Effects of Tool Tooth Number and Cutting Parameters on Milling Performance for Bamboo–Plastic Composite

Abstract

Cutting force and temperature are critical indicators for improving cutting performance and productivity. This study used an up-milling experiment to ascertain the effect of tool tooth number, cutting speed, and depth on the machinability of bamboo–plastic composite. We focused on the changes in the resultant force and cutting temperature under different milling conditions. A response surface methodology was used to build prediction models for the resultant force and temperature. A verification test was conducted to prove the model’s reliability. The empirical findings suggested that the number of tool teeth had the most significant impacts on both the resultant force and the cutting temperature, followed by the depth of cut and the cutting speed. Moreover, the resultant force and cutting temperature showed increasing trends with decreasing numbers of tool teeth and increasing cut depths. However, cutting speed had a negative relationship with the resultant force and a positive relationship with temperature. We also determined the optimal milling conditions with the lowest force and temperature: four tool teeth, 300 m/min cutting speed, and 0.5 mm depth. This parameter combination can be used in the industrial manufacture of bamboo–plastic composite to improve tool life and manufacturing productivity.

1. Introduction

Bamboo–plastic composite (BPC) is a new engineering material produced from bamboo as a reinforcing component and compounded with a polymer resin matrix [1]. BPC breaks through the limitations of traditional bamboo applications and represents a high-quality and high-value-added method of utilizing bamboo. BPC integrates the advantages of bamboo and plastic materials, such as high wear resistance, green construction and environmental protection, and good chemical and mechanical properties [2,3,4]. BPC is used in many fields, including in construction, packaging, furniture, and decoration [5,6,7]. Milling is widely used to form final products with differing sizes and shapes [8]. During this milling process, a rotating cutter is fed into a workpiece to remove unneeded material [9]. Determination of the cutting properties for the plastic matrix composites has recently become an attractive topic of research in the field of materials processing. Wu et al. [10] explored the changes in cutting force during the spiral milling of wood–plastic composite. They demonstrated that the cutting force correlated positively with the spindle speed, helical angle, and depth of cut. Pei et al. investigated cutting temperature in related wood–plastic composite research [11]. They showed that the depth of the cut had the greatest effect on the cutting temperature, followed by the spindle speed and the width of milling. Furthermore, Cao et al. [12] and Zhang et al. [13] discussed the cutting force and temperature for stone–plastic composite. They found that the cutting force and temperature showed similar trends with different parameters. Specifically, the cutting force and temperature increased as the rake angle and cutting depth decreased and as cutting speed increased.

To date, research on BPC has mainly focused on its modification and preparation [14,15,16,17,18]. As far as the authors know, there has been no research on cutting performance in relation to BPC, especially cutting force and temperature. Cutting force and temperature are essential performance evaluation indexes in the BPC machining process. They directly or indirectly affect the machining process, including the energy efficiency of machine tools, tool life, and product quality. Due to the lack of systematic research on BPC, workers still use the traditional cutting parameters for bamboo in industrial production of BPC. However, the difference between BPC and bamboo material means that the traditional cutting parameters cannot meet modern processing requirements. This leads to many machining problems, such as severe tool wear, poor quality, and low productivity. Therefore, developing better cutting performance is crucial to improving BPC production quality and cost.

The objective of this study was to enhance cutting performance in the production of BPC. We determined the changes in cutting force and temperature under different conditions, including the number of tool teeth, the cutting speed, and the depth of the cut. We also determined the optimal cutting combination to provide a baseline and scientific guidance for the industrial production of BPC.

2. Materials and Methods

2.1. Workpiece and Cutting Tool

Commercial BPC was used for the workpieces. It was made from 100 parts per hundreds of rubber (phr) bamboo fibre, 100 phr polypropylene, and 3 phr additives (Yayang Composite Material Technology Co., Ltd., Suzhou, China). The BPC workpieces had a length of 180 mm, width of 80 mm, and thickness of 8 mm. The workpiece properties are given in

Table 1. Properties of the workpieces.

Flexural Strength	Moisture Content	Impact Strength	Tensile Strength	Density
32.4 MPa	1.01%	16.8 KJ/m2	57 MPa	9022 kg/m3

.

