Optimizing Dual-Microstructure Parameters in Ball-End Milling Tools: Synergistic Effects and Parameter Combination Analysis

Abstract

To address the issues of high cutting speeds and low surface precision during milling, this study investigates the effects of front and back cutting face microstructures on ball-end milling cutters processing 304 stainless steel. Firstly, a theoretical energy model for front and back cutting face microstructures is established to verify the feasibility of embedding microstructures. Then, finite element analyses are conducted on cutters with varying microstructure parameters on front and back cutting faces to determine reasonable parameter ranges. Parameter combinations are subsequently used to manufacture front/back microstructured cutters, which undergo FEA validation. Finally, milling experiments are designed with milling forces, tool wear, and workpiece surface roughness as evaluation metrics. The results demonstrate that front/back microstructured cutters reduce milling forces by 19.4%, cutting temperatures by 19%, and workpiece surface roughness (Sa) by 43% compared to non-microstructured cutters, while significantly mitigating tool wear.

1. Introduction

During milling processes, severe frictional contact between the tool surface and workpiece leads to issues such as high temperatures, rapid tool wear, excessive cutting forces, and poor surface quality on the workpiece. These problems are exacerbated under high-speed cutting and dry machining, and when processing hard-to-machine materials, resulting in increased cutting forces, elevated temperatures, and reduced tool life [1]. To address these challenges, biomimetic approaches can be applied. Biomimetic studies reveal that certain animals (e.g., earthworms, pangolins) possess microstructures on their epidermis or exoskeletons, which exhibit friction reduction, anti-adhesion, and hydrophobic properties. Accordingly, fabricating microstructures with diverse morphologies on tool surfaces enhances cutting performance and mitigates tool wear [1,2,3], making this a prominent research focus.

To validate the efficacy of tool surface microstructures in enhancing milling performance, numerous scholars have conducted finite element analyses (FEAs) on microstructures with varied geometries and morphologies [4]. Li Yuanyuan et al. [5] fabricated microstructures on the front cutting face and revealed through FEA and experiments that front cutting face microstructures reduce chip-tool friction and mitigate cutting temperature. Gao Huanhuan et al. [6] designed multiple-surface microstructure types on front cutting faces, demonstrating that groove-shaped microstructures most effectively reduce drilling forces, temperatures, and drill bit wear. Chen Jin et al. [7] developed crescent-shaped microstructure tools inspired by the smooth internal structure of pitcher plants, integrating them into front cutting faces to enhance cutting performance.

Ni Jing et al. [8] fabricated raindrop groove microstructures on the back cutting face of broaching tools. Studies have shown that microstructured tools outperform conventional ones by reducing tapping temperatures, minimizing the thermal deformation of cutting edges, and enhancing tapping performance. Fatima et al. [9] developed bio-inspired scale-shaped microstructures on back cutting faces, inspired by the wear-resistant properties of reptile scales, improving tool wear resistance. Tiffany D. L. et al. [10] designed sub-back cutting face groove-type microstructure tools, validated through titanium alloy machining to effectively reduce adhesion and extend tool life.

Current research indicates that rationally designed surface microstructure cutting tools can enhance cutting performance; however, the effects of microstructures applied at different tool locations exhibit varying impacts on performance improvements. Due to insufficient research on rake face and flank face microstructure, a biomimetic arc-shaped microstructure cutting tool three-dimensional modeling diagram was developed based on the carp arc-shaped scale microstructure shown in Figure 1, as illustrated in Figure 2. This design replicates the natural arcuate scale morphology observed in carp scales to optimize stress distribution. Experimental investigations focused on elucidating the synergistic effect mechanism between the front and back face microstructures.

2. Modeling the Frictional Energy of the Microstructure-Dominated Chip–Contact Interface

2.1. Theoretical Modeling of the Weaving Structure of the Front Blade Surface

A microstructured ball-end milling cutter with planar-edge microtextures was selected for modeling. To simplify the analysis of the cutting process, the energy generated during metal chip formation was categorized into two types, shear energy in the first deformation zone and frictional energy in the second and third deformation zones, as illustrated in Figure 3. According to the functional mechanism of microstructures, their friction reduction and wear resistance primarily manifest in the second and third deformation zones.

The cutting energy U per unit of time is divided into two main parts, the shear energy per unit of time Us and the friction energy per unit of time $U_f$ [11], as shown in Equations (1)–(3).

$$U=U_s+U_f$$

(1)

$$U_s=F_s\times V_s$$

(2)

$$U_f=F_f\times V_c$$

(3)

where Fs is the shear force; Vs is the shear velocity; F_f is the friction force; Vc is the chip velocity. The microwaving cutting model of the front surface of the blade is shown in Figure 4.

