Experimental Study on the Peeling Fracture Effect of Fresh Corn Ear Based on High and Low Roller Peeling Equipment

Abstract

Aiming to address the problems of low working efficiency and high damage rate of high and low roller peeling equipment in the process of fresh corn harvesting in China, this paper theoretically analyzes the mechanical motion process between the peeling device and the corn ear, and a high–low roll peeling structure is proposed. This structure incorporates elastomeric rubber material, a roller segmentation design, and an adjustable spiral frame, and the selection of relevant parameters is given. To determine the optimal operating parameters for the fresh-corn-peeling device, a three-factor, three-level orthogonal test was conducted using the Box–Behnken central grouping method in Design-Expert 12 software. The test factors were peeling roller speed, peeling roller tilt angle, and vibrating plate frequency. The evaluation indices considered were the bract peeling rate (BPR) and the grain breaking rate (GBR). Based on the theoretical analysis results, a test bench for the fresh-corn-ear-peeling device was established and the parameter combination for optimal peeling quality was determined according to the actual work situation. The results show that the impact on the BPR and GBR, from large to small, is in the following order: peeling roller speed, peeling roller tilt angle, and frequency vibration plate. The optimization module was used to optimize the operating parameters and used the following integers to obtain the optimal operating parameter combination: the peeling roller speed was 480 r·min⁻¹; the peeling roller tilt angle was 8°; the vibrating plate frequency was 260 times·min⁻¹; the corresponding BPR was 91.75%, which was 0.66% points lower than the optimal value; and the GBR was 1.55%, which was 0.08% points higher than the optimal value. Notably, this fresh-corn-peeling equipment exhibited superior performance in terms of peeling fracture results compared with standard peeling equipment. Therefore, this study provides valuable technical support for the optimal design and selection of fresh-corn-peeling equipment.

1. Introduction

Fresh corn, also known as fruit corn, is a fresh ear that is picked at the milk stage, and it can be used not only as grains but also as vegetables since it is rich in protein, carbohydrates, and other nutrients [1,2]. Corn is planted widely around the world, and it has become an important economic pillar in many countries and regions [3,4,5,6,7,8,9]. At present, the total planted area of fresh corn exceeds 1.34 million hectares in China, which is currently ranked first in the world, and the northeastern provinces, Hainan and Sichuan, are the main principal producers [10,11,12]. Peeling is the most labor-intensive part in the process of fresh corn harvest. Manual peeling has the disadvantages of high labor intensity, low speed, and low efficiency, but it is still the mainstream operating mode in China. Due to natural conditions and the regional growing environment, the harvest period of fresh corn is tightly timed. This results in a tight peeling time and a heavy task at harvest, and it directly affects the next process and leads to lower economic benefits for fresh corn [13,14]. Mechanized peeling has the advantages of high efficiency, low cost, and low labor intensity, and it is the inevitable trend of the corn industry [15].

In China, researchers have conducted lots of theoretical analyses and experimental studies on peeling crops. Zhao et al. [16] designed rollers with a surface pattern and carried out experimental studies on the speed of the rotating rollers, the material of the rotating rollers, the type of motor, and the size of the rotating rollers to optimize the structure parameters. They achieved efficient and accurate peeling of fresh corn ears. Liu et al. [17] designed some key components based on the Theory of the Solution of Inventive Problems (TRIZ theory) and analyzed the motion process and the force conditions of the ear using the LS-DYNA dynamic simulation software, and then, the key components were verified via experiments to obtain a better peeling result. Yang et al. [18] established a high-speed camera system based on the Hertz theory. They created a segmented design for a peeling roller on account of the characteristics of the ear and analyzed the design via finite element simulations and experiments to test different peeling situations, and the peeling effect was good. In addition, in terms of foreign researchers, Kedge et al. [19] designed various components of an improved peeling machine, optimized the extracted parameter values, and selected suitable materials to effectively reduce the grain breaking rate (GBR) and to increase the bract peeling rate (BPR) to achieve the desired goal. In the study conducted by Gorad et al. [20], a theoretical approach was employed to enhance and optimize the analysis of key components. The researchers successfully validated their findings by subjecting the redesigned components and assemblies to rigorous experimental testing under extreme loading conditions. Through a systematic and methodical approach, they evaluated various parameters to ensure that the redesigned components and assemblies met the expected performance requirements. The experimental results confirmed the effectiveness and reliability of the proposed improvements. The previous research findings utilized a rolling friction mechanism that was not completely suitable for efficient debarking of fresh corn. Consequently, it led to a high GBR and a low BPR, failing to meet the desired criteria. To tackle this problem, a comparative analysis of the mechanical properties of fresh water caltrop and corn was conducted [21,22]. The study unveiled a significant adhesion between the bracts and kernels of fresh corn, further compounded by the high moisture content. Therefore, the standalone friction method does not yield satisfactory results.