Cemented carbide cutting tools (Leuco Precision Tooling Co., Ltd., Suzhou, China) with different numbers of teeth were employed. The tool geometries and properties are shown in

Table 2. Tool geometries and properties of cutting tools.

Rake Angle	Wedge Angle	Clearance Angle	Tool Diameter	Cutting Edge Radius
12°	68°	10°	8 mm	0.02 mm
Blade length	Tool length	Bending strength	Hardness	Thermal conductivity
45 mm	106 mm	1.35 GPa	86 HRA	72.4 W/m·K

and Figure 1a.

2.2. Measurement Equipment

Figure 1 shows how up-milling was conducted under dry machining conditions in a high-speed machining centre (MGK01, Nanxing Machinery Co., Ltd., Guangdong, China). The dynamic cutting temperature was detected using an A20M infrared imaging camera (Figure 1b; Thermo Fisher Scientific Inc., Waltham, MA, USA). The maximum temperature of the cutting zone was obtained with the software ThermaCAM Researcher Pro 2.10 (Thermo Fisher Scientific Inc., Waltham, MA, USA). Furthermore, the dynamic cutting forces in different directions were recorded with a 9257B high precision dynamometer (Figure 1c; Kistler Group, Winterthur, Switzerland) at a sampling rate of 7300 Hz. The resultant force was defined using Equation (1) [19].

$$F_R=\sqrt{F_x{}^2+F_y{}^2},$$

(1)

where F_R stands for the resultant force in Newtons (N), and F_x, and F_y denote the component forces parallel and perpendicular to the feeding direction in N, respectively.

2.3. Measurement Design

This study focused on cutting variables, including the number of cutting tool teeth, cutting speed, and depth. The values for these variables were selected based on related WPC reports and the manufacturer’s guidance (

Table 3. Cutting variables and their values.

Cutting Variables	Number of Teeth	Cutting Speed (m/min)	Depth of Milling (mm)
Values of variables	2, 3, 4	200, 250, 300	0.5, 1, 1.5

). The radial depth of the cut was kept constant at 8 mm. As displayed in

Table 4. RSM design and results.

Runs	Number of Teeth	Cutting Speed (m/min)	Depth of Milling (mm)	Resultant Force (N)	Cutting Temperature (°C)
1	2	200	1.0	225.6	43.7
2	2	250	1.5	277.3	45.3
3	2	250	0.5	185.9	41.2
4	2	300	1.0	199.1	44.9
5	3	250	1.0	130.5	39.6
6	3	300	1.5	146.3	40.7
7	3	250	1.0	131.1	39.3
8	3	300	0.5	120.6	38.5
9	3	200	1.5	139.7	39.3
10	3	250	1.0	132.9	39.6
11	3	200	0.5	124.8	38.4
12	3	250	1.0	132.0	39.5
13	3	250	1.0	131.6	39.6
14	4	300	1.0	103.5	35.9
15	4	250	0.5	85.0	34.6
16	4	200	1.0	118.2	34.6
17	4	250	1.5	120.2	36.2

, we used a response surface methodology (RSM) experimental design [20,21,22] and Design-Expert Version 12 (Stat-Ease, Inc., Minneapolis, MN, USA). The functional relationship between the cutting variables and the response of the resultant force or cutting temperature is shown in Equation (2) [23].

$$Y={\beta }_0+{\displaystyle \sum _{i=1}^k{\beta }_i{X}_i+}{\displaystyle \sum _{ij}^k{\beta }_{ij}{X}_i{X}_j+}{\displaystyle \sum _{i=1}^k{\beta }_{ii}{X}_i^2},$$

(2)

where Y is the response of the resultant force or cutting temperature; β₀ is the constant term; β_i and β_ij are coefficients of the linear and quadratic terms; β_ii is the interacting term; and X_i and X_j denote the cutting variables [24].