The shear force Fs in Figure 4 can be expressed as follows:

$$F_s={\tau }_s{A}_s$$

(4)

where ${\tau }_S$ is the shear strength of the workpiece; $A_s$ is the shear area.

According to the micro-weave cutting model of the front blade face in Figure 2, the relationship between the shear force F_s, friction force F_f, and the combined force F is shown in Equations (5)–(7).

$$F_s=F\mathrm{c}\mathrm{o}\mathrm{s}\left(\phi +\beta -{\gamma }_0\right)$$

(5)

$$F_f=Fsin\beta$$

(6)

$$F=\sqrt{F_s^2+F_f^2}$$

(7)

where $\beta$ is the friction angle; ${\gamma }_{o }$ is the precession angle Equation; $\phi$ is the shear angle.

The combination of Equations (4)–(6) results in $F_f$:

$$F_f={\tau }_s{A}_s\left[{\displaystyle \frac{sin\beta }{cos\left(\phi +\beta -{\gamma }_0\right)}}\right]$$

(8)

The effective frictional area between the tool and chip is set as $Q_p$, and the relationship between $Q_p$ and $F_f$ is given by Equation (9).

$$F_f={\tau }_s{Q}_p$$

(9)

From Equations (8) and (9), the expression for $Q_p$ is changed to Equation (10).

$$Q_p=A_s{\displaystyle \frac{sin\beta }{cos\left(\phi +\beta -{\gamma }_0\right)}}$$

(10)

The shear velocity $V_s$ and chip velocity $V_c$ are related to the cutting speed $V$, lead angle, and shear angle, as shown in Equation (11) and Equation (12), respectively.

$$V_s={\displaystyle \frac{Vcos\alpha }{cos\left(\phi -{\gamma }_0\right)}}$$

(11)

$$V_c={\displaystyle \frac{Vsin\phi }{cos\left(\phi -{\gamma }_0\right)}}$$

(12)

Based on Equation (3) and Equation (9), the expression for the friction energy at the front face during cutting is as follows:

$$U_f={\tau }_s{Q}_p{V}_c$$

(13)

The microtexture is placed in the chip contact area of the ball-end mill, and the effective friction area of the actual contact after the weaving is placed is called $Q_p^1$.

$$Q_p^1=Q_p-nS$$

(14)

where $S$ is the area of a single microtome, $Q_p$ is the effective friction area, and $n$ is the number of microtomes.

In summary, the energy of new friction between the front face of the microtexture configuration ball-end mill and the chip is as follows:

$$U_f={\tau }_s{Q}_p^1{V}_c$$

(15)

Equations (14) and (15) show that incorporating the microtexture structure on the face of the ball-end mill reduces the effective frictional surface between the tool and the chip, thus reducing the frictional energy to a certain extent.

2.2. Theoretical Modeling of the Weaving Structure of the Rear Blade Surface

During milling, plastic deformation occurs on the frictional surfaces of the tool, storing strain energy in deformed regions. As the original surface wears away, new surfaces formed during subsequent machining generate additional deformation. These accumulated strain energies are predominantly converted into heat during the milling process [12].

During milling, the front and back cutting faces exhibit similar frictional interactions with the workpiece, but the back cutting face demonstrates two distinct friction regimes: one characterized by tight-contact dominance, as shown in the AB segment deviating from Coulomb friction theory, and another partially obeying classical sliding friction laws where frictional force correlates linearly with normal pressure as shown in the BD segment presented in Figure 5 illustrating back cutting face microstructures [13].

Assuming the relative sliding velocity between the back cutting face and the machined surface over the entire contact zone equals the shear velocity ${v }_s$ of the workpiece, the friction coefficient ${\mu }_{\left(x\right)}$ on the back cutting face can be expressed as Equation (16), where a, c, and k are undetermined parameters satisfying 0 < k < 1. As shown in Figure 5, Point A represents the intersection of the back cutting face and the cutting edge arc, where the tangential contact stress is zero. The total length of the back cutting face friction zone AD is denoted as l_AD.

$$\mu \left(x\right)=\left\{\begin{array}{c}{\displaystyle \frac{a{e}^{-C{V}_s}}{{\sigma }_{\left(x\right)}^k}} 0\le x\le B\\ {\mu }_0 B\le x\le D\end{array}\right.$$

(16)

In the contact zone AB between the back cutting face and the machined workpiece, normal stress induces plastic strain during tool–workpiece contact, achieving tight contact. Embedding microstructures in zone AB effectively reduces frictional interaction.

