Influence of Fluorine Nano-Coating on Cutting Force and Surface Roughness of Wood–Plastic Composites During Milling

Abstract

Wood–plastic composites (WPCs) are important materials used in interior architectural decorations and landscape construction products. Enhancing the cutting performance of WPCs is of great significance for improving both production efficiency and product quality in factories. This study aims to elucidate the impact of fluorine nano-coating technology on the cutting performance of cemented carbide tools during the milling of WPCs. The main results are given as follows. The cutting force and surface roughness showed similar trends with the varied parameters; both increased with increasing cutting depth and decreased with increasing cutting speed. The fluorine nano-coating technology exerts a positive influence on the cutting performance in terms of lower cutting forces and surface roughness. Meanwhile, based on the analysis of variance results, the experimental factors of cutting speed, depth, and surface treatment had a significant contribution to both cutting force and surface roughness, and cutting depth had the greatest impact on cutting force and surface roughness, followed by cutting speed and tool surface treatment. In general, the cutting performance of WPCs can be improved by higher cutting speed and lower depth, with the tool surface treated with fluorine nano-coating.

1. Introduction

Wood–plastic composites (WPCs) are a type of thermoplastic engineering material. WPCs are manufactured through a specific process that combines wood (or plant) fibers, polymer plastics, and coupling agents [1,2]. These composites exhibit excellent properties, such as corrosion resistance, waterproofing, thermal insulation, and ease of processing [3,4,5]. At the same time, WPCs, with their excellent physical properties and durability, provide more innovative space for the optimization and reconstruction of materials and structures, as well as functions in smart furniture [6,7]. In the context of the annual decline in global forest resources and the increasing scarcity of timber resources, WPCs serve as an alternative to solid wood [8]. WPCs are widely used in interior architectural decorations and landscape construction products [9,10].

Milling is a crucial step in the processing of WPCs [11]. However, during the milling process, the wear of cutting tools can lead to increased cutting forces and may result in surface defects such as burrs and pits during the cutting process, thereby reducing the quality of the machining [12,13]. The quality of machining is particularly important for the application of WPCs [14]. Therefore, the development of cutting tools suitable for improving processing efficiency and product quality for these materials has become a new challenge in current research and industrial applications [15,16,17].

To meet the complex cutting requirements, the application of coating technology to woodworking cutting tools is becoming increasingly widespread in the field [18]. Darmawan et al. [19] explored the delamination wear characteristics on the clearance face of newly coated K10 cutting tools during particleboard milling operations. Their experimental results indicated that multilayer-coated tools exhibited less delamination wear compared to monolayer-coated tools. This indicates the potential of multilayer coatings in enhancing high-speed cutting performance for abrasive wood-based materials. Pangestu et al. [20] conducted an analysis on the performance of tungsten carbide tools with AlCrN, TiN, or TiAlN coatings when cutting composite boards. Their findings indicated that the TiAlN-coated carbide tools were superior in terms of wear resistance and produced smoother surfaces on the composite boards, as well as lower noise levels. Darmawan et al. [21] conducted a study on the grooving performance of coated carbide tools across various densities of hardboards and wood-chip cement boards. The study revealed that the coated tools outperformed their uncoated counterparts in terms of wear resistance and maintaining lower cutting forces and noise levels when cutting high-density hardboards and wood-chip cement boards. Kazlauskas et al. [22] conducted a study on the milling of solid oak, comparing the friction coefficient (COF), wear behavior, and the impact of wear on surface roughness between uncoated tools and tools coated with physical vapor deposition (PVD) coatings of CrN and TiAlN. The study indicated that uncoated cemented tungsten carbide (WC–Co) tools exhibited the poorest wear resistance, whereas tools coated with chromium nitride (CrN) demonstrated the best wear resistance. Sheikh–Ahmad et al. [23] examined how tool geometry, edge preparation, and coating materials impact the wear resistance of coated carbides. In summary, by applying high-performance coatings to the tool surface, the surface characteristics of tools can be enhanced, including improved impact toughness, high-temperature hardness, and thermal stability. These improvements enhance the cutting performance of the tools and extend their service life [24,25].