3. Results and Discussion

3.1. Cutting Resultant Force and Temperature Models

The regression models for the resultant force (F_R) and cutting temperature (T) are given in Equations (3) and (4).

$$\begin{array}{l}{F}_r=523.02-236.97z+0.45\times {10}^{-1}{v}_c+0.72a_p+0.59\times {10}^{-1}z\cdot v_c\\ -28.10z\cdot a_p+0.11v_c\cdot a_p+32.12z^2-0.85\times {10}^{-3}{v}_c{}^2+13.46a_p{}^2\end{array}$$

(3)

$$T=47.31-3.10z+0.45\times {10}^{-2}{v}_c+2.70a_p-0.5\times {10}^{-3}z\cdot v_c-1.25z\cdot a_p+0.13\times {10}^{-1}{v}_c\cdot a_p$$

(4)

where F_r denotes the resultant force in N, T represents the cutting temperature in °C, z stands for the number of teeth, v_c is the cutting speed in m/min, and a_p is the depth of the cut in mm.

Table 5. Model summary statistics for resultant force (F_r) and temperature (T) models.

Models	Std. Dev.	C.V.%	Adeq Precision	R2	Adj-R2
Fr	10.08	10.08	13.84	0.95	0.91
T	0.50	1.26	33.58	0.98	0.97

lists the resultant force and cutting temperature models’ accuracy evaluation indexes. It shows the standard deviation (Std. Dev.), coefficient of variation (C.V.%), adequate (adeq) precision, R-squared (R²), and adjusted R-squared (Adj-R²) values for these two models. The Std. Dev. and C.V.% represent the degree of dispersion, which was relatively low, thereby suggesting that both models were strongly representative of the results [25]. The adeq precision represented the signal-to-noise ratio. Since the signal-to-noise ratios for both models were higher than 4, as suggested by [26], both models could be adapted to navigate the design space. Furthermore, the R² and Adj-R² values for both models were close to 1, indicating that the models had high accuracy [27]. Figure 2 shows that the predicted points for the resultant force and cutting temperature were near the actual points, and no abnormal points were observed [28]. This meant that the proposed models fit the actual data well and could be used to predict the resultant force and cutting temperature to optimize milling parameters.

3.2. Analysis of Variance for Resultant Force and Cutting Temperature

Figure 3 shows that the resulting force and cutting temperature residuals had normal distributions. However, analysis of variance (ANOVA) was used to further check the statistical normality of the data residuals.

Table 6. ANOVA for the resultant force (*: significant, p-value < 0.05).

Source	Sum of Squares	df	% Cont.	Mean Square	F-Value	p-Value
Model	35,552.00	9	95.84	3950.22	17.91	0.0005 *
z	26,565.12	1	71.61	26,565.12	120.47	<0.0001 *
vc	188.18	1	0.51	188.18	0.85	0.3863
ap	3494.48	1	9.42	3494.48	15.85	0.0053 *
z × vc	34.81	1	0.09	34.81	0.16	0.7030
z × ap	789.61	1	2.13	789.61	3.58	0.1003
vc × ap	29.16	1	0.08	29.16	0.13	0.7269
z²	4342.62	1	11.71	4342.62	19.69	0.0030 *
vc²	19.19	1	0.05	19.19	0.09	0.7765
ap²	47.68	1	0.13	47.68	0.22	0.6561
Residual	1543.55	7	4.16	220.51
Total	37,095.56	16	100

shows the results of the ANOVA for the resultant force with a 0.05 significance level. If the p-value is lower than 0.05, there is a significant effect; otherwise, it is insignificant [29]. The findings suggest that the model was statistically fit for the data (F-value = 17.91; p < 0.05). There is a 0.05% chance that an F-value this large could occur due to noise. Moreover, the ANOVA findings suggested that z, a_p, and z² had statistically significant effects on the resultant force (p < 0.05). However, v_c, z × v_c, z × a_p, v_c × a_p, v_c², and a_p² had insignificant impacts on the resultant force, as their p-values were greater than the 0.05 significance level. Furthermore, the percentage contribution (% Cont.) is the ratio of the source and the total sum of squares. It indicates the degree of effect on the resultant force; that is, the higher the % Cont. value, the higher the factor’s contribution to the resultant force. The % Cont. value for the tool tooth number (z; % Cont. = 71.61%) was higher than those for the depth of cut (a_p; % Cont. = 9.42%) and cutting speed (v_c; % Cont. = 0.51%). Thus, it can be concluded that tool tooth number had the most significant influence on the resultant force, followed by the depth of cut and cutting speed.

