Comprehensive Evaluation Method for High-Performance Milling of Inconel 718 Alloy

Abstract

The aim of this paper was to develop and verify a method for evaluating the high-performance milling of Inconel 718 alloy under accelerated tool wear conditions. The method considered parameters such as cutting-force components, total machine power consumption, cutting-edge wear, and material removal rate. The study compared high-feed milling and plunge milling, using sets of cutting parameters that are appropriate for both techniques. The results indicate that high-feed milling was more efficient, achieving higher material removal rates and lower tool wear. On the other hand, plunge milling was characterized by a lower axial force component (F_z), which can positively affect machining accuracy. The paper highlights that the proposed evaluation method can also be applied to other hard-to-machine materials, and plunge milling offers a competitive alternative for roughing operations in the milling of Inconel 718 alloy.

1. Introduction

Inconel alloys, being nickel-based superalloys, are characterized by exceptional mechanical strength and corrosion resistance at high temperatures, making them invaluable in the aerospace, energy, and chemical industries. However, these very properties also make the machining of Inconel alloys a significant technological challenge. The issues associated with machining these materials include rapid tool wear, intense heat generation in the cutting zone, and the formation of residual stresses, which can reduce the durability of the machined components [1]. In response to these challenges, researchers are developing and implementing advanced methods aimed at improving the machinability of Inconel alloys. Examples include advanced cooling techniques, such as cryogenic machining, which significantly extends tool life and enhances surface quality [2], and minimum quantity lubrication (MQL) enhanced with nanoparticles, which reduces energy consumption and improves process efficiency [3,4]. One such innovative method is also ultrasonic-assisted milling, which improves process stability and reduces cutting forces, which is particularly important when machining alloys such as Inconel 718 [5].

The milling of Inconel alloys using carbide and ceramic inserts differs significantly in terms of performance, tool durability, and the quality of the machined surface. Ceramic tools, such as those made from SiAlON, are particularly effective in roughing operations, where high cutting speeds are essential for the rapid removal of large volumes of material. Research revealed that ceramic tools achieve a higher material removal rate (MRR) compared to carbide tools, making them more suitable for roughing operations [6]. Meanwhile, it has been emphasized that milling with ceramic inserts allows for a significant reduction in machining time, which is especially advantageous in large-scale production [7]. However, for finishing operations, where surface quality and dimensional accuracy are paramount, carbide inserts prove to be more effective.

It was observed that finishing milling with carbide tools generates lower residual stresses and provides better surface quality compared to ceramic tools [8]. Carbide tools, particularly those with PVD or CVD coatings, exhibit superior wear resistance at lower cutting speeds, which results in reduced surface damage and higher machining precision [9,10]. Consequently, while ceramic tools are favored for roughing operations due to their efficiency, carbide tools dominate in finishing operations, where quality and precision are critical.

The milling of Inconel alloys using ceramic inserts requires specific parameters, including high cutting speeds, which are crucial for efficient material removal and extended tool life. Ceramic tools, particularly those made from materials such as SiAlON, are used at cutting speeds ranging from 250 m/min to even 900 m/min, depending on the specifics of the process and the type of tool. For example, it has been indicated that an optimal cutting speed for ceramic tools is around 250 m/min, allowing for a compromise between tool wear and tool longevity [11]. Conversely, it was described that, during high-speed milling, up to 900 m/min, SiAlON tools exhibit a significantly enhanced performance, albeit at the cost of increased residual stresses in the machined material [12]. Research demonstrated that, at cutting speeds ranging from 900 to 1100 m/min, it is possible to achieve a significant increase in the material removal rate (MRR), making these tools exceptionally effective for roughing operations [7]. Such high cutting speeds can lead to localized melting of the microstructure of Inconel 718, which reduces cutting forces, as previously observed [13].

In addition to the previously discussed methods, research on the machining of Inconel alloys highlights the need to optimize the cutting parameters and the tool geometry to improve surface quality and reduce residual stresses. It has been demonstrated that the use of “wiper” inserts during the face milling of Inconel 625 enhances the surface integrity and extends tool life [14]. Further investigations into residual stresses after milling Inconel 718 confirmed that selecting the appropriate production parameters is crucial for minimizing stresses, which can impact the durability of components [8].