This paper investigates the effects of fluorine nano-coating technology on the cutting performance of cemented carbide tools in the milling of WPCs, mainly focusing on the impact of cutting speed, cutting depth, and fluorine nano-coating on cutting force and surface machining quality. This work is intended to provide a theoretical basis and practical guidance for the efficient machining of WPCs.

2. Materials and Methods

2.1. Material

2.1.1. Workpiece Materials

The WPC used in these experiments is primarily composed of poplar wood, polyethylene (PE), and other additives mixed in a ratio of 5:4:1. The physical and mechanical properties of the WPC are as follows: density is 1263 kg/m³, modulus of elasticity is 2.25 GPa, and modulus of rupture is 21.82 MPa.

2.1.2. Cutting Tools

Figure 1 shows the test tools used, which were provided by Hangzhou Qie MicroNano Technology Co., Ltd., Hangzhou, China. The tool’s blade geometric parameters, provided by the manufacturer, are shown in

Table 1. Geometric parameters of the tool.

Tool Surface	Rake Angle γ (°)	Wedge Angle β (°)	Clearance Angle α (°)	Edge Length (mm)
No coating	15	50	25	22
Coating	15	50	25	22

.

The coating process involved immersing the milling cutter in a heated fluorine nano-composite solution for a defined period (e.g., 15 min), followed by drainage of excess material and subsequent thermal curing at 100 °C for 2 h. The fluorine-based nano-coating used to coat the tool surface is a low-surface energy composition, i.e., weight percentage of 0.1% to 10% fluorinated organic polymeric surfactant, 0% to 10% functional additives, and 80% to 99.9% environmentally friendly fluorinated solvents. The fluorinated organic polymeric surfactant has the structure shown in Figure 2.

In the composition, A1 and A2 are functional end groups, where A1 is a C1–C2 fluorinated or non-fluorinated straight-chain alkyl group, A2 is a C1–C2 fluorinated or non-fluorinated straight-chain alkyl group, A3 is an anchoring end group, and n = 1 to 40.

2.2. Methods

2.2.1. Experiment Setup

The cutting forces and machining quality were tested using the up-milling method on a CNC machine (MGk01A, Nanxing Group Co., Ltd., Dongguan, China). The testing temperature was 24.2 °C. Before testing, tool setting adjustments were performed using the M401T1 numerical control command, and the quartz three-component dynamometer was calibrated. The cutting force was measured using a dynamometer (9257B, Kistler Group, Winterthur, Switzerland) and a Kistler 5017B charge amplifier. The analog voltage signals generated during cutting were collected and converted into digital signals and input into a computer. The accompanying software, Dynoware for the Kistler dynamometer, was then used to analyze and process the measured force signals. During the milling process of the workpiece, vibrations from the machine tool and testing system could affect the acquisition of cutting forces. Analysis of the raw data revealed that the vibration frequency of the machine tool and testing system was approximately 3000 Hz, while the measured cutting force signal frequency was 7300 Hz. A high-pass filter with a cutoff frequency of 6800 Hz was applied to the raw measurement data using Matlab. This filter removed lower-frequency vibrations while retaining the higher-frequency cutting force signal, thereby eliminating the influence of system vibrations and obtaining the true cutting force signal. This process yielded the cutting force data under various cutting conditions. In this study, three components of the cutting force were measured: F_x (parallel to the feed direction), F_y (perpendicular to the feed direction), and F_z (parallel to the axial direction). Given the use of straight cutting edges on tools, the axial force component F_z remains approximately zero based on the experimental data, thereby eliminating the need for specialized consideration in relevant analyses. Consequently, the cutting resultant force F was calculated based on F_x and F_y (Figure 3). The surface roughness of the workpiece was measured using the stylus-type (SURFCOM NEX 001 SD-12 surface roughness tester, Tokyo Seimitsu Co., Ltd., Tokyo, Japan). This surface roughness tester offers automatic measurement capabilities and can perform assessments in horizontal, vertical, inclined, or any arbitrary direction. For each test, cutting forces were measured three times, and a mean value was reported. The resultant force was defined using Equation (1) [26].

$$F=\sqrt{F_x+F_y}$$

(1)

where F stands for the cutting resultant force in newtons (N).