In this study, we analyzed the force conditions between the fresh corn ear and the peeling device to determine the structure of the peeling equipment composition and made a high and low roller peeling equipment test bench. The experiment was conducted in a testing facility to investigate the effect of different conditions of the peeling roller speed, the peeling roller tilt angle, and the frequency vibrating plate on the peeling result, and the BPR and GBR were chosen as the evaluation indicators. Then, we optimized the peeling parameter values based on the experiments. The results indicated that the BPR was greater than 90%, while the GBR was less than 2%. This study can not only provide an indication for the existing fresh corn harvester but also provide a theoretical basis for the optimized design of fresh corn peeling equipment.

2. Structural Design and Working Principle of the Whole Machine

The high and low roller peeling equipment for fresh corn is composed of a base, a frame, a spiral adjusting frame, a peeling roller mounting frame, and a transmission mechanism, in which the length of the frequency vibration plate is parallel to and equal to the length of the peeling roller, as shown in Figure 1.

During the operation, ears entered into high–low rollers from the feeding inlet at a certain initial speed, and the rollers rotated in opposite direction with a certain inclination angle, while the frequency vibrating plate vibrated periodically. Two rollers generated two different tangential friction and acted at the ears. The ears slid down the rollers in the effect of their own gravity and the movement developed by the rollers. When the rubbing force of the surface of the peeling roller was greater than the force between the bract and the root and the adhesion between the bract and the seed, the bract was peeled off and discharged. This was the whole working process of peeling.

Figure 1 illustrates that the peeling process of the corn ear comprises various stages and processes to enable efficient peeling while minimizing harm to the seeds. The peeling rollers’ design considers these requirements. The peeling procedure consists of three primary operations and four stages. The first process involves bract puckering and rubbing peeling, followed by high-speed tearing to remove impurities, and, finally, infusion and transportation. These processes are connected in a series, and the length of each stage is equidistant. In the first stage (the peeling roller I section), the bracts are quickly seized and ripped apart due to the increased peeling roller speed and time, aided by the friction of the transverse spiral raised pattern. In the second stage (the peeling roller II section), the seized bracts are efficiently peeled. At this stage, the force is mainly concentrated on the upper part of the bracts, which can be increased to remove more adherent ears of fresh corn bracts. During the third stage (peeling roller III section), the remaining bracts are removed, as most of them have already been eliminated at this stage. Additionally, the small remaining portion of leaves was further excluded. At the fourth stage (peeling roller IV section), only a small residual portion of the ear bracts remains, which is almost completely peeled. The peeling rollers with a raised longitudinal spiral pattern in this stage are responsible for removing impurities, infusing, and evacuating. An efficient and effective process for peeling corn ears while maintaining seed integrity can be achieved by dividing the peeling process into different stages and designing the peeling rollers accordingly.

The fresh corn ear is peeled using a peeling roller with a specific tilt angle. This improves the efficiency and performance of the peeling process. The spiral adjustment frame, illustrated in Figure 1, has a front and rear pair and uses the cylindrical worm gear transmission to adjust the lift. One of the worm heads, with the quantity of 1, can ascend or descend a fixed distance and gains a self-locking ability. In the event that the front two worm gears adjusting the frame ascend or descend while the back two worm gears adjusting the frame remain fixed, this will cause frame interference and negatively impact the peeling effect of the peeling roller. Consequently, to prevent interference between the front and rear racks, the back two worm gears are adjusted to ascend or descend in unison with the front two worm gears for a certain distance when they are adjusted.

3. Dynamics Analysis of the Peeling Rollers

A kinematic mechanics model is developed by selecting the contact moment between the ear and roller at any point. Force analyses of the peeling force and the sliding conditions of the fresh corn ears were conducted to evaluate the motion of the peeling rollers. The analysis of the forces on the fruit spike during peeling is shown in Figure 2.