The optimization of the cutting parameters, such as spindle speed, feed rate, and depth of cut, is crucial for achieving a high surface quality, as demonstrated by studies focusing on the machining of Inconel 601 [15]. Additionally, tool geometry parameters, such as rake angle and helix angle, have a significant impact on cutting forces and cutting temperature, as confirmed in prior research [16]. Further research on the machining of Inconel 718 using carbide-coated tools showed that the appropriate selection of a tool coating can significantly reduce tool wear and improve surface roughness [17].

Modern techniques, such as laser-assisted milling (LAM) and induction-assisted milling, represent innovative approaches to enhancing the cutting efficiency of Inconel alloys. It has been demonstrated that laser-assisted milling leads to a reduction in cutting forces and an improvement in surface quality, which is particularly important for precision finishing operations [18]. Similarly, research showed that induction-assisted milling reduces tool wear and improves process stability, which is crucial in the machining of Inconel 718 [19].

The machining of Inconel alloys, particularly with the use of advanced ceramic tools, presents a significant technological challenge due to the high hardness and wear resistance of these materials. Analysis of the application of Si₃N₄ tools in the milling of Inconel 718 showed that such tools can significantly reduce cutting forces, thereby improving the overall process efficiency [20]. Simultaneously, research highlighted the benefits of using nanoparticle suspension lubrication, which not only enhances surface quality but also improves the energy efficiency of the machining process for Inconel 800 [4].

One of the key aspects that requires special attention is the impact of machining parameters on material deformation. An analysis of deformation during the continuous milling of Inconel 718 indicated that the proper optimization of parameters such as cutting speed and feed rate can significantly reduce deformation and improve machining quality [21]. Further research examined the effect of tool geometry and cutting speed on the machinability of Inconel 718, demonstrating that the appropriate selection of these parameters is crucial for process efficiency and tool longevity [22].

In the context of sustainable development, a comparison of the additive and subtractive methods in the production of Inconel 718 components evaluated time costs and CO₂ emissions, highlighting the importance of selecting the appropriate manufacturing technology for addressing global environmental challenges [23]. Meanwhile, a review of the current knowledge on surface integrity after machining nickel-based alloys pointed out the risk of white-layer formation, which can negatively affect the fatigue life of components [24].

Research on the high-speed dry milling of Inconel 718 using SiAlON tools demonstrated that the proper optimization of the process parameters, such as cutting speed, can lead to a reduction in the cutting forces and an extension of tool life [13]. Additionally, findings on the machining of Inconel 718 emphasized the critical role of cutting temperature, which directly affects tool wear and surface quality [25].

A common feature of most of the publications described above is the use of various cutting parameters and conditions aimed at assessing the changes associated with the introduction of innovative machining solutions. However, these results are difficult to compare due to differences in the experimental setups. The literature contains numerous examples of identifying which factors and parameters influence specific elements during the machining of Inconel alloys, and to what extent. Several studies have analyzed the impact of spindle speed, feed rate, and depth of cut on cutting forces [26,27]. Additionally, the influence of spindle speed, feed rate, and depth of cut on both cutting forces and power consumption has been examined [28]. Other research studied the effect of these parameters on tool life [29], while further investigations focused on the impact of spindle speed and feed rate on tool longevity [30]. The influence of the same factors on cutting forces and tool wear has also been explored [31]. These parameters often have ambiguous effects on the outcomes, and considering some factors and effects in isolation from others, they do not provide a complete understanding of Inconel milling. It has been suggested that methods such as GRA, TOPSIS, or fuzzy logic could be utilized to optimize the milling process of Inconel alloys [32]. GRA, in particular, yields promising results, as it does not require determining the weighting factors of individual process-influencing parameters, which, as noted, often vary in the literature [33]. Most studies focus on analyzing a maximum of 2–3 parameters, which is a relatively small number considering the capabilities of GRA-based methods. Therefore, it was decided that the designed method would analyze as many as four parameters. These parameters would be related not only to the cutting process itself but also to the cutting machine. For this reason, the energy consumption of the machine tool was considered, which has not been accounted for in similar studies, yet it is becoming an increasingly important aspect in the context of sustainable machining. We believe this highlights a research gap that we aim to address by presenting the developed method.

