Sage Journals: Discover world-class research

Abstract

Tool edge preparation can improve the tool life, as well as cutting performance and machined surface quality, meeting the requirements of high-speed and high-efficiency cutting. In general, prepared tool edges could be divided into symmetric or asymmetric edges. In the present study, the cemented carbide tools were initially edge prepared through drag finishing. The simulation model of the carbide cemented tool milling steel was established through Deform software. Effects of edge form factor, spindle speed, feed per tooth, axial, and radial cutting depth on the cutting force, the tool wear, the cutting temperature, and the surface quality were investigated through the orthogonal cutting simulation. The simulated cutting force results were compared to the results obtained from the orthogonal milling experiment through the dynamometer Kistler, which verified the simulation model correctness. The obtained results provided a basis for edge preparation effect along with high-speed and high effective cutting machining comprehension.

Keywords

Edge preparation asymmetric edge form factor cutting performance wear

Introduction

With the rapid development and evolution of cutting technology, the performance of cutting tools has become an extraordinary research topic. Moreover, investigations on the improvements of macro structures and micro contours of cutting edges have increased. Studies have demonstrated that the edge preparation could modify the micro surface defects and strengthen the cutting edge. Therefore, the tool life is prolonged to a high extent. The surface smoothness of the cutting edge has been improved, subsequently increasing the friction between the cutting edge and the workpiece.¹

Through literature review it has been indicated that low amount of research has been performed on the tool edge preparation. On the other hand, in the majority of these studies, the simulation analysis and the cutting experiments were mainly utilized, to investigate the impact of edge radius and cutting parameters on the cutting force, cutting temperature, tool service life, and surface quality. In fact, certain researchers investigated the effect of tool edge preparation on cutting performance. It was worth being noted that cutting edges were mainly categorized into symmetric or asymmetric edges, while asymmetric edges have been usually characterized by the form factor K for characterization.² Denkena utilized the abrasive nylon brush method, to prepare the edge of a PVD-coated carbide tool. The symmetrical edge with K = 1 and the asymmetrical cutting edge with K = 2 were studied through contact conditions change of the abrasive brush and the workpiece. It was found that the asymmetrical cutting edge with the factor K = 2 sustained the least wear, while the symmetrical cutting edge with the factor K = 1 resulted in the higher cutting force.^3,4 Bassett applied the abrasive brush method to prepare the tool edge. Following, the influence of the edge preparation parameters such as the feed rate, time, and the tangential velocity on four characterization parameters, including cutting force, temperature, tool life, and roughness were investigated. The bilateral abrasive brush method was applied to obtain the symmetric cutting edge with a large radius.⁵ Varela studied the influence of prepared edge morphology on residual stress. Experiments were carried out and it was demonstrated that the edge preparation could improve the surface quality of the workpiece to a certain extent.⁶ Ventura adopted the grinding method for the edge preparation of the Cubic Boron Nitride (CBN) tool. Moreover, experiments were conducted to verify the effect of different K-factor values on the cutting edge wear and the optimal value of the K-factor to reduce the wear of the cutting edge was found.⁷ Fulemova and Janda⁸ Fulemova and Řehoř⁹ prepared the edge tool through drag finishing, demonstrating that through this method, the highest tool life and best surface quality were achieved, when the corresponding asymmetric factor was K > 1. Morrison adopted the nylon brush method to prepare the tool edge and studied the impact of the form factor on the thermal stress distribution of the coated cutting tool. The carried out experiments proved that the value Sα could affect the flank face wear.¹⁰ Konstantin Sauer evaluated the influence of edge radius and form factor K on the thrust force during machining of carbon fiber reinforced plastics (CFRP) through an orthogonal cut. The application of small depths in combination with different cutting edges enabled the effect of different edge regions on the process forces.¹¹ Mohamed compared two damage modeling approaches in metal cutting finite element simulations through the Johnson-Cook shear failure model and the progressive damage model. He also investigated the influence of different process parameters on the cutting forces and chip thickness through the simulation and orthogonal cutting tests.¹² Min Wan established a cutting force model that could separately consider the shearing and plowing effects. He also validated that the proposed material separation model and the cutting force model are reasonable for micro milling process.¹³ Wojciechowski et al.¹⁴ showed that the instantaneous and average micromilling forces determined using the proposed model have considerably better conformity with the experimental forces than those predicted using the commonly rigid micro end milling model. Maruda presented the analysis of metrologic and tribologic aspects of machined surfaces obtained after turning with the application of various cooling/lubricating methods. The smallest values of the surface roughness are obtained after turning under minimum quantity cooling lubrication with modifications conditions.¹⁵ Kalisz presented an evaluation of sequential ball end milling-burnishing process of a curvilinear surfaces by considering some technological and tribological aspects. He also found the application of burnishing process is more advantageous than polishing in terms of surface finish, as well as the tribological characteristics.¹⁶