3.1. Force Analysis of Peeling Force of Fresh Corn Ears

As shown in Figure 2:

$$\left(F_1+F_2\right)\cos \beta =\left(N_1+N_2\right)\sin \beta$$

(1)

and $N_1$ = $Q_1$; $N_2$ = $Q_2$; $F_1$ = ${\mu }_1{N}_1$; $F_2$ = ${\mu }_2{N}_2$.

where $N_1$ is the support force of the low-level roller to the fruit ears, N; $N_2$ is the support force of the high-level roller to the fruit ears, N; $F_1$ is the friction force between the fruit ears and the low-level roller, N; $F_2$ is the friction force between the fruit ears and the high-level roller, N; $Q_{1 }$ is the force of the fruit ears to the low-level roller, N; $Q_2$ is the force of the fruit ears to the high-level roller, N; and $\beta$ is the stripper roll gripping angle.

When ${\mu }_1$ = ${\mu }_2$ = $\mu$ ($\mu$ is a certain value), Equation (1) can be written as:

$$\mu \left(N_1+N_2\right)\cos \beta =\left(N_1+N_2\right)\sin \beta$$

(2)

Therefore,

$$\tan \beta =\mu$$

Figure 2 also shows that

$$\sin \beta =\frac{R}{R+r}$$

(3)

where $R$ is the radius of the peeling roller, mm, and $r$ is the radius of the ear, mm.

According to Equation (3),

$$D=\frac{\sin \beta }{1-\sin \beta }$$

(4)

Figure 3 shows the fresh corn plant and the three-dimensional size of the fresh ear. To gather the geometric dimensions of fresh corn ears, a random selection of 100 fresh corn ears were measured for their length dimensions and radial dimensions. The measurements obtained are presented in

Table 1. Fresh corn ear size measurement results.

Parameters	Average/mm	Maximum/mm	Minimum/mm
Length	266.3	294.0	227.0
Diameter	65.5	69.2	59.6

. As shown in Table 1, measurement of fresh corn ear size was carried out using 300 mm vernier calipers with a product accuracy of ±0.03, and the average length and diameter of the fresh corn ears were 266.3 mm and 65.5 mm, respectively. $\beta$ is the threshold value for the peeling roller to elevate the ear to produce the beginning of the decline; through the calculation of the peeling roller gripping angle, $\beta$ is 35.5~41.1°. After comprehensive consideration, the average diameter of the ear was taken as the parameter, and the parameter of the peeling roller diameter was designed as 73 mm. As a matter of experience, if the length of the peeling roller is longer than that of a normal field corn peeling roller, the peeling result will be improved, and the seed damage will be reduced [18]. At last, we set the length of the peeling roller at 1500 mm, and the material used was elastomer rubber [17,18,23].

3.2. Force Analysis of Sliding Conditions of Fresh Corn Ear

As shown in Figure 2b, the condition for the unsliding of the fresh ears is

$$G\tan \alpha \ge F$$

(5)

where $G$ is the self-weight of the ear, N; $F$ is the peeling roller support force on the ear, N; and $\alpha$ is the angle between the direction of the peeling roller’s support force $F$ on the ear and the direction of the peeling roller’s inclination, (°).

The friction force between the peeling roller and the ear is

$$F=F_1\cos {\delta }_1+F_2\cos {\delta }_2=\frac{1}{f_1}{\mu }_1{P}_1\cos {\delta }_1+\frac{1}{f_2}{\mu }_2{P}_2\cos {\delta }_2$$

(6)

and

$$\left\{\begin{array}{c}{\delta }_1=\arctan \frac{v_1}{v}\\ {\delta }_2=\arctan \frac{v_2}{v}\end{array}\right.$$

(7)

where $P_1$, $P_2$ are the pressures of the ear to the low roller and high roller, respectively, N; ${\mu }_1$, ${\mu }_2$ are the friction factors of the low roller and high roller, respectively; $v_1$, $v_2$ are the tangential speeds of the ear relative to the low roller and high roller, respectively, m·s⁻¹; $v$ is the speed of the corn ear, m·s⁻¹; $F_1$, $F_2$ are the friction forces of the low roller and high roller acting on the ear, respectively, N; ${\delta }_1$, ${\delta }_2$ are the included angles between the friction force and the axis direction of the low and high roller, respectively, (°); $f_1$, $f_2$ are the tangential friction factors between the low roller and high roller with the ear, respectively.

There is a relationship between $N_1$, $N_2$, $G_1$

$$\frac{N_1}{\mathrm{s}\mathrm{i}\mathrm{n}(\gamma +\theta )}=\frac{N_2}{\mathrm{s}\mathrm{i}\mathrm{n}(\theta -\gamma )}=\frac{G_1}{\mathrm{s}\mathrm{i}\mathrm{n}(180-2\theta )}$$

(8)

where $N_1$, $N_2$ are the low roller and high roller support force on the ears, N; $\theta$ is the included angle between the center of the ears and the center of the roller connecting line and the ears and roller center connecting line, (°); $\gamma$ is the included angle between the connecting line of the low and high rollers’ centers and the horizontal plane, (°); and $\sin \gamma =\frac{S}{2R}$, $\sin \theta =\frac{R}{R+r}$.