2. Materials and Methods

An attempt was made to develop a method for evaluating the high-performance milling of Inconel 718. This method should involve a comprehensive, multi-dimensional analysis of the process and allow for the comparison of various machining types, regardless of their kinematics or tool path. The primary assumptions of the proposed evaluation method are as follows:

• the method pertains only to roughing operations;
• the compared processes must use tools with the same geometry (i.e., the same diameter and number of cutting edges, or in the case of assembled tools with interchangeable inserts, the same tool body or the same number of cutting edges in the tool);
• the cutting speed must be the same in the compared processes;
• the cutting trials must be conducted on the same machine tool;
• The cutting trials must use material from the same heat of steel.

Additionally, the research plan must be designed to take place under accelerated wear conditions. Beyond the economic rationale, this approach allows for a more detailed analysis of a greater number of parameters. Based on studies [34] regarding minimum quantity lubrication (MQL) and other lubrication methods, it has been established that even minimal lubrication affects tool life in the milling process of Inconel alloys. For this reason, to accelerate tool wear, dry machining was chosen, allowing for the achievement of results related to accelerated wear. A key aspect of the method is to observe the accelerated wear mechanism in order to accurately calculate tool life until the V_BBmax wear is reached.

Based on the literature analysis presented in the introduction, it was assumed that a comprehensive evaluation of the high-performance milling of Inconel 718 requires the investigation of the following process parameters:

• components of cutting forces F_x, F_y, and F_z [N];
• total machine power consumption (P_c) [kW];
• cutting-edge wear (V_BB) [mm];
• material removal rate (Q) [cm³/min].

Since the focus is on roughing operations, parameters related to the dimensional and shape accuracy of the machined surface and the residual stresses, as described in the literature, were excluded from the analysis.

To determine the values of the cutting forces, it is necessary to analyze the graphs of the force components to identify the dominant force component, which will reflect the increase in cutting resistance as tool wear progresses. Based on this component, the maximum value occurring during the cutting process should be determined, excluding the forces present during tool entry and exit from the material, as these may be prone to significant measurement errors.

Similarly, the total machine power consumption should be measured. Special attention should be paid to the spikes in the power values during the activation of various drives (particularly the main spindle drive), as these could mislead the person conducting the analysis. The values considered for the comparative evaluation method should be obtained during the cutting process, excluding the moments of tool entry and exit from the material.

Tool wear measurement is typically performed outside of the machine involved in the experiment. This means that the number of machining passes, as a function of the volume of machined material, must be designed to ensure that the number of wear measurements allows for the determination of a wear curve approximating the theoretical accelerated wear curve. It is crucial that wear measurement points are captured in each of the three wear stages, namely initial wear, steady-state wear, and accelerated wear. The polynomial curve describing the wear progression will be determined using Cardano’s formulas. Ultimately, the function describing the tool wear progression will take the form (Equation (1)), where the value of V_BBmax will be determined based on the manufacturer’s data. The issue of tool wear determination in the rough milling of Inconel alloys is more extensively discussed in the literature [35].

$$\mathrm{a}{\mathrm{x}}^3+\mathrm{b}{\mathrm{x}}^2+\mathrm{c}\mathrm{x}+\mathrm{d}={\mathrm{V}}_{\mathrm{B}\mathrm{B}\mathrm{m}\mathrm{a}\mathrm{x}}$$

(1)

The material removal rate (MRR) will be determined by the ratio of the volume of the removed material to the cutting tool’s working time in the machined material.

Due to the lack of data in the literature regarding the determination of appropriate weights and the influence of individual analyzed parameters on the machining process, it was decided to use grey relational analysis (GRA) for the evaluation.

