Parameter Optimization Study of Adaptive Spiral Profiling Automatic Rubber Cutter for Natural Rubber Trees

Abstract

Long-term tracking tests revealed that the adaptive spiral profiling automatic rubber cutter is prone to “knife off” and “knife jumping” states during the cutting process. These issues result in inconsistent skin thickness and poor cutting surface uniformity and can even damage natural rubber trees, leading to reduced yield, disease, and potentially death. This paper presents experimental research on the parameters affecting the performance of the adaptive spiral profiling automatic rubber cutter. It explores the impact of key parameters on cutter performance and establishes a mathematical model relating these parameters to the peel thickness and cutting surface uniformity rates. Through multi-objective parameter optimization, the optimal parameter combination was determined: a cutting speed of 15 s/knife, a blade thickness of 0.50 mm, and a bending angle of 27.5°. In six tests, the average peel thickness and cutting surface uniformity rates were 97.5% and 96.5%, respectively. These results meet national standards, suggesting that the optimized parameters significantly improve the rubber cutter’s performance. The results meet national standards, demonstrating that the optimized parameters improve the peel thickness and cutting surface uniformity. The regression model is reliable and provides a foundation for selecting better parameter combinations for future prototypes.

1. Introduction

Natural rubber is a crucial strategic material and industrial raw material. It possesses both agricultural and resource properties and is the only renewable resource among the four primary industrial raw materials. Natural rubber is characterized by excellent insulation, wear resistance, plasticity, elasticity, and other properties [1]. Notably, it exhibits superior ductility and resilience at low temperatures compared to synthetic rubber [2]. Due to these properties, natural rubber products are extensively utilized across diverse sectors including industry, agriculture, national defense, transportation, machinery manufacturing, medicine, healthcare, and daily life [3,4]. For example, natural rubber is used in the manufacture of tires, especially car, truck, and aircraft tires, etc.

Natural rubber trees are perennial, tall plants, and harvesting rubber by cutting these trees is one of the primary methods for obtaining natural rubber [5,6,7]. Currently, natural rubber is primarily obtained through the semi-helical ring cutting of the bark [8]. Rubber cutting has become a critical step in the production process, where cutting knives are used to slice the outer epidermis of the tree trunks, severing the milk ducts to extract the latex. Rubber cutting plays a crucial role in increasing production and income in the natural rubber industry, yet it remains highly dependent on manual labor. The technology is labor-intensive, with rubber cutting costs accounting for approximately 70% of the total management expenses for natural rubber forests. This dependence on manual labor has been a significant challenge in the development of the natural rubber industry over recent decades [9,10,11]. In recent years, the shortage of rubber workers, coupled with an aging workforce, has led to an increasing abandonment of natural rubber forests and a decline in the number of cutting or replanting activities. This trend threatens the sustainable development of the natural rubber industry. Exploring automated rubber cutting technology, which replaces manual labor with machines, is seen as a potential solution to this issue. Automating rubber cutting is an effective solution to the challenges facing the natural rubber industry [12,13,14]. However, the rough and irregular nature of rubber tree trunks, coupled with their thin and delicate bark, presents challenges for existing rubber cutting equipment. Current machines consume high power, struggle with unstable cutting depths and thicknesses, and are prone to damaging the bark, which can reduce yield or even kill the trees. These issues significantly hinder the widespread adoption of automated rubber cutting technology in the industry.

The Asia–Pacific region is the primary production hub for natural rubber, contributing over 90% of global production [15]. In recent years, the shortage of rubber workers has become a major bottleneck for the growth of the natural rubber industry [16,17]. Major rubber-producing countries have focused significant attention on developing technologies and equipment to move beyond traditional manual cutting methods and mechanize and automate the cutting process. Rubber-producing countries have traditionally relied on inexpensive labor for harvesting, with less emphasis on mechanized cutting equipment. This reliance has partly been due to the concentration of rubber tree cultivation in tropical regions, primarily within the 15° north and south latitudes, which are mostly in developing countries. Recently, rising labor costs and the persistently low price of natural rubber have exacerbated the shortage of workers. Consequently, research into the mechanization and automation of rubber cutting technology has become a focal point in the natural rubber industry [18].