Based on the aforementioned studies, in the present study, it was indented to edge prepare a tool through drag finishing. The prepared tool model with an asymmetric edge was constructed with Solidworks software. The finite element DEFORM software was utilized to simulate and analyze the cemented carbide tool milling of the 45 steel. It was intended to investigate the impact of diverse parameters, including form factor K, spindle speed, feed per tooth, axial, and radial cutting depths on the cutting force, the tool wear, cutting temperature, and quality of the machined surface through the orthogonal cutting simulation. In order to evaluate the accuracy of simulations, the axial cutting force obtained through the orthogonal simulation were compared to the experimental results. It was expected that the present study could constitute an important basis of theoretical value and practical significance in the identification of basic rules governing cutting and development of high speed as well as high efficiency machining technologies.

Simulation modeling of asymmetrical edge cutting process

The contour of the prepared tool edge could be symmetric or asymmetric. In fact, most prepared edge contours do not have regular arcs. The asymmetrical edge of the prepared tool was commonly presented by the form factor K = Sγ/Sα[2], which was shown in Figure 1. For this representing method, segments of the cutting edge, including Sγ and Sα were introduced to measure the distance between the separation point of the cutting edge rounding and the sharp cutting edge at the flank edge and rake edge, respectively.

Figure 1.

Asymmetrical tool edge characterization method.

Figure 2 presented that the tool milling simulation model was established through the cutting simulation DEFORM software. It was significant that the DEFORM software was a simulation and analysis software, based on the finite element method, often utilized to analyze the cutting, forming, and heat treatment processes of materials. Characteristics of the cutting tool and parameters of the cutting process could be optimized through the simulation, which effectively prevented the requirement for costly experiments.

Figure 2.

Simulation model of milling process.

The three-dimension milling and down milling were selected. The cutting method was the corner milling. The three-dimension models of the cutting tools were of different asymmetric edges. The corresponding workpiece was established through the Solidworks software. The cutting tool was the cemented carbide end milling tool of ZX40, which was shown in Figure 3. The tool geometry and material properties were shown in Table 1.

Figure 3.

Milling tool model.

Table 1.

Tool geometry and material properties.

Rake angle	Relief angle	Edge length	Diameter	Total length	Poisson’s ratio	Shear modulus (Pa)	Density (kg/m³)	Rolling friction coefficient	Hardness
14°	15°	25 mm	10 mm	75 mm	0.3	2.346 × 10¹¹	1456	0.01	90.5

The workpiece was the annealed 45 steel, with 2.5 mm in length, 1 mm in width, 1 mm with height. The reasonable choice of the constitutive equations was the key to realize the accurate cutting simulation. The mechanical properties and chemical composition of the workpiece was shown in Table 2. The Johnson-cook model included the effects of different factors, such as strain, strain rate, and thermal softening on the hardening stress of materials, especially suitable for simulation of metal materials at high strain rates. The Johnson-cook model parameters was shown in Table 3. Therefore, the constitutive equation of the material was also called the flow stress equation, through the Johnson-cook constitutive equation utilization. The number of tool meshes was 350,000, the size ratio was 8 and the local refinement ratio was 0.001. Since the workpiece size was small, the mesh was divided with absolute method, in order to better balance the computational expenses and accuracy. The minimum size of the mesh was 0.025 mm, which was1/4 of the feed per tooth and the size ratio was 8. The shear friction coefficient between tool and workpiece was 0.6, the heat conduction coefficient was 45 N/sec/mm/°C and the wear model was Usui model.