2.2.2. Experimental Design

The effects of various factors on cutting forces and machining quality can be reliably determined through experimental testing [27]. In this work, the cutting parameters were chosen to include the tool surface treatment, cutting depth (mm), and cutting speed (m/min). These parameters were set according to industrial WPC machining. The experiments monitored dependent variables such as cutting forces (N) and surface roughness (μm). Each set of cutting parameters was tested three times, and the mean values for cutting forces and surface roughness were utilized in the analysis. Based on WPC industrial machining practices, the feeding rate was held constant at 5 m/min during the experiment. The tool used had three teeth. When measuring, the surface roughness was assessed over a length of 75 mm on the workpiece. Five distinct points were selected on each workpiece surface, and the surface roughness was measured at each point with a measuring speed of 1.2 mm/s. The arithmetic mean deviation of the profile, denoted as Ra, was chosen as the parameter to evaluate the surface roughness of the workpiece. Ra represents the arithmetic mean of the absolute values of the profile deviations over a sampling length and comprehensively reflects the micro-geometric characteristics of surface roughness. Experimental design details are presented in

Table 2. Experimental design with resulting cutting forces and surface roughness.

Test Number	Surface Treatment	Cutting Speed (m/min)	Cutting Depth (mm)	Cutting Forces (N)	Surface Roughness (μm)
1	Coating	300	1.0	114.5	1.13
2	Coating	400	1.0	109.6	1.09
3	Coating	500	1.0	99.4	1.01
4	Coating	300	1.5	126.7	1.23
5	Coating	400	1.5	119.4	1.14
6	Coating	500	1.5	107.5	1.08
7	Coating	300	2.0	142.5	1.39
8	Coating	400	2.0	138.6	1.28
9	Coating	500	2.0	128.8	1.2
10	No coating	300	1.0	119.6	1.24
11	No coating	400	1.0	115.5	1.2
12	No coating	500	1.0	103.9	1.13
13	No coating	300	1.5	134.4	1.36
14	No coating	400	1.5	127.1	1.24
15	No coating	500	1.5	114.1	1.21
16	No coating	300	2.0	152	1.53
17	No coating	400	2.0	148.9	1.42
18	No coating	500	2.0	136.6	1.33

.

3. Results and Discussion

3.1. Influence of Cutting Speed, Cutting Depth, and Tool Surface Treatment on Cutting Force

Figure 4 is drawn based on the cutting experiment data from Table 2. It depicts how variations in cutting speed, cutting depth, and tool surface treatment influence cutting force. It is evident from the figure that an increase in cutting speed significantly reduces cutting force. Within a unit of time, as the cutting speed increases, the frequency of friction events between the tool and the workpiece increases, which then leads to a rise in the temperature of the contact area. This results in a reduction of the coefficient of friction. The temperature increase also leads to a decrease in the deformation coefficient of the chip. And based on Equation (2), chip thickness is inversely proportional to cutting speed; as the cutting speed increases, the chip thickness decreases (Figure 5). Consequently, the elasticity coefficient of the chip becomes smaller, leading to reduced forces generated by chip deformation. Ultimately, this causes the cutting force in high-speed cutting to be lower than that in low-speed cutting [28]. Figure 4 demonstrates that the cutting force rises with an increase in cutting depth. According to Equation (2), a deeper cut results in greater chip thickness, subsequently enlarging the chip’s cross-sectional area, as illustrated in Figure 6. This results in an increased elasticity coefficient of the chip, causing greater forces to be generated by chip deformation, ultimately leading to an increase in cutting force [29,30]. Additionally, the data in Figure 4 indicate that coated tools consistently exhibit reduced cutting forces compared to their uncoated counterparts. This indicates that the coating has a stable and effective role in reducing the cutting force; this is primarily because the friction coefficient between the coating material and the WPC is lower than that between cemented carbide and the WPC [31]. Therefore, compared to uncoated cutting tools, the frictional force between coated cutting tools and the workpiece is smaller.