To address the issues of inaccurate cutting depth and thickness, operational difficulty, and low efficiency in traditional rubber cutters, many scholars and frontline technicians have worked to improve and optimize these tools, which are the most widely used in rubber cutting operations. H. Susanto et al. [19] from Deku Uma University designed a flexible cutter with an adjustable cutting depth and thickness. The cutter allows for quick adjustments between depths of 1 and 1.5 mm from the formation layer and a thickness of 1.5–2 mm compared to traditional cutters. This flexible cutter improved cutting efficiency by 21.4%. Ru Shaofeng et al. from Hainan University [20,21] enhanced the traditional rubber cutter using bionic principles. They designed a push-type cutter modeled after rodent claws and a pull-type cutter inspired by pangolin scales. These designs reduce cutting resistance, improve surface uniformity, and extend the cutter’s lifespan. Walaiporn Pramchoo et al. [22] from Prince of Songkhla University analyzed the causes of carpal tunnel syndrome from the prolonged use of traditional cutters and developed an ergonomic cutter with a handle bent downward by 15–20°. Testing showed that this ergonomic design effectively prevents carpal tunnel syndrome in rubber workers. Although the above-mentioned improved manual cutters have enhanced the precision of depth and thickness control and improved comfort, they still rely on skilled workers and do not address the technical challenges of cutting or the shortage of labor.

The electric rubber cutter replaces the traditional manual cutting method with an electric power-driven operation supplemented by human input. The electric rubber cutter operates primarily through the high-frequency vibration of the cutter blade to cut the rubber bark. The Malaysian Rubber Research Institute [23] began researching electric rubber cutting as early as 1979. In collaboration with the Japanese company Nihongiken, they developed the Motoray Mark II electric rubber cutter and conducted field tests to assess the cutting time, tree damage rate, skin consumption, and other factors. However, due to the production and processing limitations of the time, it did not meet the portability requirements, such as the large motor and battery sizes and the low machining accuracy of parts. As a result, it has not yet been widely adopted. Susan John Soumya et al. [24,25] developed a handheld electric gum cutter aimed at lowering the entry barriers for rubber cutting operations. The cutter features a mechanically guided structure with an electronically assisted monitoring system, making it easier to adjust key operational parameters and reducing training times for workers. Cao Jianhua et al. [26,27,28] from the Chinese Academy of Tropical Agricultural Sciences (CAATAS) developed the 4GXJ-I and 4GXJ-II electric rubber cutters, which are powered by brushless motors. These cutters are simple to use, and in testing, their injury rate, glue cleanliness, cutting surface smoothness, and efficiency values were comparable to or exceeded those of traditional rubber cutters. This innovation reduces the technical difficulty and labor intensity of rubber cutting by over 60% while increasing efficiency by 20–30%. In a single plantation, rubber cutting efficiency can increase by 60–80% [29,30]. The successful development of the above electric rubber cutters represents an innovation in traditional rubber cutting methods. With a mechanical structure that limits cutting depth and thickness, these cutters significantly reduce the technical demands on workers, shorten training times for new workers, and help alleviate the industry’s “rubber worker shortage”. However, manual assistance is still required during operation, meaning that it does not offer a complete solution to the industry’s challenges.

An automatic rubber cutter is an automated system that operates without human intervention. The machine autonomously completes processes such as positioning the incoming cutter, cutting with the walking cutter, and returning the cutter to its original position. N. Ahmad et al. [31] from the Malaysian Rubber Board developed an automatic rubber cutter where the cutter head rotates under the drive of a high-speed motor. This design facilitates cutting and allows the easy adjustment of the cutting angle. However, the machine did not incorporate a helical cutter movement, and the cutting depth was not adjustable, making it prone to injuring the tree. Deepthi S. R. et al. [32] designed an automatic rubber cutter with rigid circular clamps to facilitate cutting through a helical rack. While simple in structure, the design lacked an axial displacement mechanism, preventing continuous and automatic rubber cutting. Abhilash et al. [33] developed an automatic rubber cutter with a milling cutter that uses a position sensor to detect the first cut. The machine completes the rubber cutting operation in 12 s. However, the tree bark is ground into powder, contaminating the latex. Gao Keke et al. [34] from the Beijing University of Information Science and Technology introduced an ultrasonic scanning-based method for rubber cutting control. They designed a stationary rubber cutting machine, improving efficiency by about 63% compared to manual cutting. Ningbo Zhongchuang Hanwei Science and Technology Co., Ltd., Ningbo, China [35,36,37] developed a rubber cutting machine designed for non-regular rubber tree trunks with a “one machine, one tree” approach. This machine uses a deformable guide rail [38] and a composite circumferential and axial movement for helical cutting, achieving a high degree of automation. However, issues such as high power consumption and unstable cutting depth and thickness remain. Although the above automatic rubber cutting equipment can reduce dependency on rubber workers, decrease labor intensity, and improve efficiency, most are still in the prototype testing stage. They face challenges such as complex structures, high power consumption, high costs, and the unstable control of cutting depth and thickness, preventing widespread adoption.