Plugging $\sin \gamma$ and $\sin \theta$ into Equation (8), we obtain the following equation:

$$\left\{\begin{array}{c}{N}_1=\frac{G\cos \alpha (R+r)(R\sqrt{4R^2-S^2}+S\sqrt{r^2+2Rr)}}{4R^2\sqrt{r^2+2Rr}}\\ N_2=\frac{G\cos \alpha (R+r)(R\sqrt{4R^2-S^2}-S\sqrt{r^2+2Rr)}}{4R^2\sqrt{r^2+2Rr}}\end{array}\right.$$

(9)

where $S$ is the height difference of the high roller, mm.

Equation (9) shows that the support force of the roller on the ear was influenced by α°, $R$ mm, $S$ mm, and $r$ mm.

Combining Equations (6) and (9), we obtain the following equation:

$$F=\frac{G\cos \alpha (R+r)}{4R}\left[\frac{\sqrt{4R^2-S^2}}{\sqrt{r^2+2Rr}}({\mu }_1\cos {\delta }_1+{\mu }_2\cos {\delta }_2)+\frac{S}{R}({\mu }_1\cos {\delta }_1-{\mu }_2\cos {\delta }_2)\right]$$

(10)

Substituting Formula (10) back into Formula (6), we can obtain the sliding condition of the fruit ear along the roller. The conditions for normal sliding of corn along the peeling roller can be described as:

$$\mathrm{t}\mathrm{a}\mathrm{n}\alpha >\frac{(R+r)}{4R}\left[\frac{\sqrt{{4R}^2-S^2}}{\sqrt{r^2+2Rr}}({\mu }_1\cos {\delta }_1+{\mu }_2\cos {\delta }_2)\right.+\left.\frac{S}{R}({\mu }_1\cos {\delta }_1-{\mu }_2\cos {\delta }_2)\right]$$

(11)

Equation (11) shows that the sliding condition is influenced by $\mu$, $\alpha$°, $r$ mm, $R$ mm, $S$ mm, and $\delta$°.

The fresh corn peeling is mainly dependent on the tangential friction between the bracteal leaf and the peeling roller, for which the following conditions need to be met:

$$\left\{\begin{array}{l}\sigma >{\sigma }_1\\ \sigma >{\sigma }_2\\ F_N>P_{\mathrm{n}\mathrm{m}\mathrm{a}\mathrm{x}}\end{array}\right.$$

(12)

where $\sigma$ is the friction factor between the bracteal leaf and the roller; ${\sigma }_1$ is the friction factor between the bracteal leaves; ${\sigma }_2$ is the friction factor between the bracteal leaf and the corn kernel; $F_N$ is the tangential friction force of the peeling roll on the bracteal leaf, N; $P_{\mathrm{n}\mathrm{m}\mathrm{a}\mathrm{x}}$ is the maximum connection force between the bracteal leaf and the root of the ear, N.

Equation (11) and Figure 2 show that the peeling effect of fresh corn was effected by many factors. How to improve the BPR and reduce the GBR are the design keys of peeling equipment under certain parameters [17,24]. Equation (12) shows that the tangential friction force needs to be greater than the friction force between the bracteal leaves and the bracteal leaves and seeds to ensure the bracteal leaves can be ground and torn off. Based on the previous research [22,25] and the characteristics of fresh corn itself, the tilting angle of the peeling roller ranges was chosen as 5~9°, and the height difference between the high and low roller axes was chosen as 28 mm.

According to the analysis of Equation (9), the speed of the roller increased with the increasing the speed of the peeling rollers, resulting in a significant increase in the support force. Meanwhile, the friction force between the ears and rollers increased with the increasing support force, which is helpful to improve the BPR, but the GBR increases significantly. So, it is necessary to control the speed of the rollers. In addition, the frequency vibration plate can make the peeling more uniform and movement more smooth. Therefore, the speed of the peeling roller ranges from 400~500 r·min⁻¹ and the frequency vibration plate ranges from 250~270 times·min⁻¹ [17].

In conclusion, in order to improve the operational efficiency and quality of fresh-corn-peeling equipment, it is necessary to conduct experimental research on the BPR and GBR under different peeling roller speeds, peeling roller tilting angles and frequency vibration plates, and it can make the interactive effects of these influence factors on the peeling and breaking effect of the ear explicit.