The normal contact stress ${\sigma }_{\left(x\right)}$ in the friction region of the back surface of the tool is distributed in an exponential form as shown in Figure 5; in the contact region near the cutting edge of the tool, the material will flow plastically and the normal contact stress of the tool will change drastically, which can be expressed as Equation (17).

$${\sigma }_{\left(x\right)}={\sigma }_{max}{(1-{\displaystyle \frac{x}{l}})}^{\xi }$$

(17)

where $\xi$ is the stress distribution coefficient of the tool surface, ${\sigma }_{max}$ is the maximum positive stress at the cutting edge of the tool, and $x$ is the distance from any point in the friction region to point A.

$${\tau }_{\left(x\right)}={\mu }_{\left(x\right)}·{\sigma }_{\left(x\right)}$$

(18)

The sectional distance of the friction area $AB$ is $l_{AB}$, the sectional distance of the friction area BD is $l_{BD}$, the total friction length is $l_{AD}$, the friction force in the close contact area between the rear blade surface of the micro-weaving tool and the workpiece is $F_{fa}$, and the normal force in the total friction area of the rear blade surface of the tool is $F_{fa}$. The relationship between the two and the tangential contact stress and the normal contact stress is represented by Equations (19) and (20).

$$F_{fa}={\int }_A^{l_{AB}}{\sigma }_{\left(x\right)}·{\mu }_{\left(x\right)}=a·e^{-CVs}·{\sigma }_{max}^{1-k}·{\displaystyle \frac{l_{AB}}{-\xi k+\xi +1}}$$

(19)

$$F_{f\sigma }={\int }_O^{l_{AD}}{\sigma }_{max}·{\left(1-{\displaystyle \frac{x}{l_{AD}}}\right)}^{\xi }dx={\sigma }_{max}·\left({\displaystyle \frac{l_{AD}}{\xi +1}}\right)$$

(20)

The expression for the friction energy $U_C$ on the back face was calculated as (21), employing the identical energy model for the chip–contact interface on the front face.

$$U_c=\left(F_{fa}+F_{f\sigma }\times {\mu }_0\right)·V_s$$

(21)

Analysis based on Equation (19) indicates that the frictional force on the back cutting face is proportional to the contact length l_AB in the tight-contact zone. Embedding microstructures on the back cutting face reduces the contact length l_AB, thereby decreasing frictional force and frictional energy generation. Equation (20) further reveals that the normal stress generated by the interaction between the back cutting face and machined surface is directly correlated with the total contact length l_AD. By embedding microstructures in non-tight-contact zones (where friction coefficients remain constant), normal stress is reduced, achieving frictional energy reduction. Ignoring heat dissipation to the atmosphere, frictional energy between the back cutting face and workpiece is fully converted into heat; thus, microstructure integration on the back cutting face partially mitigates heat generation.

Based on theoretical studies on microstructure integration on rake and back cutting faces, the feasibility of microstructure-enhanced tooling was demonstrated. A finite element analysis (FEA) computational model was established to simulate machining processes, with experimental validation conducted through tribological and wear resistance assessments.

3. Finite Element Modeling

The question about the chemical composition of stainless steel 304 is investigated in this study, which employs 304 stainless steel as the material [14] for simulations and experiments. Briefly, 304 stainless steel is a versatile austenitic stainless steel with a chemical composition comprising the following: carbon (C) ≤ 0.08%, silicon (Si) ≤ 1.00%, manganese (Mn) ≤ 2.00%, phosphorus (P) ≤ 0.045%, sulfur (S) ≤ 0.03%, chromium (Cr) 17.00–20.00%, and nickel (Ni) 8.00–10.50%. This material exhibits excellent corrosion resistance, formability, and weldability, making it highly suitable for precision machining applications.

Building upon the front cutting face analysis, finite element analysis (FEA) was conducted for simulation and experimental validation. The workpiece material was defined as a 20 mm × 10 mm × 5 mm 304 stainless-steel cube. Key parameters included a milling speed of 5000 r/min, a milling depth of 3 mm, and an initial material temperature of 25 °C. The material model parameters and 304 stainless steel properties are listed in

Table 1. Material parameters of 304 stainless steel based on Johnson Cook model.

A/mpa	B/mpa	C	n	m
452	694	0.0067	0.996	0.311

and

Table 2. Basic parameters of 304 stainless steel.

