energies of the collision events in a practical way.

The bonding model uses a dense accumulation of spherical particles of arbitrary size that are bonded together at the point of contact and simulated with the DEM [33]. This model has been used to simulate rocks as part of a direct particle model approach to simulating the inelastic deformation and fracturing of rocks [34]. The bonded particle model (BPM) of rocks directly simulates the behavior of rocks like complexly shaped particles and cemented granular material, where both the particles and the cement are distortable and may be destroyed [35]. In principle, the conceptual BPM can explain all aspects of the behavior of the mechanism. The system is very accurate in predicting sudden behavior between particles and corresponds well to real application scenarios. It offers the possibility to study the microscopic mechanisms that generate these complex macroscopic behaviors and to predict them in DEM [36].

The particle-based part of the force–displacement behavior at each contact is described by six parameters, and a detailed description of the contact force–displacement calculation is provided in Figure 3a [30]. According to the theory of beams, the formulae for the maximum tensile and maximum shear stresses acting on the parallel bonded edges are as follows [37];

$${\overline{\sigma }}_{max}=\frac{-{\overline{F}}_n}{A}+\frac{\left|{\overline{M}}_s\right|\overline{R}}{I}$$

(3)

$${\overline{\tau }}_{max}=\frac{-{\overline{F}}_s}{A}+\frac{\left|{\overline{M}}_n\right|\overline{R}}{J}$$

(4)

where ${\overline{F}}_n$ and ${\overline{F}}_s$ and ${\overline{M}}_n$ and ${\overline{M}}_s$ are the axial- and shear-directed forces and moments, respectively. Additionally, A, I, J and R are the area, moment of inertia, polar moment of inertia of the parallel bond cross-section and particle radius. If the maximum tensile stress exceeds the tensile strength $\left({\overline{\sigma }}_{max}\ge {\overline{\sigma }}_c\right)$ or the maximum shear stress exceeds the shear strength $\left({\overline{\tau }}_{max}\ge {\overline{\tau }}_c\right)$, the parallel bond breaks and is removed from the model along with its accompanying forces, bending moments and stiffness. In general, the BPM is characterized by particle density, particle shape, particle size distribution, particle stacking and particle–cement microscopic properties. Each of these affects the behavior of the model.

3.3. Particle Model

In the process of modeling the EUO samara, in order to obtain an accurate profile of the EUO samara and improve the accuracy of the simulation, this study used three-dimensional scanning technology for the three-dimensional modeling of EUO samaras. Then, a box-shaped geometry was established in DEM software, and the processed EUO samara model was imported into its internal area and set as virtual-type. A large number of particles were generated inside the box to fill the model. After filling, the model was set as physical-type. When the box type is changed to virtual, the excess particles leave the calculation domain and disappear automatically under the action of gravity. At this point, the internal particle coordinates of the EUO samara model are derived, and the derived coordinates are used to generate a discrete element model of the EUO samara with bonding bonds by particle replacement. Considering the analysis of samara breakage and computer simulation time, it is necessary that filling particles are selected at the appropriate size when performing particle replacement. If the filling particles are too large, the crushing effect decreases. On the contrary, if the filling particles are too small, the simulation time is too long. The model is imported into DEM and filled with spheres with radius of 0.5 mm to form a discrete model of EUO samaras. Each samara was filled with 444 spheres. BPM models were generated according to the measured parameters of EUO samaras, and the spheres formed a whole through bonds, as shown in Figure 3b.

3.4. Simulation Parameter Determination

According to the requirements of simulation, the hammer in the hulling device was endowed with alloy steel. The mechanical properties and physical properties of EUO samaras and alloy tool steel are shown in

Table 3. Physical properties of EUO samaras and alloy steel.

Parameter	EUO Samara	Steel
Poisson’s ratio	0.403	0.27
Young’s modulus (Pa)	1.998 × 107	2.09 × 109
Density (g/cm−3)	0.67	7.8
Coefficient of restitution	0.373	0.335
Coefficient of Static Friction	0.676	0.442
Coefficient of Rolling Friction	0.349	0.254

[38]. The time step in DEM was set as 10% ($8.82\times {10}^{-7}$ s), and the time step mainly affected the simulation accuracy and simulation time. The larger the time step is, the shorter the simulation time is and the lower the accuracy is. In the crushing simulation, if the time step is too large, a complete bond model cannot be formed. On the contrary, the smaller the time step is, the longer the simulation time is. Therefore, a smaller time step should be selected in the crushing simulation. In order to extract the motion information of particles in as much detail as possible, the data were saved every 0.01 s in the DEM. Finally, the bonding parameters were adopted from the results of previous research on the calibration of the parameters of EUO samaras, as shown in

Table 4. Bond parameters of EUO samaras.