4. Materials and Methods

4.1. Test Platform

The high–low roller peeling equipment of the agricultural products harvesting and processing laboratory of Anhui Science and Technology University shown in Figure 4 were used for the experiments. Figure 4a shows a prototype peeling apparatus. Figure 4b shows the drive regulator box. Figure 4c shows a rear view of the peeling roller apparatus. The equipment was composed of a segmented surface structure pattern and adjustable spiral frame, and it followed the design principle of reducing GBR and increasing BPR. The peeling process of fresh corn ears were realized by rubbing the rubber rollers against each other.

4.2. Test Conditions

The test material was produced from Fengnuo 168, Chuzhou City, Anhui Province, and harvested in late September 2022. The material for this experiment was fresh corn harvested by hand, and samples with large and full ears and no damage to the epidermis and pests were selected, where the moisture content of fresh corn ears was around 67.59 ± 5% (mass of water contained in the ear/overall ear mass (water content + dry matter) ×100%). The test material samples are shown in Figure 5. Each set of trials was replicated three times, and 10 fresh corn ears were placed in a single trial.

4.3. Evaluation Indicators

BPR and GPR were chosen as the evaluation indicators in the study [26].

BPR is given by:

$$\mathrm{BPR}=\frac{m-m_j}{m} \times 100\%$$

(13)

where BPR, %; $m$ is the total number of ears in the measurement area, pcs; and $m_j$ is the number of ears not stripped of bracts, pcs.

The following expression can be obtained for GBR:

$$\mathrm{GBR}=\frac{m_s}{m_i} \times 100\%$$

(14)

where GBR, %; $m_s$ is the mass of broken seeds, g; and $m_i$ is the total mass of sample seeds, g.

5. Experiment and Results Analysis

5.1. Test Results

The peeling roller speed, peeling roller tilt angle, and vibration times per minute of frequency vibration plate were chosen as the influencing factors.

Table 2. Factor level table of peeling test.

Levels	Factors
	Peeling Roller Speed ${\mathit{x}}_1$/(r·min−1)	Peeling Roller Tilt Angle ${\mathit{x}}_2$/(°)	Frequency Vibration Plate ${\mathit{x}}_3$/(times·min−1)
−1	400	5	250
0	450	7	260
1	500	9	270

shows the influencing factors and their corresponding value.

In this study, we analyze the data based on a central grouping method of Box–Behnken in Design-Expert (12), and we used a three-factor and three-level quadratic regression orthogonal combination experimental design to conduct the experiments. A quadratic regression model was applied to assess and validate the validity of the assessment indicators, which were evaluated by the coefficient of determination (R²) [27]. To determine the significance of each factor on the assessed indicators, an analysis of variance (ANOVA) was performed at a significance level of p = 0.05. Subsequently, response surfaces were generated to provide insight into the interactions between the factors. Ultimately, the study identified the optimal working and structural parameters to achieve efficient fresh corn husking.

Table 3. Test results.

No.	Test Factors			BPR%	GBR%
	${\mathit{x}}_1$ /(r·min−1)	${\mathit{x}}_2$ /(°)	${\mathit{x}}_3$ /(times·min−1)
1	400	7	270	87.59	1.87
2	450	7	260	93.54	1.46
3	450	7	260	91.56	1.38
4	500	7	270	89.06	2.03
5	450	9	270	86.59	1.69
6	450	7	260	94.02	1.42
7	450	5	250	92.34	1.92
8	400	5	260	86.98	1.57
9	450	7	260	93.28	1.21
10	500	9	260	90.01	1.76
11	450	7	260	93.89	1.33
12	450	9	260	88.79	1.53
13	400	9	260	87.98	1.67
14	500	5	260	91.21	2.13
15	450	5	270	90.06	1.97
16	400	7	250	86.78	1.63
17	500	7	250	92.59	1.98

shows the test results. $x_1$, $x_2$, and $x_3$ are factor-level values, and the BPR and GBR are the evaluation metrics.

5.2. Multiple Linear Regression Significance Analysis

5.2.1. Significance Analysis of BPR

Equation (15) is a comprehensive equation that incorporates the effect of peeling roller speed, peeling roller tilt angle, and vibration times per minute of frequency vibration plate on BPR. As shown in

Table 4. BPR analysis of variance.