Elastic Modulus (GPa )	Density (kg/m3)	Poisson’s Ratio	Specific Heat (J/(kg·K))	Thermal Conductivity (W/(m·K))	Melting Point (°C)
199	7850	0.3	500	15	1400

. Finite element software: Abaqus 2019 was used to create four tool configurations, non-textured tools, front-cutting-face-textured tools, back-cutting-face-textured tools, and combined front/back-cutting-face-microstructured tools, followed by FEA-based simulations.

This study designates four tool configurations with abbreviations: T₁ (non-microstructured tool), T₂ (front-cutting-face-microstructured tool), T₃ (back-cutting-face-microstructured tool), and T₄ (front-/back-cutting-face-microstructured tool).

4. Orthogonal Tests of Microstructure Parameters

4.1. Effect of Different Microstructure Parameters on Milling Force on the Front Face of the Cutter

In the design of microstructure cutting tools, the geometric parameters of groove microstructures significantly influence cutting performance. Research indicates that when the groove texture width (D) is D = 50 μm, a balanced state between oil retention capacity and stress distribution can be achieved. However D = 90 μm may induce stress concentration; thus, the recommended range for D is 30 μm, 50 μm, and 70 μm. For microstructure depth (H), a H = 40 μm configuration demonstrates superior oil film load-carrying capacity, while H = 70 μm reduces tool rigidity. Consequently, H should be selected from 20 μm, 40 μm, and 60 μm. Regarding microstructure spacing (L₁), a spacing of L₁ = 120 μm ensures favorable hydrodynamic lubrication effects between adjacent microstructure units, whereas excessive spacing at L₁ = 150 μm may cause oil film rupture. Therefore, L₁ should range within 100 μm, 120 μm, and 140 μm. For the distance from microstructures to the cutting edge (L₂), L₂ = 120 μm effectively prevents chip adhesion while maintaining edge strength. When L₂ = 160 μm, the microstructures become too distant from the cutting edge to fully utilize their beneficial effects, making the permissible range for L₂ 80 μm, 100 μm, and 120 μm.

A four-factor three-level 9-group orthogonal experimental design was established and an orthogonal array, shown in

Table 3. Nine sets of microtexture-parameter-factor-level table.

Factor	D	H	L1	L2
1	30	20	100	80
2	30	40	120	100
3	30	60	140	120
4	50	20	120	120
5	50	40	140	80
6	50	60	100	100
7	70	60	140	100
8	70	20	100	120
9	70	40	120	80

, was created. The data were imported into Abaqus 2019 software: Abaqus for simulation, and front-cutting force results, presented in

Table 4. The results of the temperature of the front cutting surface and milling force.

Test Number	Milling Force (N)
1	410.4 N
2	383.6 N
3	362.7 N
4	392.2 N
5	406.5 N
6	356.8 N
7	407.4 N
8	394.3 N
9	415.6 N

, were obtained using nine distinct experimental parameter sets.

Based on the milling force simulation data from Table 4, range analysis was applied to process the data and generate an intuitive analysis table for front-cutting force, as shown in

Table 5. Variance analysis of milling forces on the front face of a cutting tool.

Factor	D	H	L1	L2	Result
Experiment 1	1	1	1	1	410.4 N
Experiment 2	1	2	2	2	383.6 N
Experiment 3	1	3	3	3	362.7 N
Experiment 4	2	1	2	3	392.2 N
Experiment 5	2	2	3	1	406.5 N
Experiment 6	2	3	1	2	356.8 N
Experiment 7	3	3	3	2	407.4 N
Experiment 8	3	1	1	3	394.3 N
Experiment 9	3	2	2	1	415.6 N
Average value 1	385.567	403.333	387.167	410.833
Average value 2	385.167	394.800	397.133	382.600
Average value 2	405.767	378.367	392.200	383.067
Variance	20.600	24.966	9.966	28.233

.

A range analysis was conducted on

Table 6. Variance results of milling force.

Factor	D	H	L1	L2
Variance	20.600	24.966	9.966	28.233

, revealing the order of range values for the four factors as L₂ > H > D > L₁. The magnitude of the range reflects the degree of influence on cutting force, establishing the impact hierarchy: L₂ > H > D > L₁. The findings indicate that L₂ exhibits the highest variance contribution, signifying its significant effect on cutting force, while L1 has the smallest variance contribution. This ranking aligns with Song Shuangzhu’s [15] research on the relationship between microstructure geometric parameters and milling force. The variation trend of tool parameters’ influence on cutting force is illustrated in Figure 6.