Parameter	Value
The normal stiffness per unit area (N/m3)	3.200 × 107
The shear stiffness per unit area (N/m3)	3.834 × 107
The critical normal stress (Pa)	4 × 106
The critical shear stress (Pa)	4 × 106
The bonded disk radius (mm)	0.83

[38].

3.5. Numerical Model of Crushing Process

In the simulation process, a discrete element model of EUO samaras was generated by particle replacement in DEM software, and then the BPM bond model was added to the particles immediately for solid bonding. Due to the huge amount of data needed to be processed in the simulation process, in order to reduce the simulation calculation time, only 10 discrete element models of EUO samaras were generated in the simulation process, as shown in Figure 3c.

3.6. Simulation Process

In order to achieve the best hulling effect of EUO samaras, it is necessary to explore the optimal values of hammers’ structural parameters and device operating parameters. In the simulation process, the change in bond number is taken as the index to analyze the influence of each parameter change on the hulling effect. Regarding the key parameters to determine, considering the gap between the hammer blade and the machine shell during the crushing process, if the gap is too large, the material directly slides away, and if the gap is too small, the machine is clogged, so the hammer blade length was set to 60 mm, 70 mm and 80 mm in the simulation process. In addition, the spindle speed is the main factor affecting the hammer impact force; if the spindle speed is too high, it produces a large impact force, so that the samara kernel breakage rate increases; if the spindle speed is too low, the impact force is not enough to break the rubber filaments’ structure and the shelling rate is low, so the spindle speed was set to 2400 r/min, 2800 r/min and 3200 r/min in the simulation process.

In the simulation process, no special settings were made for the way the material falls, so that the material was subjected to free-fall motion, and particle replacement was carried out 0.2 s after the start of the simulation to generate the BPM model of EUO samaras, while the spindle started to rotate. When the material came into contact with the hammer blade, the bond in the BPM model of EUO samaras started to be crushed, and the crushed material entered the collection bin at the outlet of the hulling device.

3.7. Simulation Results and Analysis

Figure 4 shows the simulation results of the hulling device with different hammer lengths and principal-axis speeds.

By comparing the variation curves of the number of bonds obtained by simulation under different hammer lengths, the number of bond breakages is the largest at a 70 mm hammer length (Figure 4b). When the hammer length is 60 mm or 80 mm, the number of bond breakages is significantly reduced (Figure 4a,c), reflecting the lower shelling rate of EUO samaras under this condition. Through the analysis of the whole crushing process, it is found that some materials were not hit by the hammer in the hulling device and slid away from the gap at a 60 mm hammer length, resulting in the bonds not being destroyed. Furthermore, as the hammer length increases to 80 mm, the material pile ups and bounces, resulting in a reduction in the number of bond breakages. In order to ensure the crushing effect and to avoid material piling up and slipping away, a hammer head length of 70 mm is the best.

After determining the hammer length to be 70 mm, the simulation was performed again by changing the principal-axis speed.

Comparing the variation curves of the number of bonds simulated at different principal-axis speeds, when the principal-axis speed is 2800 r/min, the number of bond breakages is the largest (Figure 4b). When the principal-axis speed is 2400 r/min, the hammer cannot obtain enough potential energy, so the number of bonds does not change significantly (Figure 4d). When the principal-axis speed is 3200 r/min, because the principal-axis speed is too high, the material directly flies out, resulting in no significant change in the number of bonds (Figure 4e), so the principal-axis speed of 2800 r/min is the best.

Therefore, through simulation analysis, a hammer length of 70 mm and a principal-axis speed of 2800 r/min are the optimal hulling parameters of the device.

4. Screening Device Design of EUO Samara-Hulling Machine

The screening device is composed of a two-stage vibrating screen, an eccentric wheel, a connecting rod and a fan. The structure diagram is shown in Figure 2c. After the broken EUO samara enters the screening device, most shells are screened out of the hulling device under the action of the fan because of the low density and light weight of the shells, and the remaining shells and kernels fall onto the vibrating screen. The eccentric wheel is driven by the motor to rotate at high speed. Under the action of the eccentric wheel, the rotation is transformed into the swing of the connecting rod, thus driving the reciprocating motion of the vibrating screen. Under the action of the vibrating screen, kernels with low volume and mass fall into the bottom of the hulling machine, while the remaining shells are screened out of the hulling machine with the reciprocating motion of the vibrating screen.