Source of Variance	SS	df	MS	F	p	Significance
Model	101.43	9	11.27	7.68	0.0068	**
$x_1$	22.92	1	22.82	15.61	0.0055	**
$x_2$	6.52	1	6.52	4.44	0.0731
$x_3$	6.48	1	6.48	4.41	0.0737
$x_1{x}_2$	1.21	1	1.21	0.8244	0.3941
$x_1{x}_3$	4.71	1	4.71	3.21	0.1164
$x_2{x}_3$	0.0016	1	0.0016	0.0011	0.9746
$x_1^2$	22.79	1	22.79	15.53	0.0056	**
$x_2^2$	14.98	1	14.98	10.21	0.0152	*
$x_3^2$	15.63	1	15.63	10.65	0.0138	*
Residual	10.27	7	1.47
Lack of fit	6.33	3	2.11	2.14	0.2387
Pure error	3.94	4	0.9858
Total	111.71	16
R2	0.908

Note: SS is sum of squares; df is freedom; MS is mean squares; ** indicates highly significant ($p$ < 0.01); * indicates significant (0.01 ≤ $p$ ≤ 0.05).

, it contains 10 empirical coefficients, of which only 4 are significant. It has an R² value of 0.908, which means this prediction equation has a high predictive ability. And all the variables included in the study were equidistant to ensure no multicollinearity issues in model selection. Therefore, the prediction equation is robust to the coefficients [28]. This equation shows that the BPR increased with the increase in peeling roller speed, while it decreased with the increase in peeling roller tilt angle and vibration times per minute of frequency vibration plate. Of the model parameters, the peeling roller speed had the greatest influence on the BPR, followed by peeling roller tilt angle, and vibration times per minute of frequency vibration plate. Excluding the non-significant factors, the regression equation of the BPR with the test factors can be written as:

$$\mathrm{BPR}=93.26+1.69x_1-2.33x_1^2-1.89x_2^2-1.93x_3^2$$

(15)

5.2.2. Significance Analysis of GBR

As shown in

Table 5. GBR analysis of variance.

Source of Variance	SS	df	MS	F	p	Significance
Model	1.13	9	0.1250	14.43	0.0010	**
$x_1$	0.1682	1	0.1682	19.41	0.0031	**
$x_2$	0.1104	1	0.1104	12.75	0.0091	**
$x_3$	0.0312	1	0.0312	3.61	0.0993
$x_1{x}_2$	0.0552	1	0.0552	6.37	0.0395	*
$x_1{x}_3$	0.0090	1	0.0090	1.04	0.3414
$x_2{x}_3$	0.0030	1	0.0030	0.3491	0.5732
$x_1^2$	0.2874	1	0.2874	33.17	0.0007	**
$x_2^2$	0.1095	1	0.1095	12.64	0.0093	**
$x_3^2$	0.2765	1	0.2765	31.91	0.0008	**
Residual	0.0606	7	0.0087
Lack of fit	0.0232	3	0.0077	0.8289	0.5431
Pure error	0.0374	4	0.0093
Total	1.19	16
R2	0.9489

Note: SS is sum of squares; df is freedom; MS is mean squares; ** indicates highly significant ($p$ < 0.01); * indicates significant (0.01 ≤ $p$ ≤ 0.05).

, the coefficient of multiple determination for Equation (16) is R² = 0.9489, indicating that the equation accurately describes the relationship between the GBR and the peeling roller speed, peeling roller tilt angle, and vibration times per minute of the frequency vibration plate. Because all the variables included in the study were equally spaced, we were able to use centered variables that guaranteed no multicollinearity issues in model selection. Therefore, the prediction equation is robust with respect to the coefficients and has high predictive ability. This equation shows that the GBR increased with the increase in the peeling roller speed, while it decreased with the increase in the peeling roller tilt angle and vibration times per minute of the frequency vibration plate. Of the model parameters, the peeling roller speed had the greatest influence on the GBR, followed by the peeling roller tilt angle and the vibration times per minute of the frequency vibration plate. Excluding the non-significant factors, the regression equation of the BPR with the test factors can be written as:

$$\mathrm{GBR}=1.36+0.145x_1-0.1175x_2-0.1175x_1{x}_2+0.2612x_1^2+0.1613x_2^2+0.2562x_3^2$$

(16)