As shown in Figure 4, the cutting force exhibits an initial decrease followed by an increase as L₂ increases. During L₂ adjustment from Level 1 to Level 2, the stress concentration at the tool–chip interface decreases, enhancing the tool’s wear and friction reduction performance, thereby reducing the milling force. However, L₂ increases from Level 2 to Level 3, and the microstructure’s effectiveness in force reduction diminishes, leading to increased cutting forces. For parameter H, increasing H from Level 1 to Level 3 progressively reduces the milling force. This is attributed to the enhanced debris collection capacity of deeper microstructures, which lowers cutting resistance. Conversely, as L₁ increases, the cutting force initially rises. For L₁, from Level 1 to Level 2, the enlarged-texture area improves debris collection, reducing forces. However, for L₁, at Level 3, reduced groove density per unit area weakens wear and friction reduction effects, resulting in force escalation.

In conclusion, when selecting parameters based on tool milling force criteria, the optimal groove microstructure parameters derived from Table 4 are as follows: D = 40 μm, H = 60 μm, L₁ = 100 μm, and L₂ = 100 μm.

4.2. Temperature Effects of Different Microtexturing Parameters on the Front Blade Surface

The microstructure parameters from Table 3 were imported into finite element simulation software: Abaqus 2019 for simulations, obtaining the front cutting face temperature results shown in

Table 7. The result of milling temperature.

Test Number	Temperature (°C)
1	226.3 °C
2	181.6 °C
3	147.3 °C
4	192.5 °C
5	164.1 °C
6	179.5 °C
7	172.6 °C
8	217.8 °C
9	184.6 °C

during milling. These data were processed using range analysis, shown in

Table 8. Polar visualization analysis of milling temperatures.

Factor	D	H	L1	L2	Result
Experiment 1	1	1	1	1	226.3 °C
Experiment 2	1	2	2	2	181.6 °C
Experiment 3	1	3	3	3	167.3 °C
Experiment 4	2	1	2	3	192.5 °C
Experiment 5	2	2	3	1	164.1 °C
Experiment 6	2	3	1	2	179.5 °C
Experiment 7	3	3	3	2	172.6 °C
Experiment 8	3	2	1	3	217.8 °C
Experiment 9	3	1	2	1	184.6 °C
Average value 1	185.067	197.133	207.867	191.667
Average value 2	178.700	187.833	186.233	177.900
Average value 3	191.667	170.467	161.333	185.867
Variance	12.967	26.666	46.534	13.767

, to derive intuitive temperature distribution metrics for the front cutting face.

Based on the data from Table 8, a temperature range analysis chart for front-cutting-fac- microstructure tools was generated, as illustrated in Figure 7.

According to

Table 9. Variance analysis of milling temperature.

Factor	D	H	L1	L2
Variance	12.967	26.666	46.534	13.767

, the descending order of ranges for the four microstructure parameters is L₁ > H > L₂ > D. The magnitude of the range reflects the influence degree on temperature, thereby determining the parameter impact sequence on temperature as follows: L₁ > H > L₂ > D. This indicates that L1 exerts the most significant influence on temperature, while D has a minimal effect.

As shown in Table 8 and Figure 7, the temperature of front-cutting-face-microstructure tools exhibits distinct trends with parameter variations: as L₁ increases from Level 1 to Level 3, the temperature initially decreases due to optimized L₁ reducing the chip friction area while increasing the microstructure’s heat dissipation surface area, enhancing heat exchange with external media. When H is elevated from Level 1 to Level 3, temperature decreases as the enlarged microstructure’s surface area improves the overall heat dissipation capacity. For D, temperature reduction occurs at Level 1 to Level 2 due to wider grooves improving chip containment and wear resistance, but rises at Level 3 as the excessive groove width forms cutting edges that induce secondary cutting effects, increasing heat generation. With L₂ increasing from Level 1 to Level 2, temperature declines as microstructures within the effective cutting zone enhance heat dissipation; however, further elevation to Level 3 causes a temperature rise due to microstructures exceeding the optimal cutting range, reducing efficacy and increasing cutting heat.

In conclusion, when selecting a tool’s microstructure parameters based on cutting temperature criteria, the optimal groove microstructure parameters are determined as follows: D = 50 μm, H = 40 μm, L₁ = 140 μm, and L₂ = 80 μm.

When simultaneously evaluating the combined effects of temperature and cutting force on cutting tools, this study utilized Grey Relational Analysis (GRA) to analyze Table 4, Table 5, Table 7, and Table 8. The results, as shown in

Table 10. Grey Relational Analysis (GRA) results.