Table 2.

Mechanical properties and chemical composition of 45 steel.

AISI	Mechanical properties				Chemical composition (%)
	Tensile strength $σ_{b}$ /MPa	$σ_{s}$ /MPa	$δ_{s}$ (%)	$Ψ$ (%)	C	Si	Mn
45	≥600	≥355	≥16	≥40	0.42–0.50	0.17–0.37	0.50–0.80
	Akv (J)	HB	Density (g/cm³)	Young modulus (GPa)	Cr	Ni	Cu
	≥39	197	7.85	210	≤0.25	≤0.35	≤0.25
					P	S
					≤0.035	≤0.035

Table 3.

45 Steel Johnson-Cook constitutive parameters.

A	B	C	n	m	$ε_{0}$	$T_{r}$	$T_{m}$
553.1 MPa	600.8 MPa	0.0134	0.23	1	1 $S^{- 1}$	20°C	1500°C

Establishment of milling experiment

During milling, the cutting parameters and the form factor K affected the cutting performance. In order to accurately investigate the influence of parameters on the cutting performance, an orthogonal experiment was designed. Following, the orthogonal experiments of the cemented carbide tool milling were carried out. The edge preparation and detection equipment, cutting equipment, and detection equipment were as follows.

The tool and the workpiece were the same as in the simulation.The drag finishing method was applied to edge prepare the cemented carbide tool, while tools with different asymmetric cutting edges were obtained accordingly. It was noteworthy that this method was utilized for both edge preparation and workpiece polishing. The group of tools was installed on the spindle. The tools moved along the two-stage planetary trajectory through the abrasive particles. Figure 4 presented the drag finishing process principle. Through this method, a single tool could execute both rotational and the revolution movements. The group of tools could also execute rotational and revolution movements. The dispersed solid abrasive, consisting of walnut powder, brown corundum particles and silicon carbide particles mixed at a certain ratio, was packed in the container. During edge preparation, the tool edge was prepared through continuous impacts from dispersed solid abrasive particles, which removed micro defects, thereby resulting in efficient and uniform edge preparation. Therefore, it was concluded that the edge preparation was quite complicated and required further investigation. The detection of the asymmetric edge morphology parameters subsequently to the edge preparation was performed through an optical 3D tool measuring instrument called the infinite focus SL, as presented in Figure 5.

Figure 4.

Drag finishing principle.

Figure 5.

Optical 3D tool measuring instrument infinite focus SL.

The tool cutting performance experiments were carried out with a horizontal 3-axis milling vertical machining center (VM600). The cutting force was measured through the Kistler wireless dynamic cutting dynamometer. The Kistler 9257B three-directional piezoelectric dynamometer. The force measuring system was mainly composed of four parts: computer end M&T HORIZON cutting force acquisition and analysis software, HRU-1212M data collector, Kistler5070A charge amplifier and Kistler 9257B three-way piezoelectric force measuring instrument. The cutting force was analyzed and processed with the specific analysis software of the dynamic cutting dynamometer. The sensitivity is −7.5pC/N for Fx, −7.5pC/N for Fy, −3.7pC/N for Fz. The natural frequency is 2.3 kHz for fnx, 3.5 kHz for fny.

The main factors affecting the cutting performance were the rotation speed, feed per tooth, axial depth, radial depth, and form factor K. Therefore, five factors and four levels were adopted in the orthogonal milling experiments, as presented in Table 4. The orthogonal experiment scheme was shown in Table 5. Figure 6 presented the milling experiment.

Table 4.

Orthogonal experiment level parameters.