Furthermore,

Table 7. ANOVA for cutting temperature (*: significant, p-value < 0.05).

Source	Sum of Squares	df	% Cont.	Mean Square	F-Value	p-Value
Model	156.47	6	98.45	26.08	105.74	<0.0001 *
z	142.80	1	89.85	142.80	579.02	<0.0001 *
vc	2.00	1	1.26	2.00	8.11	0.0173 *
ap	9.68	1	6.09	9.68	39.25	<0.0001 *
z × vc	0.0025	1	0.002	0.0025	0.0101	0.9218
z × ap	1.56	1	0.98	1.56	6.34	0.0305 *
vc × ap	0.4225	1	0.27	0.4225	1.71	0.2199
Residual	2.47	10	1.55	0.2466
Total	158.94	16	100

displays the findings of the ANOVA for cutting temperature with a 0.05 significance level. The results suggested that the model was statistically fit for the data (F-value = 105.74; p < 0.05). Moreover, the ANOVA findings suggested that z, v_c, a_p, and z × a_p had statistically significant effects on the cutting temperature (p < 0.05). However, z × v_c and v_c × a_p had insignificant effects on the cutting temperature. The % Cont. value for the tool tooth number (z; % Cont. = 89.85%) was higher than those for the depth of cut (a_p; % Cont. = 6.09%) and cutting speed (v_c; % Cont. = 0.002%). Thus, it can be concluded that, similarly to the findings for the resultant force, the tool tooth number had the greatest effect on the cutting temperature, followed by the depth of cut and cutting speed.

3.3. Changes in Resultant Force and Cutting Temperature under Different Milling Conditions

Figure 4a–c show the effects of the number of tool teeth, the cutting speed, and the depth of the cut on the resultant force. It should be noted that the resultant force (1) increased with decreasing numbers of teeth and decreasing cutting speed and (2) decreased with decreasing cutting depths. Another key parameter, cutting quantity, affects the magnitudes of the cutting forces and mainly depends on the feed per tooth and average cutting thickness (Figure 5), which are expressed with Equations (5) and (6) [30], respectively:

$$U_Z=\frac{\pi DU}{v_{c}Z}$$

(5)

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

(6)

where U_z is the feed per tooth in mm, D is the tool diameter (8 mm), U is the feed speed (10 m/min), h is the depth of milling in mm, v_c is the cutting speed in m/min, and Z denotes the number of tool teeth. (1) At a constant 250 m/min cutting speed and 1.0 mm cutting depth, the number of tool teeth decreased from four to two, the feed per tooth increased from 0.25 mm to 0.50 mm, and the average cutting thickness increased from 0.089 mm to 0.178 mm. (2) At a constant 1.0 mm cutting depth and with three tool teeth, the cutting speed decreased from 300 m/min to 200 m/min, the feed per tooth increased from 0.28 mm to 0.42 mm, and the average cutting thickness increased from 0.099 mm to 0.148 mm. (3) With a constant number of tool teeth of three and 250 m/min cutting speed, the depth of the cut increased from 0.5 mm to 1.5 mm, the feed per tooth remain unchanged at 0.33 mm, and the average cutting thickness increased from 0.084 mm to 0.145 mm. Overall, with decreases in the number of teeth and the cutting speed, the feed per tooth and average cutting thickness increased; i.e., the cutting quantity increased. Thus, a higher resultant force was generated to remove the unneeded material. Moreover, an increased cutting depth improved the cutting quantity in terms of higher average cutting thickness, thereby leading to a stronger cutting force.

Figure 4d–f show the changes in cutting temperature. It can be seen that the cutting temperature was negatively related to the number of teeth, cutting speed, and depth of milling. Based on previous studies [10,11], the generation of heat during the cutting process is mainly determined by (1) the friction between the tool and workpiece and (2) the deformation rate for the removed material. As stated earlier, a decrease in the number of tool teeth and an increase in the depth of the cut resulted in the removal of a greater amount of unneeded material. This led to greater resistance between the tool and workpiece and an increase in cutting temperature. Furthermore, the increase in the cutting speed increased the friction frequency on the interfaces of the flank face-machined surface and the rake face-chip, thereby resulting in greater cutting heat. In general, the cutting temperature was positively related to the cutting speed and depth and negatively correlated with the number of tool teeth.