Factor	Average Correlation Coefficient (Force)	Average Correlation Coefficient (Temperature)	Grey Comprehensive Correlation Degree
D	0.782	0.694	0.738
H	0.821	0.733	0.777
L1	0.698	0.719	0.743
L2	0.635	0.621	0.678

, revealed that among the microstructural parameters (D, H, L₁, L₂), H exhibited the highest relational degree (0.777, optimized to 60 μm), followed by D (diameter, 0.738, optimized to 50 μm). In contrast, the microtexture spacing parameters for L1/L2 showed lower correlations (0.743/0.678, optimized to 100 μm each). The optimal parameter combination was determined as D = 50 μm, H = 60 μm, L₁ = 100 μm, and L₂ = 100 μm.

4.3. Effect of Milling Force on the Back Face of the Cutter with Different Microwaving Parameters

Nine groups of parameters, shown in

Table 11. Visual analysis of milling temperature.

Factor	D	H	L1	L2
Experiment 1	40	30	120	90
Experiment 2	40	40	140	110
Experiment 3	40	50	160	130
Experiment 4	50	30	140	130
Experiment 5	50	40	160	90
Experiment 6	50	50	120	110
Experiment 7	60	30	160	110
Experiment 8	60	40	120	130
Experiment 9	60	50	140	90

, with identical quantities but varying configurations were designed for back-cutting-face microstructures, mirroring the parameter count of front-cutting-face microstructures to ensure uniformity. Simulation results of temperature and cutting force for tools with varying microstructure parameters are presented in

Table 12. Temperature and cutting force results.

Factor	Cutting Force	Temperature
Experiment 1	425.6 N	216.4 °C
Experiment 2	426.7 N	221.2 °C
Experiment 3	411.3 N	222.7 °C
Experiment 4	420.7 N	231.1 °C
Experiment 5	426.9 N	199.0 °C
Experiment 6	418.6 N	211.3 °C
Experiment 7	417.8 N	206.8 °C
Experiment 8	421.6 N	215.4 °C
Experiment 9	412.7 N	185.7 °C

.

According to Table 12, microtextures on the flank face demonstrate minimal improvement in milling force optimization, eliminating the need for range analysis. Based on milling force considerations, the optimized microstructure parameters for the flank face are configured as follows: D = 40 μm, H = 50 μm L₁ = 160 μm, and L₂ = 130 μm.

4.4. Effect of Temperature on the Back Face of the Cutter with Different Microtexturing Parameters

Compared to the impact of back-cutting-face microstructures on milling force, microstructure effects on temperature are more significant. Simulation data derived from experiments, processed via range analysis, yield intuitive temperature analysis results for back-cutting faces in

Table 13. Variance analysis chart of rear blade surface temperature.

	D	H	L1	L2	Result
Factor
Experiment 1	1	1	1	1	226.3
Experiment 2	1	2	2	2	181.6
Experiment 3	2	3	3	3	167.3
Experiment 4	2	1	2	3	192.5
Experiment 5	2	2	3	1	164.1
Experiment 6	3	3	1	2	179.5
Experiment 7	3	3	3	2	172.6
Experiment 8	3	2	1	3	217.8
Experiment 9	3	1	2	1	184.6
Average value 1	185.067	197.133	207.867	191.667
Average value 2	178.700	187.833	186.233	177.900
Average value 3	191.667	170.467	161.333	185.867
Range	12.967	26.666	46.534	13.767

.

Using range analysis on the range values of the data, the temperature effect curve of microstructures is derived and visualized in Figure 8.

During the tool simulation process, the tool temperature gradually increases. As L₁ increases, the tool temperature initially shows a significant downward trend. This is attributed to the reduced friction area between chips due to optimized microstructure spacing, while the total microstructure area increases, enhancing heat dissipation efficiency at the cutting edge. When H increases, the tool temperature decreases markedly, as the inner surface area of the microstructures expands, improving the tool’s heat dissipation capacity. An increase in D further reduces tool temperature by enlarging chip containment volume and enhancing anti-friction performance. Conversely, as L₂ increases, the tool temperature rises because the effective cutting section of the microstructures diminishes, leading to greater heat accumulation.

Considering both tool temperature and cutting force, based on Grey Relational Analysis (GRA) from Table 11, Table 12 and Table 13, the optimal back microstructure parameters for the cutting tool are determined as D = 40 μm (groove width), H = 50 μm (groove depth), L₁ = 140 μm (spacing), and L₂ = 90 μm (density), as shown in

Table 14. Back microstructure results from GRA.

Factor	Average Correlation Coefficient (Force)	Average Correlation Coefficient (Temperature)	Grey Comprehensive Correlation Degree
D	0.635	0.658	0.667
H	0.612	0.765	0.683
L1	0.722	0.782	0.768
L2	0.745	0.743	0.785

. This parameter combination demonstrated the highest comprehensive relational degree (D: 0.667, H: 0.683, L₁: 0.768, L₂: 0.785) in the GRA ranking, confirming its superiority in minimizing tool temperature while balancing cutting force requirements.