No.	Speed (r/min)	Feed (mm/tooth)	Axial depth (mm)	Radial depth (mm)	Form factor K
1	2000	0.10	0.5	0.5	0.865
2	2300	0.12	0.7	0.7	0.899
3	2700	0.14	0.9	0.9	0.995
4	3200	0.16	1.1	1.1	1.121

Table 5.

Orthogonal experiment scheme.

No.	Speed (r/min)	Feed (mm/tooth)	Axial depth (mm)	Radial depth (mm)	Form factor K
1	2000	0.10	0.5	0.5	0.865
2	2000	0.12	0.7	0.7	0.899
3	2000	0.14	0.9	0.9	0.995
4	2000	0.16	1.1	1.1	1.121
5	2300	0.10	0.7	0.9	1.121
6	2300	0.12	0.5	1.1	0.995
7	2300	0.14	1.1	0.5	0.899
8	2300	0.16	0.9	0.7	0.865
9	2700	0.10	0.9	1.1	0.899
10	2700	0.12	1.1	0.9	0.865
11	2700	0.14	0.5	0.7	1.121
12	2700	0.16	0.7	0.5	0.995
13	3200	0.10	1.1	0.7	0.995
14	3200	0.12	0.9	0.5	1.121
15	3200	0.14	0.7	1.1	0.865
16	3200	0.16	0.5	0.9	0.899

Figure 6.

Milling experiment.

Results and discussions

Analysis of cutting force

Analysis of simulation and experimental results

The influence of asymmetrical edge preparation on the carbide cemented tool milling 45 steel was investigated through both simulations in Deform software and corresponding orthogonal experiments. Figures 7 to 9 presents that the cutting force obtained through the finite element simulation was compared to the orthogonal experimental results.

Figure 7.

Comparison of simulated and measured cutting force Fx.

Figure 8.

Comparison of simulated and measured cutting force Fy.

Figure 9.

Comparison of simulated and measured cutting force Fz.

It was observed that the numerical error of cutting force, as measured by simulation analysis and milling experiment, was basically within 20%, while the variation trend of cutting force between simulation analysis and milling experiment was basically the same. It could be observed that the simulation results could accurately simulate the cutting force during the actual cutting process, but the simulation of cutting force Fx had the highest degree of coincidence, followed by cutting force Fy, along with the lowest degree of coincidence cutting force Fz.

Extreme difference analysis of cutting force results

Results of the experiment were assessed through the extreme difference analysis. Tables 6 to 8 present the impact of different cutting parameters and form factor K on cutting forces Fx, Fy, and Fz. Based on the extreme difference analysis, the influence of each cutting parameter and the form factor K on the cutting force was investigated. According to the average values, the impact of different factors on the cutting force was observed and suitable operating conditions were obtained for cutting force minimization. The range analysis demonstrated that the influence of the form factor and cutting parameters on the cutting force differed.

Table 6.

Extreme difference analysis of cutting force F_X.

Extreme difference	Speed (r/min)	Feed (mm/tooth)	Axial depth (mm)	Radial depth (mm)	Form factor K
Mean 1	214.485	201.416	187.378	251.007	228.920
Mean 2	245.232	241.585	227.928	228.440	235.191
Mean 3	279.953	253.044	277.420	233.284	233.299
Mean 4	228.424	272.049	275.368	255.364	270.683
Range	65.468	70.633	90.042	26.924	41.763

Table 7.

Extreme difference analysis of cutting force Fy.

Extreme difference	Speed (r/min)	Feed (mm/tooth)	Axial depth (mm)	Radial depth (mm)	Form factor K
Mean 1	180.344	189.575	125.877	157.819	212.822
Mean 2	205.330	216.080	186.170	214.287	153.633
Mean 3	191.952	204.407	221.153	209.435	215.527
Mean 4	202.141	169.705	246.567	198.227	197.785
Range	24.986	46.375	120.690	56.468	61.894

Table 8.

Extreme difference analysis of cutting force Fz.