This paper investigates the performance optimization of an adaptive spiral profiling automatic rubber cutting machine, which frequently experiences issues such as “off the knife” and “jump knife” states during operation. These issues lead to inconsistent bark thickness, poor surface uniformity, and potential damage to rubber trees, including reduced yield, disease, or even death. The paper focuses on the impacts of various machine parameters on performance optimization. To optimize machine performance, the optimal parameter combinations were identified: a cutting speed of 15 s/knife, a cutting blade thickness of 0.50 mm, and a blade bending angle of 27.5°. The average qualification rates for bark thickness and cutting surface uniformity were 97.5% and 96.5%, respectively. These findings provide a foundation for identifying improved parameter combinations for subsequent prototype machines.

2. Materials and Methods

2.1. Adaptive Spiral Profiling Automatic Rubber Cutting Machine: General Structure and Working Principle

This study addressed challenges in current rubber cutting operations, including high labor costs, low efficiency, and unstable cutting depth. Most rubber cutting tasks still rely on manual assistance, leading to high labor intensity. Our research focuses on the automation of natural rubber cutting, employing theoretical analysis, virtual simulations, and experimental methods. After years of scientific research, several key technical challenges have been addressed, resulting in innovations such as adaptive flexible tooth-belt fixing technology, tree-mimicry cutting technology, and APP-based remote control. Additionally, an adaptive spiral-mimicry automatic rubber cutting machine has been developed, as shown in Figure 1. The machine includes components such as upper and lower drive teeth, an adjustable bundle-fixing device, a circumferential motion device, a vertical motion device, a transmission device, a tree-mimicry cutting device, an intelligent cutting control module, stepping motors, DC geared motors, and more. The material used for the cutting blade is 3Cr13 steel, as shown in Figure 1c.

The adaptive spiral profiling automatic rubber cutting machine replaces traditional manual cutting, allowing workers to avoid round-the-clock labor. This high-automation machine is not limited by location; it can operate in any area with network coverage and offers lower operational costs. It has been demonstrated in regions such as Danzhou City, Hainan Province, and Tunchang County, with positive outcomes. This innovation accelerates the modernization of rubber plantations, freeing workers from manual labor and enhancing natural rubber production, which is crucial for ensuring the security of strategic materials. It can accelerate the modernization of rubber plantations, liberating workers from manual labor and improving rubber production, which is vital for securing national strategic materials. The machine has broad application potential; however, long-term testing has revealed several issues. During operation, the rubber cutting process may experience “off-cut” and “jump-cut” phenomena, leading to inconsistent thickness and poor surface uniformity. These problems may damage rubber trees, reduce yields, and cause disease or even death.