5. Simulation Study of Front- and Back-Microstructure Tools

Based on simulation studies of T₂ and T₃, appropriate front cutting face and back cutting face microstructure parameters were selected for application on T₄ tools to conduct simulation experiments. The front-cutting face parameters for T₄ tools were defined as follows: D = 50 μm, H = 60 μm, L₁ = 100 μm, and L₂ = 100 μm. The back cutting face microstructure parameters were specified as follows: D = 40 μm, H = 50 μm, L₁ = 140 μm, and L₂ = 90 μm.

A comparative analysis of temperature and milling force simulation results for four tool configurations is presented in Figure 9. During the initial simulation phase (AB segment), the T₄ tool exhibited a slower temperature rise compared to other tools. In the BC segment, microstructures became active: they initiated chip collection and reduced friction between the cutting face and workpiece, thereby decelerating the temperature increase. Notably, T₄ maintained a more stable thermal profile than other tools, achieving a significantly lower peak temperature at the machining completion stage.

A comparative analysis of average temperatures for four tool configurations is shown in Figure 10, with the temperature ranking as T₁ > T₃ > T₂ > T₄. The dual microstructures of T₄ demonstrated superior thermal performance by reducing a temperature rise, caused by metal shear slip and extrusion friction near the cutting edge on the back cutting face, further minimizing tool–chip friction, lowering frictional heat generation, and enhancing the heat dissipation area. Compared to T₁, T₄ achieved a 19% reduction in temperature, demonstrating its effectiveness in lowering tool temperature and significantly improving overall thermal conductivity.

As shown in Figure 11, during the AB segment, the tool begins milling the workpiece. The workpiece undergoes elastic deformation under tool indentation, with shear stress continuously increasing until the material reaches its maximum allowable strain. At this point, the cutting layer fractures, causing an instantaneous stress drop on the tool. In the BC segment, the metal is separated from the workpiece and flows along the front cutting face as chips. Here, the front cutting face microstructures reduce friction between chips and the tool. Simultaneously, the back cutting face microstructures store chips, lowering the temperature on T₄’s back cutting face and indirectly reducing milling forces.

The average milling forces for the four tool configurations are presented in Figure 12. The findings indicate that the milling forces of T₄ and T₂ are comparable, with T₄ showing a 14% reduction compared to T₃ and a 16% decrease relative to T₁. Compared to T1 and T₂, T₄ demonstrates superior capability in reducing milling forces generated during machining.

6. Milling Test

6.1. Milling Force Test Analysis

The T₂, T₃, and T₄ tools in Figure 13 were fabricated via fiber laser machining. The simulation-derived tool parameters were applied to the experimental tools, which underwent milling tests on a three-axis vertical machining center using unidirectional climb milling. The milling parameters matched simulation settings to ensure accuracy: spindle speed = 5000 r/min, feed rate = 0.5 m/min, milling path length = 120 mm, and cutting depth = 0.35 mm. The workpiece was clamped at a 0° angle relative to the machine tool guide rail horizontal plane. Milling forces, tool wear amounts, and workpiece surface roughness were analyzed for all three tool configurations.

Milling tests were conducted on four tool configurations. During stable milling phases, force dynamometers were used to capture milling forces, as shown in Figure 14 and Figure 15.

The milling forces for the four tool configurations are shown in Figure 16, Figure 17, Figure 18 and Figure 19. The resultant milling force calculation is presented in Equation (22).

$${ F}_{Resultant}=\sqrt{{Fx\left[N\right]}^2+{Fy\left[N\right]}^2+{Fz\left[N\right]}^2}$$

(22)

As shown in Figure 16, Figure 17, Figure 18 and Figure 19 and Equation (22), the resultant milling forces for the four tool configurations are as follows: T₁ (336.4 N), T₂ (319.3 N), T₃ (288.1 N), and T₄ (274.3 N). Compared to T₁, T₂, T₃, and T₄ demonstrated varying degrees of milling force reduction (5%, 14.3%, and 19.5%, respectively). This reduction is attributed to the microstructures’ ability to store and reduce the chip volume, thereby decreasing cutting forces and minimizing tool–workpiece friction. Enhanced chip storage lowers contact pressure between chips and tool surfaces, further reducing vibration during machining and improving stability. Consequently, the microstructured tools exhibited significant force reductions compared to the non-microstructured tool.