To obtain accurate results, the data must first be normalized to avoid comparing different units. This step is necessary because the variability of the characteristics will be analyzed. Each result is assigned values within the range from 0 to 1. This is a conversion method used to obtain data for further analysis (referred to as processed data). Depending on whether the analyzed value should be maximized or minimized, a different formula is applied. For parameters where the values should be minimized (e.g., P_c and F_z), Equation (2) is used, while for parameters where the values should be maximized (e.g., Q and G_max), Equation (3) is applied.

$${\mathrm{x}}_{\mathrm{i}}^{\mathrm{*}}\left(\mathrm{k}\right)={\displaystyle \frac{\max {\mathrm{x}}_{\mathrm{i}}\left(\mathrm{k}\right)-{\mathrm{x}}_{\mathrm{i}}\left(\mathrm{k}\right)}{\max {\mathrm{x}}_{\mathrm{i}}\left(\mathrm{k}\right)-\mathrm{m}\mathrm{i}\mathrm{n} {\mathrm{x}}_{\mathrm{i}}\left(\mathrm{k}\right)}}$$

(2)

$${\mathrm{x}}_{\mathrm{i}}^{\mathrm{*}}\left(\mathrm{k}\right)={\displaystyle \frac{{\mathrm{x}}_{\mathrm{i}}\left(\mathrm{k}\right)-\mathrm{m}\mathrm{i}\mathrm{n} {\mathrm{x}}_{\mathrm{i}}\left(\mathrm{k}\right)}{\max {\mathrm{x}}_{\mathrm{i}}\left(\mathrm{k}\right)-\mathrm{m}\mathrm{i}\mathrm{n} {\mathrm{x}}_{\mathrm{i}}\left(\mathrm{k}\right)}}$$

(3)

where i = l, …, m; k = l, …, n, m is the number of experimental data points, and n is the number of responses; x_i(k) represents the original sequence, x_i*(k) represents the sequence after initial data processing, and max x_i(k) denotes the maximum value of x_i(k).

Next, the grey relational coefficient ξ_i(k) is calculated from the previously normalized values. The relationship between the actual and normalized measured values is determined using Equation (4).

$${\mathsf{\xi }}_{\mathrm{i}}\left(\mathrm{k}\right)={\displaystyle \frac{{∆}_{\mathrm{m}\mathrm{i}\mathrm{n}}+\mathsf{\xi }{∆}_{\mathrm{m}\mathrm{a}\mathrm{x}}}{{∆}_{0\mathrm{i}}\left(\mathrm{k}\right)+\mathsf{\xi }{∆}_{\mathrm{m}\mathrm{a}\mathrm{x}}}}$$

(4)

where ∆_0i is calculated using Equation (5):

$${∆}_{0\mathrm{i}}=‖{\mathrm{x}}_0\left(\mathrm{k}\right)-{\mathrm{x}}_{\mathrm{i}}\left(\mathrm{k}\right)‖$$

(5)

where ∆_min is the smallest value of ∆_0i (k), and ∆_max is the largest value of ∆_0i (k) among all the compared values. ξ is the distinguishing or identification coefficient, and its value ranges from 0 to 1. ξ is typically set to 0.5.

The next step is the analysis of the grey relational grade (GRG). This is a multi-output feature analysis based on grey relational assessment. The result of this analysis transforms multiple responses into a single numerical value. The GRG (γ) is calculated using Equation (6), and its value ranges between 0 and 1.

$${\mathsf{\gamma }}_{\mathrm{i}}={\displaystyle \frac{1}{\mathrm{n}}}\sum _{\mathrm{k}=1}^{\mathrm{n}}{\mathsf{\xi }}_{\mathrm{i}}\left(\mathrm{k}\right)$$

(6)

where γ_i is the required grey relational grade for the i-th set of parameters, and n is the number of process responses. In this work, the responses are the volumetric material removal rate (Q), the volume of material predicted to be removed during the tool’s life cycle (G_max), the average maximum power consumption (P_c), and the average maximum component of cutting forces (F_z).

Based on the values of the grey relational grade (GRG, γ), a summary can be developed that includes the most favorable parameter sets and machining methods for the rough milling of Inconel alloys. The algorithm for using the described method is presented in the flowchart (Figure 1).