2.2. Adaptive Spiral Profiling Automatic Rubber Cutter: Working Principle

Considering the difference between the sizes of the upper and lower circumferences of the natural rubber tree and the irregularity of its surface and other factors, firstly, the upper and lower drive teeth and the adjustable bundling and fixing device are used to work together so as to stably fix the adaptive helical profiling automated rubber cutter to the natural rubber tree, avoiding the deviation of the center axes of upper and lower teeth and the center axis of the rubber tree in the process of cutting, thus ensuring the accurate and stable autonomous cutting work in the later stage. The specific cutting work is as follows: for the adaptive spiral mimic automatic rubber cutting machine in the receipt of rubber cutting instructions, we extend the push rod, and driven by the tree-mimicry cutting device close to the surface of the rubber tree bark, the push rod completely extends to stop the movement. At this time, the cutter knife in the depth spring is preloaded, to achieve the role of the tension, into the state of waiting for the cut; then, the DC geared motor begins to work, through the drive of the vertical movement of the device’s turbine and the rotation of the screw, to drive the tree-mimicry cutting device, and then, the cutter will be cut to the rubber tree bark. Then, the DC gear motor starts to work, and by driving the turbine and screw of the vertical motion device to rotate, the rubber cutting device is driven to carry out vertical motion along the aluminum column track; by driving the upper and lower transmission gears of the circumferential motion device to mesh with the upper and lower transmission gears of the adjustable strapping fixing device for power transmission, thus driving the rubber cutting machine to carry out circumferential motion along the strap track as a whole, and by adopting the way of synergistic operation of the circumferential motion and the vertical motion, it realizes that the rubber cutter carries out spatial imitation of the human cutting trajectory along the trunk of natural rubber tree Spiral movement. When the cutter knife reaches the cutting termination water line, the DC reducer motor stops, the push rod is withdrawn, and the cutter knife is withdrawn by the tool feeding mechanism. At this time, the DC reducer motor reverses and the cutter is driven to return to the cutting start water line according to the original cutter trajectory. Subsequently, the stepping motor turns positively, driving the tree-mimicry cutting device along the screw down a distance (thickness of the consumed skin) and then making it stop, waiting for the next step to start the cutting instructions and successfully complete a complete mowing operation. The whole cutting process can be remotely operated by the intelligent control module to complete the automated cutting operation.

2.3. Adaptive Spiral Form—Following Automatic Rubber Cutter Forest Cutting Performance Test

Long-term tracking tests have shown that the adaptive spiral profiling automatic rubber cutter still experiences issues such as “off the knife” and “jump knife” states during the cutting process. These problems lead to unstable skin thickness and poor cutting surface uniformity and can even damage natural rubber trees, thereby reducing yield and increasing susceptibility to diseases. This paper presents experimental research on the parameters affecting the performance of the adaptive spiral profiling automatic rubber cutter. It explores the influence of key parameters on the cutter’s performance and establishes a mathematical model linking these parameters to the skin thickness and cutting surface uniformity qualification rates.

2.3.1. Purpose of the Test

The initial pre-test of the adaptive spiral profiling automatic rubber cutter indicated that its operational performance depends primarily on three factors: cutting speed, blade thickness, and the bending angle of the cutting blade. This study used the Box–Behnken design to evaluate two key performance indicators: the qualified rate of skin thickness and the uniformity of the cutting surface. The factors tested were cutting speed, blade thickness, and the bending angle, which significantly affect the performance of the adaptive spiral mimic automated rubber cutter. A three-factor, two-level response surface test was conducted to investigate the influence of these parameters on the cutter’s performance. The response surface test explored how key parameters affected the performance of the adaptive spiral rubber cutting machine. It established a mathematical model linking these parameters to the qualified rates of skin thickness and cutting surface uniformity. This model provided the basis for identifying optimal parameter combinations for the subsequent prototype machine.

2.3.2. Test Methods and Equipment

The experimental research on rubber cutting performance was conducted in the natural rubber tree plantation at Hainan University Danzhou Campus, using the “Heat Research 7-33-97” variety. The test followed the DG46/Z004-2022 “Outline of Special Appraisal for Agricultural Machinery”. To facilitate quick calculation and analysis of the test results, and to optimize the parameters of the adaptive spiral profiling automatic rubber cutter, the qualified rates of skin thickness and cutting surface uniformity were chosen as performance indicators. Based on previous experimental research and rubber cutting experience, three key factors affecting the performance of the adaptive spiral profiling automatic rubber cutter were selected: cutting speed, blade thickness, and blade bending angle.

The speed of rubber cutting was adjusted by varying the motor’s rotational speed, the blade thickness was modified by replacing the blade with one of different thickness, and the blade’s bending angle was altered by using blades with different angles. The relevant tests were conducted when the adaptive spiral profiling automatic rubber cutter was in stable working conditions. At the end of the test, the qualified rates of skin thickness and cutting surface uniformity were calculated using Formulas (1) and (2).

The qualified rate of skin-consuming thickness was measured by collecting the cut natural rubber bark and using vernier calipers to measure the thickness at three points: the starting, middle, and end points. If the thickness fell within the range of 1.1–1.8 mm, it was considered qualified. The formula for calculating the qualified rate was as follows:

$$H={\displaystyle \frac{x}{X}}\times 100\%$$

(1)

Here, H—pass rate (%) of depleted skin thickness;

$x$—Number of qualified measurement points;

$X$—Total number of measurement points.