Extreme difference	Speed (r/min)	Feed (mm/tooth)	Axial depth (mm)	Radial depth (mm)	Form factor K
Mean 1	163.329	140.671	97.511	141.409	144.920
Mean 2	119.490	152.855	129.478	131.577	165.655
Mean 3	173.157	172.162	182.982	174.148	162.024
Mean 4	142.815	133.102	188.818	151.657	126.191
Range	53.667	39.060	91.307	42.571	39.464

Table 6 illustrates that the main factors affecting the cutting force F_X, in descending order of importance, were axial cutting depth, tooth feed, spindle speed, form factor K, and radial cutting depth. The lowest cutting force Fx was obtained through the following parameters: speed = 2000 r/min, feed rate = 0.16 mm/tooth, axial cutting depth = 0.7 mm, radial cutting depth = 0.7 mm, and form factor K = 0.899.

Table 7 presents that the main factors affecting the cutting force Fy, in descending order of importance, included axial depth, form factor, radial depth, feed per tooth, and spindle speed. The lowest cutting force Fy was obtained through the following parameters: speed = 2000 r/min, feed rate = 0.16 mm/tooth, axial cutting depth = 0.5 mm, radial cutting depth = 0.5 mm, and form factor K = 0.899.

Table 8 presents that the main factors affecting the cutting force Fz, in descending order of importance, were axial depth, spindle speed, radial depth, form factor and feed per tooth. The lowest cutting force Fz was obtained through the following parameters: speed = 2300 r/min, feed rate = 0.16 mm/tooth, axial cutting depth = 0.5 mm, radial cutting depth = 0.7 mm, and form factor K = 1.121.

Predictive model for cutting force

Based on experimental results, the cutting force was the dependent variable, while feed, rotational speed, axial depth, radial depth, and form factor K were independent variables. According to the experiment based on the orthogonal design, a predictive model of the cutting force could be established in each of the three directions as:

F_{x} = 123.31 n^{0.288} f_{z}^{0.688} {a_{p}}^{0.561} {a_{e}}^{0.075} K^{0.739}

(1)

F_{y} = 88.51 n^{0.088} f_{z}^{- 0.19} {a_{p}}^{0.909} {a_{e}}^{0.309} K^{0.594}

(2)

F_{z} = 99.77 n^{0.108} f^{0.120} {a_{p}}^{0.880} {a_{e}}^{0.136} K^{0.600}

(3)

where a_f, a_p, and a_e denote the feed per tooth (mm/tooth), axial depth (mm/tooth), and radial depth (mm), respectively. n and K denote the spindle speed (r/min) and form factor, respectively.

The variance method could be adopted to analyze the cutting force prediction model. The F distribution table presented that when α was 0.05, F = (5, 10) = 3.3. Since 5.820 exceeded 2.3, 3.903 exceeded 2.3 and 3.769 exceeded 2.3, the predictive model of cutting force was apparent for cemented carbide tool milling of the 45 steel. The predictive model of the cutting force could better reflect the corresponding functional correlation with cutting parameters and form factor.

Analysis of tool wear analysis

Single factor analysis

The effects of different shape factors k on the milling tool wear are presented in Figure 10. From the cloud diagram, it could be observed that the main tool wear area was concentrated on the edge, while the area with the highest wear depth was basically concentrated at the rake face. This occurred because the tool edge removed the workpiece material during milling, while the rake face and the workpiece had violent friction behavior. Consequently, the edge was easy to be worn and the highest wear depth was at the rake face. When the maximum wear depth of the rake surface reached a certain degree, it might be presented in the form of crescent depression. When milling during simulation was 2.5 mm, as 3000 steps, the maximum wear amount decreased first and subsequently increased as the shape factor K increased. When the shape factor was K = 0.99, the maximum wear depth was highest of 0.757 μm. When the shape factor was K = 0.824, the maximum wear depth was lowest of 0.504 μm.

Figure 10.

Tool wear depth at 3000 steps: (a) k = 0.81, (b) k = 0.824, (c) k = 0.88, and (d) k = 0.99.

The tool wear experiment was carried out with the milling machine VCL850. The wear of the four cutting edges of the cemented carbide milling cutter was detected and the average wear was calculated, while the average wear depth of the cutting edge was obtained through equation (4). The average wear depth of the cutting edge was obtained, as presented in Figure 11.