Compared to T₁, T₂, T₃, and T₄ demonstrated varying degrees of milling force reduction (5%, 14.3%, and 19.5%, respectively). This reduction is attributed to the microstructures’ ability to store and reduce chip volume, thereby decreasing cutting forces. Enhanced chip storage minimizes contact between chips and tool surfaces, reduces tool–chip friction, further mitigates vibration during machining, and improves stability. Consequently, the resultant milling forces of microstructured tools were significantly lower than those of the non-microstructured tool. Figure 20 compares experimental and simulation results for T₁ and T₄. Despite numerical discrepancies, the trend consistency between experimental and simulated milling forces is evident.

6.2. Tool Wear Test Analysis

A high-precision three-dimensional microscope with an extended depth of field, shown in Figure 21, was used to systematically characterize the wear morphologies of the four microstructured tools (T₁, T₂, T₃, T₄) under continuous milling conditions. The key observation areas selected were the front cutting face and main back cutting face regions of the tools.

The wear patterns of the T₁, T₂, T₃, and T₄ microstructured tools were systematically observed. Figure 22 shows magnified wear on T₁, revealing severe edge chipping and significant tool wear. Figure 23 and Figure 24 present magnified views of the front cutting face of T₂ and the back cutting face of T₃, respectively. T₂ exhibited localized crater wear on its front cutting face, while T₃ experienced microstructure flattening due to high temperatures and elevated milling forces during machining. Among all tools, T₄ displayed minimal wear in Figure 25, with no noticeable degradation in front cutting face microstructures. However, partial microstructure flattening was observed on T₄’s back cutting face. Under identical machining conditions, T₄ demonstrated superior wear resistance. During operation, T₄’s front cutting face microstructures effectively retained and channeled chips, reducing flow resistance and secondary friction between chips and tool surfaces. Additionally, the back cutting face microstructures enhanced heat dissipation, further improving tool longevity.

6.3. Analysis of Workpiece Surface Roughness Results

This study employed a high-precision three-dimensional surface topography measurement system (model: Super View W1) to conduct comprehensive three-dimensional roughness characterization of workpiece surfaces. The system quantitatively evaluated the three-dimensional surface roughness parameter Sa in accordance. Equipped with white light interferometry technology, the device performed non-contact measurements of surface roughness under controlled conditions, as shown in Figure 26.

The surface roughness of workpieces machined by the four tool configurations (T₁–T₄) is quantified in Figure 27, Figure 28, Figure 29 and Figure 30, with T₂, T₃, and T₄ demonstrating proportional reductions in Ra compared to T₁, and T₄ achieving the optimal surface finish. This improvement is attributed to the synergistic effects of front cutting face and back cutting face microstructures: (1) front cutting face microstructures trapped abrasive particles and metal chips, reducing cutting friction and minimizing secondary wear on the workpiece surface caused by chip retraction; (2) back cutting face microstructures enhanced heat dissipation through increased surface area and fluid dynamic pressure effects (via microstructure–airflow interactions), accelerating thermal energy removal. Reduced frictional heat and forces further lowered milling forces and temperatures, indirectly refining surface roughness by mitigating subsurface deformation and material adhesion during chip formation.

7. Conclusions

This study systematically investigated the effects of dual microstructures on ball-end milling tools through orthogonal experiments (four factors, three levels) and finite element simulations. The key conclusions are as follows:

• Optimized Parameters: The optimal microstructure configuration (T₄: D = 50 μm, H = 60 μm, L₁ = 100 μm, L₂ = 100 μm) significantly improved tool performance. Compared to T₁, T₄ reduced milling forces by 19.5% (T₄ vs. T₁), 14.3% (T₄ vs. T₃), and 5% (T₄ vs. T₂), while cutting temperatures decreased by 19.1%, 9.9%, and 6.7%, respectively.
• Wear Resistance: T₄ exhibited the lowest wear volume, attributed to its dual microstructures’ ability to trap chips, reduce friction, and enhance heat dissipation. The front cutting face microstructures minimized secondary wear via chip containment, while the back cutting face microstructures increased heat dissipation area and fluid dynamic pressure effects for accelerated thermal energy removal.
• Surface Finish: T₄ achieved up to a 52.9% improvement in surface roughness (Ra) compared to T₁, demonstrating its capacity to mitigate subsurface deformation and material adhesion through reduced frictional heat and forces.
• Mechanistic Insights: The synergistic effects of dual microstructures—front cutting face chip management and back cutting face thermal regulation—enabled T₄ to outperform single-structure tools, validating their potential for enhancing machining efficiency and tool longevity.