The cutting surface uniformity pass rate was determined by measuring the length of the cut line, identifying any visible waves, jagged edges, or step-like irregularities. The pass rate was calculated as follows:

$$K=(1-{\displaystyle \frac{l}{L}})\times 100\%$$

(2)

Here, $K$—pass rate of uniformity of cut surface (%);

$l$—Length of cut line where cut line appeared visibly wavy, jagged, and stepped (mm);

$L$—Total length of cut line (mm).

The main equipment required for testing included vernier calipers, tape measures, stopwatches, wrenches, etc., according to the requirements of the test parameter measurements.

3. Results and Analysis

3.1. Experimental Program and Measurement Results

Based on the relationship between the physical and mechanical properties of natural rubber bark and the design parameters of the adaptive spiral profiling automatic rubber cutting machine, along with the results of the preliminary single-factor test, the following parameter ranges were determined: the cutting speed (15–25 s/knife), blade thickness (0.5–1.5 mm), and blade bending angle (25–35°). To minimize the number of forest rubber cutting tests required, the Box–Behnken experimental design was used to conduct a three-factor, two-level combination test. A total of 17 response surface analysis tests were performed based on the test factors and value ranges provided in

Table 1. Each test factor and its value range.

Codes	Cutting Speed A (s/Penetration of a Knife)	Thickness of Cutting Blade B (mm)	Rubber Cutting Blade Bending Angle C (°)
−1	15	0.5	25
0	20	1.0	30
1	25	1.5	35

, with the coded values (A, B, and C) shown in

Table 2. Experimental design scheme and measurement results.

Test Number	Cutting Speed A	Thickness of Cutting Blade B	Rubber Cutting Blade Bending Angle C	Consumption of Skin Thickness Pass Rate H/%	Pass Rate of Cut Surface Uniformity K/%
1	0	0	0	95.0	92.9
2	0	1	−1	93.8	92.0
3	−1	0	1	93.6	93.8
4	1	−1	0	95.7	94.3
5	0	0	0	95.0	92.9
6	0	0	0	95.0	92.8
7	1	0	1	93.3	93.2
8	−1	0	−1	94.4	94.1
9	0	0	0	95.1	92.9
10	1	0	−1	93.9	92.6
11	0	0	0	95.0	92.7
12	−1	1	0	94.5	94.5
13	1	1	0	94.9	93.7
14	−1	−1	0	96.5	95.8
15	0	1	1	93.7	92.5
16	0	−1	−1	95.6	93.4
17	0	−1	1	94.4	93.2

. Each test group was repeated three times, and the averages of the results were calculated. The data were processed using Design-Expert software.

The experiment explored the effect of different parameter configurations of the adaptive spiral profiling automatic rubber cutter on forest rubber cutting. The optimal parameter combinations, which corresponded to the best working conditions (i.e., the highest pass rates for skin consumption thickness and cutting surface uniformity), were identified as shown in the experimental design and measurement results in Table 2. Based on Table 2, Design-Expert was used for multiple regression analysis and the analysis of variance (ANOVA) to establish mathematical models for each experimental factor, as well as for the qualified rates of skin-consumption thickness (H) and cutting surface uniformity (K) [39,40]. The response surfaces were then analyzed. Figure 2 shows the results of the field cutting test for the adaptive spiral profiling automatic rubber cutter in the forest.

3.2. Regression Modeling and Testing

The test results were analyzed using Design-Expert software. A p-value of <0.01 indicated a highly significant effect of the test factors on the model while a p-value of <0.05 indicated a significant effect.

Table 3. Model significance test results.