VB = \frac{H}{\tan α} - H \tan γ

(4)

where VB the wear width of the rear face, H-the tool wear depth, while $α$ , $γ$ are the rear and front angles of the tool, respectively.

Figure 11.

Tool wear depth at 3000 steps: (a) k = 0.81, (b) k = 0.824, (c) k = 0.88, and (d) k = 0.99.

According to Figure 12, the wear depth of milling tool decreased first and subsequently increased as the shape factor K increased, while the corresponding law was the same as with the simulation results, which proved the reliability of the simulation results.

Figure 12.

Influence of different form factors on wear depth.

Analysis of extreme difference results for tool wear

The tool wear was obtained through the simulation. Following, the results were analyzed through the extreme difference method, which is presented in Table 9. The main factors affecting the tool wear, in descending order of importance, were the form factor, the axial cutting depth, the radial cutting depth, the spindle speed, and the feed per tooth. The lowest tool wear was obtained using the following parameters: spindle rotation speed = 2300 r/min, feed rate = 0.14 mm/tooth, axial cutting depth = 1.1 mm, radial cutting depth = 0.5 mm, and form factor K = 0.865.

Table 9.

Tool wear extreme difference analysis.

Extreme difference	Speed (r/min)	Feed (mm/z)	Axial depth (mm)	Radial depth (mm)	Form factor K
Mean 1	0.995	1.087	1.885	0.557	0.483
Mean 2	0.565	0.841	1.000	1.191	2.262
Mean 3	1.050	0.737	0.757	1.649	0.578
Mean 4	1.638	1.545	0.567	0.813	0.887
Range	1.073	0.808	1.318	1.092	1.779

Analysis of the influence of different factors on tool wear depth

Figure 13 present the interaction of the form factor K, the cutting parameters, as well as the corresponding effect on tool wear depth. The tool wear depth of the 45 steel decreased first and subsequently increased, as spindle speed and feed rate increased. The tool wear depth decreased as the axial cutting depth increased. As the radial cutting depth increased, the tool wear depth first increased and subsequently decreased. As the form factor increased, the tool wear depth first increased and consequently decreased, while finally increasing. The maximum wear depth was four times higher than the minimum wear depth, when the tool wear depth k was closer to 1.

Figure 13.

Influence of various factors on wear depth.

Analysis of cutting temperature simulation analysis

Analysis of extreme difference results for experimentation

The cutting temperature was obtained through the simulation. Following, the results were analyzed with the extreme difference method, as presented in Table 10. The main factors affecting the cutting temperature, in descending order of importance, were the form factor, the radial cutting depth, the axial cutting depth, the spindle speed, and the feed per tooth. The lowest cutting temperature was obtained using the following parameters: spindle rotation speed = 2000 r/min, feed rate = 0.14 mm/z, axial cutting depth = 0.5 mm, radial cutting depth = 0.5 mm, and form factor K = 0.889.

Table 10.

Extreme difference analysis of experimentation.

Extreme difference	Speed (r/min)	Feed (mm/z)	Axial depth (mm)	Radial depth (mm)	Form factor K
Mean 1	372.25	402.00	372.25	363.50	380.50
Mean 2	415.75	414.00	394.25	393.75	342.25
Mean 3	416.00	383.50	406.50	404.50	410.75
Mean 4	389.50	394.00	420.50	431.75	460.00
Range	43.75	30.50	48.25	68.25	117.75

Analysis of influence of different form factors on cutting temperature

Figures 14 to 17 present the interaction of the form factor K, the cutting parameters and the corresponding effect on the cutting temperature. The cutting temperature of the 45 steel decreased first, consequently increasing as the form factor K increased. The cutting temperature increased as the radial cutting depth and axial cutting depth increased. As the spindle speed and feed rate increased, the cutting temperature increased first and subsequently decreased, while it finally increased.

Figure 14.

Influence of factor K and speed on temperature.

Figure 15.

Influence of factor K and feed on temperature.