In order to make better use of the two-stage vibrating screen to separate the EUO samaras and provide the kernels with more chances to fall into the sieve hole, it is necessary to ensure that the shells slide inward and outward along the screen, the distance of sliding outward should be greater than the distance of sliding inward, and the shells should not be thrown off the screen surface. The main factors affecting this process are vibrating screen acceleration $w^{2}r$, vibrating screen tilt angle α, etc. A larger $w^{2}r$ yields higher productivity, but if it is too large, the velocity of the EUO samaras across the screen surface is too high, which may lead to them passing over the screen holes and reduced separation performance. In order to ensure the separation effect, according to the “agricultural machinery design manual”, the vibrating screen acceleration $w^{2}r$ generally takes a value of 2~2.5 $\mathrm{g}$, crank radius $r$ generally takes a value of 23~30 $\mathrm{mm}$, and the vibrating screen tilt angle α take a value of 5~15°.

According to the structure and layout requirements of the machine, we set the vibrating screen acceleration $w^{2}r$ = 2 $\mathrm{g}$, the crank radius $r=30 \mathrm{mm}$ and the vibrating screen tilt angle $\alpha =10 °$; thus, the crank speed is:

$$w=\sqrt{\frac{2g}{r}}=25.82 \mathrm{rad}/\mathrm{s}$$

(5)

According to the speed calculation formula, the eccentric speed is:

$$n=\frac{60w}{2\pi }=246.69 \mathrm{r}/\min$$

(6)

taking the eccentric speed as 250 r/min.

During the work of the screening device, it is required that the mixture of the EUO samaras on the screen surface is in a loose state, which is conducive to separation. Therefore, the vibrating screen and the fan should be well matched, so that the shells can be blown away and the kernels can be screened out by the vibrating screen. In this way, fan airflow and the screen surface at a suitable angle improve the screening effect of indispensable conditions. This angle results in a larger blowing area and the impact of airflow diffusion can better meet the screening needs, according to the “agricultural machine design manual” screening device fan and vibration screen configuration relationship, as shown in Figure 2d. The fan outlet should be to the front of the screen; its height S and blowing direction angle $a$ should meet the following relationship:

$$S=KLsina$$

(7)

In the formula, the coefficient K takes a value of 0.4~0.6, L is the full length of the screen and $a$ is the angle between the fan airflow and the screen surface, generally 25~30°.

According to the layout and space requirements of the whole machine, substituting the length of the vibrating screen L = 1000$\mathrm{mm}$, $K=0.5$, fan airflow and screen surface angle $a$ = 30° into the upper formula allows us to obtain the height of the blower mouth $S=250 \mathrm{mm}$.

5. Verification Experiment

According to the simulation results and design results, relevant hulling experiments were carried out on a trial-produced prototype (Figure 5).

5.1. Materials and Methods

In this experiment, 100 EUO samaras were randomly selected, separating the shells and kernels. This was repeated six times as shown in

Table 5. EUO samara characteristics.

Number	Hundred Grains Weight (g)	Kernel Weight (g)	Proportion (%)	Shell Weight (g)	Proportion (%)
1	7.1321	2.5097	35.16	4.6103	64.64
2	7.2587	2.4755	34.10	4.7781	65.83
3	7.3371	2.5781	35.14	4.6924	63.95
4	7.4118	2.6503	35.76	4.7399	63.95
5	7.4106	2.5327	34.17	4.8773	65.82
6	7.2952	2.5826	35.40	4.7093	64.55
Average	7.3076	2.5548	34.96	4.7346	64.79

. The average 100-seed weight of EUO samaras was 7.3076 g, the proportion of kernel weight was 34.96%, and the proportion of shell weight was 64.97%.

Considering the properties of the EUO samara, which is rich in EUO rubber, and the working properties of the hammer-blade hulling device, it was necessary to pretreat the experimental material before the experiment by soaking and drying in the sun. The EUO samaras were soaked for 6 h, 12 h, 18 h and 36 h, and dried in the sun at a temperature of about 35 °C for 1 h, 3 h and 5 h. The EUO samara samples were then hulled.

5.2. Experiment Evaluation Index

To measure of the moisture content (ω) of the EUO samaras, the pretreated samaras were randomly weighed and dried in an oven until the weight reached a constant value. The mass before drying is denoted as m₁, and the mass after drying is denoted as m₂. The calculation method is as follows:

$$\omega =(m_1-m_2)/m_2\times 100\%$$

(8)

The yield rate of shells (Y₁) is defined as the ratio of shells harvested to the total weight of the EUO samaras. In order to measure Y₁, we collected and weighed the separated EUO samara shells after hulling. The mass of shells is denoted as $m_3$, and the calculation method is as follows:

$$Y_1=m_3/m\times 100\%$$

(9)

In the formula, $m$ is the total weight of the EUO samaras.

The yield rate of kernels (Y₂) is defined as the ratio of kernels harvested to the total weight of the EUO samaras. In order to measure Y₂, the kernels screened out after hulling were collected and weighed, and the mass of kernels was denoted as $m_4$, and the calculation method was as follows:

$$Y_2=m_4/m\times 100\%$$

(10)

The rate of loss (Y₃) is defined as the difference between the average kernel weight ratio of the EUO samaras and the rate of kernel.

$$Y_3=\phi -Y_2$$

(11)

In the formula above, $\phi$ is the average kernel weight ratio of the EUO samaras, and Y₂ is the yield rate of kernels.