Source of Variation	Consumption of Skin Thickness Pass Rate H/%				Pass Rate of Cut Surface Uniformity K/%
	Degrees of Freedom	Mean Square	F1	P1	Degrees of Freedom	Mean Square	F2	P2
regression	9	1.27	218.83	<0.0001 **	9	1.51	250.99	<0.0001 **
A	1	0.18	31.11	0.0008 **	1	2.42	403.33	<0.0001 **
B	1	3.51	606.88	<0.0001 **	1	2.00	333.33	<0.0001 **
C	1	0.91	157.50	<0.0001 **	1	0.045	7.50	0.0290 *
AB	1	0.36	62.22	<0.0001 **	1	0.12	20.42	0.0027 **
AC	1	0.010	1.73	0.2300	1	0.20	33.75	0.0007 **
BC	1	0.30	52.28	0.0002 **	1	0.12	20.42	0.0027 **
A2	1	0.040	6.92	0.0339 *	1	5.99	997.93	<0.0001 **
B2	1	0.96	165.93	<0.0001 **	1	1.24	206.53	<0.0001 **
C2	1	5.31	916.96	<0.0001 **	1	1.55	258.99	<0.0001 **
residual	7	5.786 × 10−3			7	6.0 × 10−3
incoherent	3	0.011	5.42	0.0681	3	3.333 × 10−3	0.42	0.7510
inaccuracies	4	2.0 × 10−3			4	8.0 × 10−3

Note: ** denotes highly significant difference (p < 0.01); * denotes significant difference (p < 0.05).

presents the analysis of variance for the quadratic regression model and the significance tests for the regression coefficients.

Table 3 shows that the p-values (P₁ and P₂) for the quadratic regression model, based on the test factors and the qualified rates of skin thickness (H) and cutting surface uniformity (K), were both lower than 0.01. This indicates that the model was highly significant and that the quadratic regression equations accurately reflected the relationship between the qualified rates (H and K) and the factors A, B, and C. The regression model could accurately predict the test results of the forest rubber cutting performance.

Regarding the qualified rate of skin thickness (H), the primary terms—cutting speed (A), cutting blade thickness (B), and bending angle (C)—along with the interaction terms, AB and BC, had a highly significant effect. The secondary term A² also significantly affected the qualified rate of skin thickness. In contrast, the secondary terms B² and C² showed a highly significant effect while the remaining terms did not significantly impact the qualified rate of skin thickness.

Regarding the cutting uniformity pass rate (K), the primary terms—cutting speed (A) and cutting blade thickness (B)—along with the interaction terms AB, BC, and AC, as well as the secondary terms A², B², and C², all had a highly significant effect. Additionally, the bending angle (C) significantly impacted the cutting uniformity pass rate.

The quadratic regression model for the pass rates of skin-consumption thickness (H) and cut uniformity (K), derived after removing the insignificant terms, was as follows.

$$\begin{array}{l}H=95.02-0.15A-0.66B-0.34C+0.30AB+0.05AC+\\ 0.27BC-0.098A^2+0.48B^2-1.12C^2\end{array}$$

(3)

$$\begin{array}{l}K=92.84-0.55A-0.5B+0.075C+0.17AB+0.23AC+\\ 0.18BC+1.19A^2+0.54B^2-0.61C^2\end{array}$$

(4)

Based on the analysis of Table 3 and the p-values for each factor, the influence of each factor on the qualification rate H of skin thickness, in descending order, was as follows: cutting blade thickness, bending angle of the cutting blade, and cutting speed. Similarly, the influence on the qualification rate K of cut surface uniformity, in descending order, was as follows: cutting speed, cutting blade thickness, and bending angle of the cutting blade.

3.3. Model Interaction Term Analysis

Based on the quadratic regression model, response surface plots were generated to illustrate the relationship between the qualification rates of consumed skin thickness and cut surface uniformity and the influencing factors. The shape of the response surface indicated the strength of the interactions between these factors.

Figure 3a shows that as the cutting speed increased and cutting blade thickness decreased, the qualified rate of skin consumption thickness also increased. Both parameters approached a value of −1 when the maximum qualified rate was reached. This was because higher cutting speeds and thinner cutting blades enhanced cutting performance, enabling faster cutting and ensuring a higher qualification rate for skin consumption thickness. The response surface trend indicates that the cutting blade thickness had a greater impact on the qualified rate of skin thickness than the cutting speed.

Figure 3b shows that as the bending angle of the rubber cutting blade increased, the qualified rate of consumed bark thickness first increased and then decreased. Similarly, as the cutting speed increased, the qualified rate also increased. This was because a larger bending angle stabilized the cutting blade, enabling smoother operation. However, if the bending angle became too large, the contact area between the blade and the rubber bark increased. When encountering uneven bark, this caused blade vibration, which decreased the qualified rate of bark thickness. The response surface trend indicates that the bending angle of the cutting blade had a greater effect on the qualified rate of bark thickness than the cutting speed.