Figure 16.

Influence of K and axial depth on temperature.

Figure 17.

Influence of K and radial depth on temperature.

Analysis of the residual stress experimental results

Analysis of extreme difference results for residual stress

Table 11 presents that the orthogonal simulation results of the residual stress were analyzed through the extreme difference method. The main factors affecting the residual stress, in descending order of importance, were the spindle speed, the axial cutting depth, the feed per tooth, the radial cutting depth, and the form factor. The lowest residual stress was obtained through the following parameters: spindle speed = 2000 r/min, feed rate = 0.1 mm/tooth, axial cutting depth = 0.5 mm, radial cutting depth = 0.9 mm, and form factor K = 0.995.

Table 11.

Extreme difference analysis of residual stress.

Extreme difference	Speed (r/min)	Feed (mm/z)	Axial depth (mm)	Radial depth (mm)	Form factor K
Mean 1	213.50	332.00	280.75	353.25	350.50
Mean 2	326.25	356.25	301.25	367.25	353.75
Mean 3	423.50	348.50	392.75	330.50	341.75
Mean 4	453.75	380.25	442.25	366.00	371.00
Range	240.25	48.25	161.15	36.75	29.25

Analysis of influence of different factors on residual stress

Figure 18 presents the influence of different factors on residual stress. The residual stress increased as the rotational speed and axial depth increased. However, the residual stress initially increased, consequently decreased and finally increased as the feed and radial depth increased. It should be indicated that a nonlinear correlation existed between form factor and residual stress, which initially increased gently, consequently decreased and finally increased. The form factor had a significant effect on residual stress. The residual stress was highest for tools with the form factor of K = 1.121, while the tools with the form factor of K = 0.995 obtained the lowest residual stress. The minimum residual stress decreased by 7.9% compared to the maximum residual stress.

Figure 18.

Influence of various factors on residual stress.

Conclusions

In the present study, the impact of the asymmetric edge preparation on the cemented carbide tool milling 45 steel was investigated. A simulation was performed with DEFORM software and the corresponding cutting experiment was based on the orthogonal design. The laws governing the influence of the form factor K of the asymmetrical cutting edge, spindle speed, feed per tooth, axial cutting depth, and radial cutting depth on the cutting force, cutting temperature, wear, and the residual stress were investigated, through the single factor and the orthogonal simulation. The correctness of the finite element simulation model was verified through comparison of the simulation results with the milling cutting experiments. These results provided a basis for further optimization of the edge preparation and laid the foundation for achievement of high speed high efficiency cutting technology. The main conclusions of the present study were:

The numerical error of cutting force measured by simulation analysis and milling experiment is basically within 20%, and the variation trend of cutting force between simulation analysis and milling experiment is basically the same. It can be seen that the simulation results can accurately simulate the cutting force in the actual cutting process, but the simulation of cutting force Fx has the highest degree of coincidence, followed by cutting force Fy, the lowest degree of coincidence cutting force Fz.

The tool wear depth of the carbide cemented milling tool decreased first and consequently increased as spindle speed and feed rate increased. The tool wear depth decreased as the axial cutting depth increased. As the radial cutting depth increased, the tool wear depth first increased and consequently decreased. As the form factor increased, the tool wear depth first increased and consequently decreased, while it finally increased.

The main factors affecting the cutting temperature, in descending order of importance, were the form factor, the radial cutting depth, the axial cutting depth, the spindle speed and the feed per tooth. The lowest cutting temperature was obtained through the following parameters: spindle rotation speed of 2000 r/min, feed rate of 0.14 mm/z, axial cutting depth of 0.5 mm, radial cutting depth of 0.5 mm, and form factor K of 0.889.

The main factors affecting the residual stress, in descending order of importance, were the spindle speed, the axial cutting depth, the feed per tooth, the radial cutting depth, and the form factor. The lowest residual stress was obtained through the following parameters: spindle speed = 2000 r/min, feed rate = 0.1 mm/tooth, axial cutting depth = 0.5 mm, radial cutting depth = 0.9 mm, and form factor K = 0.995.