5.3. Analysis Based on Response Surface Methodology

5.3.1. Effect of Factor Interactions on BPR

Figure 6 shows the results of the interaction of the factors on the BPR. At a given peeling roller speed, the BPR increased slowly at first and then decreased rapidly with the increase in the peeling roller inclination when the frequency vibration plate was 260 times·min⁻¹; at a given peeling roller tilt angle, the BPR increased rapidly at first and then decreased slowly with the increase in the peeling roller speed (Figure 6a). At a given peeling roller speed, the BPR rose slowly and then fell rapidly with the increase in the frequency vibration plate when the peeling roller tilt angle was 7°; at a given frequency vibration plate, the BPR rose slowly and then fell rapidly with the increase in the peeling roller speed (Figure 6b). At a given peeling roller inclination, the BPR rose first and then fell with the increase in the frequency vibration plate when the peeling roller speed was 450 r·min⁻¹; at a given frequency vibration plate, the BPR rose first and then fell with the increase in the peeling roller’s inclination (Figure 6c). To summarize, achieving high-efficiency fresh corn peeling and attaining the optimal BPR can be accomplished within the following ranges: a peeling roller speed of 460~500 r·min⁻¹, a peeling roller tilt angle of 5.5~8°, and a frequency vibrating plate range of 250~260 times·min⁻¹.

5.3.2. Effect of Factor Interactions on GBR

Figure 7 shows the results of the interaction of various factors on the GBR. The interaction of the peeling roller tilt angle and the peeling roller speed on the GBR is shown in (Figure 7a), when the frequency vibration plate is 260 times·min⁻¹, the peeling roller tilt angle is certain, the GBR first slowly rises with the increase in the peeling roller speed and then rises rapidly; when the peeling roller speed is certain, with the increase in the peeling roller tilt angle, the GBR first slowly falls and then shows a trend of rapid increase. The effect of the interaction between the frequency vibration plate and the peeling roller speed on the GBR is shown in (Figure 7b). When the peeling roller tilt angle is 7°, and the frequency vibration plate is certain, the GBR first slowly decreased with the increase in peeling roller speed and then shows a rapidly increasing trend; when the peeling roller speed is certain, with the increase in the frequency vibration plate, the GBR first decreases and then increases. The effect of the interaction between the frequency vibration plate and the peeling roller tilt angle on the GBR is shown in Figure 7c. When the peeling roller speed is 450 r·min⁻¹, the GBR decreases and then rises with the increase in the peeling roller tilt angle when the frequency vibration plate is certain; when the peeling roller tilt angle is certain, the GBR decreases and then rises with the increase in the frequency vibration plate. To summarize, achieving high-efficiency fresh corn peeling and attaining the optimal GBR can be accomplished within the following ranges: a peeling roller speed of 410~460 r·min⁻¹, a peeling roller tilt angle of 7.5~8.5°, and a frequency vibrating plate range of 255~265 times·min⁻¹.

5.4. Parameter Optimization and Verification

According to the results of the fresh corn ear peeling test, we set the maximum BPR and the minimum GBR as the optimization objectives, and performed optimization analysis for the regression equation. The constraint condition is

$$\left\{\begin{array}{l}\max BPR\\ \min GBR\\ 400 \mathrm{r}·{\mathrm{m}\mathrm{i}\mathrm{n}}^{-1}\le x_1\le 500 \mathrm{r}·{\mathrm{m}\mathrm{i}\mathrm{n}}^{-1}\\ 5°\le x_2\le 9°\\ 250 \mathrm{times}·{\mathrm{m}\mathrm{i}\mathrm{n}}^{-1}\le x_3\le 270 \mathrm{times}·{\mathrm{m}\mathrm{i}\mathrm{n}}^{-1}\end{array}\right.$$

(17)

The optimization results were as follows: the peeling roller speed was 478.72 r·min⁻¹, the peeling roller tilt angle was 8.05°, and the frequency vibrating plate was 259.20 times·min⁻¹. The BPR was 92.41%, and the GBR was 1.47% under this condition.

All parameters were taken as integers for the convenient setting of parameters in practical operation. Ultimately, 480 r·min⁻¹ for $x_1$, 8° for $x_2$, and 260 times·min⁻¹ for $x_3$ formed the optimal combination. Then, repeated verification tests were conducted for the optimized parameters to check the optimized results. Under the condition of removing random errors, 20 corn ears were fed into the equipment continuously until all the ears were peeled, and this was one test. After the test, we calculated the values of the BPR and the GBR. Two more tests were conducted under the same conditions, and the average values of these three tests would be the final values of the BPR and the GBR. The average value of the BPR was 91.75%, which was 0.66% lower than the optimized value, while the average value of the GBR was 1.55%, which was 0.08% higher than the optimized value. The effect of the fresh-corn-ear-peeling validation test is shown in Figure 8. The experimental results are verified in the data table, as shown in

Table 6. Experimental results of verification tests.