5.3. Experimental Results

Figure 6 shows the statistical results of experiments under different pretreatment conditions for the EUO samaras. With an increase in soaking time, the moisture content of the EUO samaras was increased. After drying in the sun, the evaporation of water was mainly concentrated on the shell, and the shell became brittle when the moisture content of the shell decreased, which made the hammering effect better. Additionally, the moisture content of the dried kernels and shells were significantly different, as the higher moisture content of the kernels made the density of the kernels greater than that of the shells, facilitating the selection of shells and kernels after hulling. Furthermore, the high moisture content allowed the kernels to acquire a certain toughness, which can reduce kernel breakage during the hammering process to some extent.

As the soaking time increased, the yield rate of kernels first increased and then decreased. After soaking for 12 h and insolating for 5 h, the kernel yield rate reached a peak of 29.73%. When the soaking time is less than 12 h, the EUO samaras do not absorb sufficient water and have low water content. After drying, there is no obvious difference in the water content of the EUO samara shells and kernels, and after hulling is completed, the shells and kernels are mixed to a high degree, making them difficult to screen and collect. In addition, the kernels become brittle with low moisture content and are easily hammered into powder during the hulling process, resulting in a lower kernel yield and higher loss rate. When the soaking time is greater than 12 h, EUO samara shells still contain a lot of water after drying, the water content is high and the toughness of the shell increases, while under the action of the rubber filaments, most of the shell is connected in a flocculent form and the kernel is wrapped in the shell, leading to a decrease in the kernel yield and an increase in the loss rate. Furthermore, excessively long soaking times not only prolong the hulling cycle but also affect the quality of the kernels.

After 12 h of soaking, with an increase in drying time, the kernel yield showed a trend of first increasing and then decreasing and reached the peak at 5 h of drying time. When the drying time was less than 5 h, the moisture content of both EUO samara shells and kernels was high; not only were they difficult to crush, but the shells and kernels were also significantly mixed, and there was greater difficulty in screening, which resulted in a lower kernel yield and higher loss rate. When the drying time exceeds 5 h, the overall moisture content of EUO samaras is seriously reduced, resulting in overall brittleness of samaras and easy breakage of the samaras into powder, which results in a simultaneous decrease in kernel and shell yields and an increase in the loss rate.

Under proper pretreatment conditions, the rate of kernel release was 28~30%, the rate of shell release was 38~43% and the rate of loss was 5~7%. In general, the whole machine has high hulling efficiency and good kernel yield rate, which can satisfy the hulling application of EUO samaras.

5.4. Economic Assessment

Table 6. The operating costs of the different hulling methods.

Hulling Methods	Weight of EUO Samaras (kg)	Operating Time (h)	Electricity Consumption (kWh)	Electricity Price (CNY/kWh)	Depreciation Per Unit Time of Equipment (CNY/h)	Labor Costs (CNY)	Rate of Kernel Removal (%)	Operating Costs (CNY/(Rate of Kernel Removal/h))
Mechanical operation	10	0.12	0.467	0.234	0.076	50	29	20.818
Manual operation	10	674	/	/	/	17,000	35	32,737,142.860

shows the operating costs of the different hulling methods for the yield rate of kernels by operating period. The rate of kernel release for the manual method was slightly greater than that of the mechanical method. However, the mechanical method was significantly better than the manual method from the perspective of economic feasibility and efficiency; the reason for this was that the operating time of the mechanical method was shorter and the labor costs were lower for the same weight of juniper berries, and both were much smaller than the manual method. In conclusion, the mechanical method is more valuable for industrialization from an economic point of view.

6. Conclusions

In this study, a hammer-blade-type EUO samara-hulling machine was developed by numerical simulation and theoretical analysis, combined with the material characteristics of EUO samara. Simulation analysis using discrete element software was used to investigate the influence of the mechanism parameters of the shelling device on the shelling effect. The simulation showed that the hammer-blade length and spindle speed had significant influence on the shelling effect, and the optimal parameters were selected with reference to the simulation results as a hammer-blade length of 70 mm and a spindle speed of 2800 r/min. The optimal pretreatment method for EUO samaras was explored by pretesting with 12 h of soaking and 5 h of drying in the sun. A prototype test was conducted on the pretreated samaras to achieve the highest kernel yield and shelling rates and the lowest loss rate. The results of the trial were a kernel yield greater than 28%, a hulling rate greater than 38% and a loss rate of less than 7%, with good prospects for application in EUO industrialization.