Footnotes

Handling Editor: James Baldwin

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors would like to acknowledge the financial support provided by the National Natural Science Foundation Project (No. 51665007) and the Research Fund of High level innovative Talents Project in Guizhou Province(Grant No.[2018]190).

ORCID iD

Xuefeng Zhao

References

Karpuschewski

Schmidt

Beňo

, et al. Measuring procedures of cutting edge preparation when hard turning with coated ceramics tool inserts. Measurement 2014; 55: 627–640.

Denkena

Biermann

Cutting edge geometries. CIRP Ann Manuf Technol 2014; 63: 631–653.

Denkena

Köhler

Breidenstein

Influence of the cutting edge preparation method on characteristics and performance of PVD coated carbide inserts in hard turning. Surf Coat Technol 2014; 254: 447–454.

Denkena

Köhler

Bergmann

Development of cutting edge geometries for hard milling operations. CIRP J Manuf Sci Technol 2015; 8: 43–52.

Bassett

Köhler

On the honed cutting edge and its side effects during orthogonal turning operations of AISI1045 with coated WC-Co inserts. CIRP J Manuf Sci Technol 2012; 5: 108–126.

Varela

Rakurty

Balaji

AK.

Surface integrity in hard machining of 300M steel: effect of cutting-edge geometry on machining induced residual stresses. Procedia CIRP 2014; 13: 288–293.

Ventura

CEH

Köhler

Denkena

. Influence of cutting edge geometry on tool wear performance in interrupted hard turning. J Manuf Process 2015; 19: 129–134.

Fulemova

Janda

Influence of the cutting edge radius and the cutting edge preparation on tool life and cutting forces at inserts with wiper geometry. Procedia Eng 2014; 69: 565–573.

Fulemová

Řehoř

Influence of form factor of the cutting edge on tool life during finishing milling. Procedia Eng 2015; 100: 682–688.

10.

Morrison

Cleary

Sinnott

MD.

Using DEM to compare the energy efficiency of pilot scale ball and tower mills. Miner Eng 2009; 22: 665–672.

11.

Sauer

Witt

Putz

Influence of cutting edge radius on process forces in orthogonal machining of carbon fibre reinforced plastics (CFRP). Procedia CIRP 2019; 85: 218–223.

12.

Nasr

MNA

Ammar

MMA

. An evaluation of different damage models when simulating the cutting process using FEM. Procedia CIRP 2017; 58: 134–139.

13.

Wan

Wen

, et al. On material separation and cutting force prediction in micro milling through involving the effect of dead metal zone. Int J Mach Tools Manuf 2019; 146: 103452.

14.

Wojciechowski

Matuszak

Powałka

, et al. Prediction of cutting forces during micro end milling considering chip thickness accumulation. Int J Mach Tools Manuf 2019; 147: 103466.

15.

Maruda

Krolczyk

Wojciechowski

, et al. Evaluation of turning with different cooling-lubricating techniques in terms of surface integrity and tribologic properties. Tribol Int 2020; 148: 106334.

16.

Kalisz

Żak

Wojciechowski

, et al. Technological and tribological aspects of milling-burnishing process of complex surfaces. Tribol Int 2021; 155: 106770.

Influence of tool edge form factor and cutting parameters on milling performance

Abstract

Keywords

Introduction

Simulation modeling of asymmetrical edge cutting process

Establishment of milling experiment

Results and discussions

Analysis of cutting force

Analysis of simulation and experimental results

Extreme difference analysis of cutting force results

Predictive model for cutting force

Analysis of tool wear analysis

Single factor analysis

Analysis of extreme difference results for tool wear

Analysis of the influence of different factors on tool wear depth

Analysis of cutting temperature simulation analysis

Analysis of extreme difference results for experimentation

Analysis of influence of different form factors on cutting temperature

Analysis of the residual stress experimental results

Analysis of extreme difference results for residual stress

Analysis of influence of different factors on residual stress

Conclusions

Footnotes

Declaration of conflicting interests

Funding

ORCID iD

References