Indicators	Test Serial Number			Average Value
	1	2	3
BPR%	92.32	90.97	91.96	91.75
GBR%	1.58	1.56	1.51	1.55

. The optimization results were basically consistent with the experimental verification results. In conclusion, the optimization of the corn-peeling equipment was of great concern for the values of the BPR and the GBR.

6. Discussion

At present, few studies have examined the peeling of fresh corn [16,29], and most of the research results are related to common field corn and seed corn [6,17,18,19,20,22,23,25]. Therefore, it is of great practical significance to improve the application of fresh-corn-peeling technology in the actual harvesting process. It is not only necessary to improve the working efficiency of the peeling equipment, but also to reduce the damage to the ear kernels during the peeling process. In this paper, from the structural design and theoretical analysis of the key components of the peeling equipment to the actual test operation, an evaluation index of this experiment was carried out with reference to the study by Zhao [16]. The data obtained from the validation tests conducted in this paper are compared with the expected results, and the degree of agreement between the two indicates the reliability of the peeling equipment.

When performing fresh corn ear peeling, the two evaluation indices, the BPR and the GBR, are related to the physical characteristics of fresh corn ears and crop agronomy. For example, Li et al. [22], when carrying out the study of selecting corn ear varieties for bract tensile rupture, the results were basically consistent when carrying out ear bract tensile rupture experiments, although there was variability among the corn ears of various varieties [30]. Compared with the experiment conducted above, the Fengnuo168 fresh corn ear varieties studied in this paper tend to be consistent when conducting bract leaf peeling fracture characteristics experiments in the same conditions. However, ear size varies relative to the planting method until ear maturity. If the relative ear size is too long, the peeling effect may produce a low BPR. If the relative ear size is too short, the peeling effect may produce a GBR high. Overall, for fresh corn ear peeling equipment, the BPR and GBR have been a better application, effectively saving labor costs as well as improving efficiency.

When the peeling roll speed was 480 r·min⁻¹ and the peeling roller tilt angle was 8°, compared with the reference [16], when the peeling roll speed was 500 r·min⁻¹ and the peeling roller tilt angle was 7°, the GBR was reduced by 60.26% and the BPR was increased by 0.005%. However, in the experiment, it was found that when the speed of the peeling roller increased, the whole machine would produce the phenomenon of resonance, and when it reached a certain speed, the vibration would reduce. In order to not affect the peeling effect, the peeling device with the combination of the “rubber segmented roller type + spiral adjusting frame” and the rubber frequency vibration plate was used to match, which improved the stability of the peeling and had a better effect on the peeling effect.

In conducting the prototype test, the number of ears used in each group of tests was 20, and three groups of tests were conducted separately, which finally resulted in a BPR of 91.75% and a GBR of 1.55%. Therefore, the peeling equipment has important research significance and application value for peeling fresh corn ears. In order to reduce errors generated randomly by the test itself, the number of actual tests should be more than the number of ears given. By conducting a large number of experimental studies on fresh corn ears, it is guaranteed that fresh corn ears will make a greater contribution to improving the BPR and reducing the GBR.

7. Conclusions

(1)

Taking into consideration the physical characteristics of fresh corn ears, a high–low roller peeling machine was developed with a focus on the design and function of its peeling roller device. The structural design and working principle of the peeling roller device were thoroughly examined. By applying theoretical analysis, a dynamic model specific to the interaction between the corn ear and the machine was developed. This allowed for the determination of the parameters necessary for the peeling mechanism, as well as the identification of the key factors influencing its working performance.

(2)

To conduct experiments on the peeling process of fresh corn, three factors were selected for testing: peeling roller speed, peeling roller tilting angle, and frequency vibration plate vibration times per minute. The BPR and GBR were chosen as the evaluation criteria for the experiments. A three-factor quadratic regression orthogonal combination test plan was adopted to carry out the tests. This approach helps to systematically explore the relationships between the selected factors and the test indices, providing valuable insights into optimizing the peeling process for fresh corn.

(3)

The test data obtained from the experiments were analyzed and optimized using Design-Expert 12 software. By applying the software, the best parameter combinations for the test factors were determined. These included a peeling roller speed of 478.72 r/min, a peeling roller tilting angle of 8.05°, and a frequency vibration plate of 259.20 times/min. Under these conditions, the peeling process resulted in a BPR of 92.41% and a GBR of 1.47%. In the validation test, the optimized parameter combinations were implemented, resulting in a BPR of 91.75% and a GBR of 1.55% for fresh corn ears. The results from the parameter optimization were consistent with the validation test, indicating that the optimized equipment met the design requirements effectively.