The average chip thickness is as follows [32]:

$$a_{av}={\displaystyle \frac{\pi U\sqrt{hD}}{v_{c}Z}}$$

(2)

where D represents the tool diameter in mm, U stands for the feed rate measured in m/min, h indicates the cutting depth in mm, v_c signifies the cutting speed also in m/min, and Z is the number of tool teeth.

3.2. Variance Analysis of Factors Affecting Cutting Force

The impact of various factors on cutting forces was analyzed using analysis of variance (ANOVA) [33,34,35]. The R-squared (R²) and adjusted R-squared (Adj-R²) values for the model of the influence of various factors on cutting force are 0.96452 and 0.95361, respectively. These values are very close to 1, indicating that the model has high accuracy.

Table 3. ANOVA for cutting forces (* Significance, p < 0.05; % Contrib. = percentage contribution).

Source	df	Sum of Squares	Mean Square	% Contrib.	F-Value	p-Value
Tool surface treatment	1	235.445	235.445	5.744	90.347	<0.0001 *
Cutting speed	2	863.898	431.949	21.185	165.751	<0.0001 *
Cutting depth	2	2922.674	1461.337	71.978	560.755	<0.0001 *
Model	5	4022.017	804.403	98.907	308.671	<0.0001 *
Residual	12	31.272	2.606	1.093
Total	17	4053.289		100

presents the results of ANOVA for the effects of various factors on cutting force. The significance level α = 0.05 was used to assess whether factors had a significant impact on cutting force. A statistically significant effect is observed when the p-value is below 0.05; otherwise, the effect is considered insignificant [32]. Our findings show that the model holds statistical significance for the analyzed data, as evidenced by a p-value under 0.05, which corresponds to a 95% confidence level [36]. The F-test critical value table identifies the critical F-values that determine whether effects of the various components on the index are significant [Fα = 0.05 (f1 = 1, f2 = 12) = 4.75; Fα = 0.05 (f1 = 2, f2 = 12) = 3.89; Fα = 0.05 (f1 = 5, f2 = 12) = 3.11]. Table 3 demonstrates that each component has a significant effect on the index. Percentage contribution is the ratio of the sum of squares for a specific source to the total sum of squares, multiplied by 100 [32]. Percentage contribution is commonly employed in quantitative analysis for significance testing. Cutting depth contributed more significantly to cutting force than both cutting speed and tool coating. As a result, ANOVA analysis revealed that cutting depth is the most influential factor on cutting force, followed by cutting speed and tool surface treatment.