Figure 3c shows that as the thickness of the rubber cutting blade decreased, the qualified rate of skin thickness increased, reaching its maximum near the −1 level. Additionally, as the bending angle of the cutting blade increased, the qualified rate first increased and then decreased, with the maximum value occurring near the 0 level. This was primarily because a thinner cutting blade enhanced cutting performance, enabling faster cutting and ensuring a higher qualified rate of skin thickness. The response surface trend further indicated that the blade thickness had a greater impact on the qualified rate of skin thickness than the bending angle, which aligned with the results of the analysis of variance (ANOVA).

Figure 3d shows that as the cutting speed increased and cutting blade thickness decreased, the pass rate of cutting surface uniformity improved, with the highest values observed at the −1 level for both factors. This was primarily due to the enhanced cutting performance resulting from higher cutting speeds and thinner blades, which enabled faster and more stable cutting, thereby ensuring a higher pass rate for cutting surface uniformity. The response surface trend further indicates that the cutting speed has a greater impact on the qualified rate of cutting surface uniformity than the blade thickness.

Figure 3e shows that as the cutting speed increased, the pass rate of cut surface uniformity also increased. The bending angle of the cutting blade initially increased the pass rate, but beyond a certain point, the pass rate decreased. This was because an increased bending angle initially stabilized the cutting blade, ensuring smoother operation. However, if the angle became too large, the blade’s contact area with the rubber bark increased. When encountering uneven bark, this led to blade vibration, which reduced the pass rate of cut surface uniformity. The response surface trend further indicates that the cutting speed has a greater impact on the pass rate of cut surface uniformity than the bending angle.

Figure 3f shows that as the thickness of the rubber cutting blade decreased, the pass rate of cut surface uniformity increased, reaching its maximum near the −1 level. As the bending angle of the blade increased, the pass rate first increased and then decreased, with the maximum value occurring near the 0 level. The response surface trend further indicates that the thickness of the rubber cutting blade has a greater impact on cut surface uniformity than the bending angle, which aligned with the ANOVA results.

4. Discussion

To maximize the performance of the adaptive spiral profiling automatic rubber cutter, Design-Expert software was used to optimize the regression equation model of the test factors and performance indices. The optimal parameters were a cutting speed of 15.01 s/knife, blade thickness of 0.50 mm, and blade bending angle of 27.45°. These parameters resulted in a skin thickness qualification rate of 96.5% and a cutting surface uniformity qualification rate of 95.8%.

To verify the reliability of the model, a verification test was conducted at the natural rubber forest test base in Danzhou City, Hainan Province. Considering the feasibility of the test parameters, the optimal settings were adjusted to a cutting speed of 15 s/knife, blade thickness of 0.50 mm, and blade bending angle of 27.5°. The average qualified rates for skin thickness and cutting surface uniformity were 97% and 96%, respectively, based on six tests. The average qualified rates for skin thickness and cutting surface uniformity, measured over six tests, were 97.5% and 96.5%, respectively. These results met the relevant national standards, indicating that the optimized parameters effectively improved the cutter’s performance. Thus, the regression model was reliable.

5. Conclusions

(1) This study applied the Box–Behnken experimental design to test the performance of the adaptive spiral profiling automatic rubber cutter for forest rubber cutting. Using a three-factor, two-level response surface analysis, the factors influencing the skin thickness qualification rate, in descending order of effect, are as follows: the cutting blade thickness, blade bending angle, and cutting speed. The factors affecting cutting surface uniformity, in descending order, are as follows: the cutting speed, blade bending angle, and cutting blade thickness. The factors affecting cutting surface uniformity, in descending order of effect, are as follows: cutting speed, blade thickness, and blade bending angle.

(2) This paper has established a quadratic polynomial regression model to relate the qualified rates of skin thickness and cutting surface uniformity to the cutting speed, blade thickness, and blade bending angle. The optimal working parameters for the adaptive spiral profiling automated rubber cutter were found to be as follows: a cutting speed of 15 s/knife, blade thickness of 0.50 mm, and blade bending angle of 27.5°. These optimized parameters were tested and verified in a field trial, yielding favorable results. The performance of the rubber cutter improved post optimization, providing valuable insights for the future research and development of the adaptive spiral profiling automatic rubber cutter.