The two-level interactions affecting the resultant force and temperature are displayed in Figure 6 and Figure 7, respectively. The density of the contour map indicates that the resultant force was mainly affected by the number of tool teeth in the interaction between the number of tool teeth and the cutting speed (Figure 6a) and in the interaction between the number of tool teeth and the depth of the cut (Figure 6b). In the interaction between the depth of the cut and the cutting speed, the resultant force was mainly affected by the depth of the cut (Figure 6c). The cutting temperature was mainly affected by the number of tool teeth in the interaction between the number of tool teeth and the cutting speed (Figure 7a) and in the interaction between number of tool teeth and the depth of the cut (Figure 7b). In the interaction between the depth of the cut and the cutting speed, the cutting temperature was mainly affected by the depth of milling (Figure 7c).

3.4. Optimization and Verification of Milling Conditions

In industrial manufacturing, blunt tools cause abnormal damage to the machined surface, which greatly affects product quality. This requires the workers to stop the machine and change the tool to continue production. Thus, tool wear is an important factor affecting productivity, surface quality, and machining cost [31,32]. According to one study [33], tool wear mainly depends on the generation of force and temperature during cutting processes. Higher cutting force and temperature accelerate tool wear, especially abnormal wear, such as tipping, cracking, and flaking [33]. Therefore, this work adopted the resultant force and temperature as the optimization targets; i.e., the combination of cutting conditions was optimized to obtain the lowest resultant force and temperature possible.

In accordance with the models established by Equations (3) and (4), the optimal cutting conditions for the production of the lowest resultant force and cutting temperature were assumed to be four tool teeth, a cutting speed of 300 m/min, and a cutting depth of 0.5 mm (Figure 8). With this combination of cutting conditions, the predicted resultant force and temperature were 95.9 N and 35.0 °C. To verify the feasibility of the optimal milling conditions, a verification test was conducted (

Table 8. Verification of resultant force and temperature.

Testing	Number of Teeth	Cutting Speed (m/min)	Depth of Milling (mm)	Resultant Force (N)	Cutting Temperature (°C)
Optimization	4	300	0.5	95.9	35.0
Verification	4	300	0.5	88.6	33.1
Error rate	/	/	/	−7.6%	−5.4%

). With these optimized cutting conditions, the measured resultant force and cutting temperature were 88.6 N and 33.1 °C, with error rates in a reasonable range: −7.6% and −5.4%, respectively. Thus, it can be concluded that the developed models of the resultant force and temperature were reliable. The best milling conditions—namely, those that resulted in the lowest force and temperature—were, therefore, determined to be four tool teeth, a cutting speed of 300 m/min, and a cutting depth of 0.5 mm.

4. Conclusions

In this study, a series of milling experiments were conducted. The influences of the number of tool teeth and the cutting parameters on the milling performance in the production of BPC were discussed, and the main conclusions are as follows:

• With increases in the number of tool teeth and the depth of the cut, both the resultant force and the cutting temperature showed increasing trends. Increased cutting speed led to a lower resultant force and higher cutting temperature;
• Predicted models for the resultant force and cutting temperature were obtained and verified, and the significance of each of the terms in the two models was analysed using ANOVA. Among the three cutting variables, the number of tool teeth had the greatest impact on both the resultant force and cutting temperature, followed by the depth of the cut and the cutting speed;
• Based on the developed models, milling conditions were optimized to produce the lowest force and temperature: four tool teeth, a cutting speed of 300 m/min, and a cutting depth of 0.5 mm;
• This work focused on the changes in cutting force and temperature in the milling of BPC. However, cutting quality, tool wear, and cutting energy consumption are also important evaluation indicators for cutting performance, and they affect both product quality and machining costs. Thus, future research is needed to examine cutting quality, tool wear, and cutting energy consumption under different cutting conditions.