3.3. Influence of Cutting Speed, Cutting Depth, and Tool Surface Treatment on Machining Quality

Surface roughness is a key indicator of machining quality as it directly affects the machined surface performance and aesthetic appearance. Figure 7 demonstrates the influence of cutting speed, cutting depth, and tool surface treatment on machining quality, as measured with the surface roughness. Based on Equation (2), an increase in cutting speed results in a reduction in chip thickness, which in turn leads to a lower average cutting volume per tooth. This reduction decreases the impact load on the tool and the edge load, thereby reducing vibrations. As shown in Figure 8, the motion path of the cutter tooth while milling the workpiece is a cycloid, resulting in regular ripples on the machined surface. With all other factors held constant, a higher cutting speed results in a more densely packed cycloidal motion trajectory of the cutting edge, which in turn reduces the surface waviness height of the machined surface. In other words, the higher the cutting speed, the lower the surface roughness of the machined surface and the better the machining quality. These factors collectively contribute to the lower surface roughness observed [37]. Figure 7 also demonstrates that surface roughness augments with increased cutting depth. This correlation arises because deeper cuts correspond to larger cutting cross-sectional areas, which amplify the cutting force. The enlarged cutting volume per pass produces greater impact forces, tool edge loading, and vibration, culminating in heightened surface machining roughness [38,39]. From Figure 7, it can also be observed that with the increase in cutting depth, the surface roughness of the workpiece processed with coated tools remains consistently lower than that processed with uncoated tools. The coating has a stable effect on reducing surface roughness and improving the quality of machining. This is mainly due to the reduced friction between the coating and WPC compared to the interaction between the cemented carbide and WPC, which decreases cutting force and vibration amplitude, consequently lowering the surface machining roughness [31].

3.4. Variance Analysis of Factors Affecting Machining Quality

ANOVA [40,41] was utilized to assess the impact of different factors on machining quality, with the outcomes detailed in

Table 4. ANOVA for machining quality (* Significance, p < 0.05; % Contrib. = percentage contribution).

Source	df	Sum of Squares	Mean Square	% Contrib.	F-Value	p-Value
Tool surface treatment	1	0.068	0.068	22.525	147.852	<0.0001 *
Cutting speed	2	0.070	0.035	23.155	76.476	<0.0001 *
Cutting depth	2	0.157	0.079	51.714	169.572	<0.0001 *
Model	5	0.296	0.059	97.395	127.990	<0.0001 *
Residual	12	0.006	0.00046	2.604
Total	17	0.302		100

. The R-squared (R²) and adjusted R-squared (Adj-R²) values for the model of the influence of various factors on surface roughness are 0.96366 and 0.95587, respectively. These values are very close to 1, indicating that the model has high accuracy. A significance level of α = 0.05 determined the factors’ substantial effect on quality. Results indicate that the model is statistically significant for the data, with a p-value less than 0.05 [42,43]. This indicates a 95% confidence interval. The F-test critical value table identifies the critical F-values that determine whether effects of the various components on the index are significant [Fα = 0.05 (f1 = 1, f2 = 12) = 4.75; Fα = 0.05 (f1 = 2, f2 = 12) = 3.89; Fα = 0.05 (f1 = 5, f2 = 12) = 3.11]. Cutting depth contributed more significantly to surface roughness than both cutting speed and tool coating. It was determined that cutting depth most significantly affects machining quality, followed by cutting speed and tool surface treatment.

4. Conclusions

This work involved cutting performance on WPCs with fluorine-coated tools, examining the impact of tool coating, cutting speed, and cutting depth on cutting forces and machining quality. The main conclusions are as follows:

(1)

Cutting force tends to decrease as the cutting speed increases, and it tends to rise with a deeper cutting depth. For all cutting conditions tested, the cutting force of coated tools was consistently lower than that of uncoated tools, which indicates that the coating of cutting tools plays an important role in reducing cutting forces;

(2)

Surface roughness of the machined surface decreases with increasing cutting speed and increases with increasing cutting depth. For all cutting conditions tested, the surface roughness of workpieces machined with coated tools was consistently lower than that of workpieces machined with uncoated tools, which reveals that the coating has a beneficial impact on reducing surface roughness and improving machining quality;

(3)

The ANOVA showed that cutting depth is the most influential factor on both cutting force and machining quality, followed by cutting speed and tool surface treatment. However, the percentage contributions of cutting speed and tool surface treatment to the surface roughness of the workpiece are 23.2% and 22.5%, respectively, which indicates that they are also important factors influencing the quality of machining.

This study focused on the impact of cutting parameters and surface treatments on cutting force and surface roughness when milling wood–plastic composites. However, cutting temperatures, tool wear, and power consumption are also crucial metrics for evaluating machining performance, which can be further investigated.