In order to conduct the analysis using the above-described method, it is necessary to prepare a testing setup. The Inconel sample must be properly prepared to allow for trials using both plunge milling and high-feed milling. The input material for the tests was a bar made of Inconel 718 with a diameter of 152.4 mm (6 inches) and a length of 150 mm. To avoid the issues commonly encountered in the machining of Inconel 718, preliminary machining was carried out on a FANUC Robocut C600iA/5/AWF/Z400 wire EDM machine. Another important aspect was preparing the sample so that it could be mounted on the dynamometer. Figure 2a shows the shape of the Inconel 718 sample after wire EDM machining, while Figure 2b illustrates the technical drawing of the tooling necessary to mount the material in the dynamometer. The method of mounting the sample in our experiment is designed to ensure the highest possible stiffness of the machined component. As shown in the research [36], the stiffness of the Inconel 718 alloy part is crucial for the stability of the machining process and the minimization of vibrations, which directly affects the results obtained during milling.

The measurement of forces was crucial during the experiments. It allowed for the observation and evaluation of process stability and the impact of cutting-edge wear on the process. Forces were measured in three directions aligned with the machine’s main axes—X, Y, and Z—referred to as F_x, F_y, and F_z, respectively. An important aspect was the amplitude of the forces, as it indicated process stability. The larger the fluctuations, the less stable the process, and the more likely catastrophic tool wear would occur due to vibrations between the tool’s cutting edge and the workpiece material. The force measurement setup consisted of a Kistler piezoelectric dynamometer model 9199AA, a Kistler 5070A charge amplifier for F_x, F_y, and F_z forces, a Kistler 5697A data-acquisition device, and a laptop with DynoWare 2825D software for data analysis.

To measure energy consumption, a Sonel PQM-701 power quality analyzer was used to record real-time network parameters. This allowed for the recording of active and reactive power values at the set frequency. The power quality analyzer was connected to the HAAS VF3/YT milling center according to the diagram shown in Figure 3.

The measurements of cutting insert wear were carried out in the measurement laboratory. A ZEISS O-INSPECT 332 multisensory machine with a measurement range of 300 × 200 × 200 mm was used for the wear analysis of the inserts. This was a numerically controlled measurement device equipped with both a tactile scanning head and a ZEISS Discovery V12 measuring lens, operating like a microscope, with a field of view ranging from 1.3 × 1 mm² to 16.1 × 12 mm², depending on the magnification used. Measurements could be performed in both transmitted and reflected light. Full adjustment of the light intensity in two colors—blue and red—was available, allowing for contrast adjustments during reflected light measurements, regardless of the color and material of the measured part.

The tests were conducted on a HAAS VF3/YT milling center. This is a 4-axis machining center with a working area of 1016 × 660 × 635 mm. It was equipped with an SK50 tool holder system, which is dedicated to roughing operations. The machine was fitted with a spindle, delivering a maximum power of 22.4 kW. The tool used for the measurements was a custom-made milling head from Seco Tools. This tool provides a unique opportunity to compare two milling methods (plunge milling and high-feed milling) using a single tool, which has not been possible until now. Additionally, this milling head is not commercially available. It was designed in collaboration with a tooling company and is not yet available through standard commercial channels. Standardized CNGN120712 whisker-reinforced inserts (CW100) were used in the tool design. To reduce project and production costs, the clamping system was adapted so that additional central holes for screws were not required in the inserts for mounting them on the milling head. A clamping system similar to that used in turning systems was implemented. The clamping part was made according to the [38] using a 27 mm diameter shank holder. The milling head was fastened to the holder with an M12 hex screw. The tool, along with the holder, was mounted on the machine (Figure 4).

The main research plan involved the use of two methods for milling Inconel alloy, namely high-feed milling and plunge milling. A total of 63 machining passes were planned, with 9 machining passes for each of the 7 parameter sets, 4 of which were for high-feed milling and 3 for plunge milling. All tests were conducted at a cutting speed of 800 m/min. The primary goal of determining the cutting parameters for the experiment was to validate the evaluation method and assess whether it could detect significant differences in the cutting trials under various conditions of depth of cut, feed per tooth, and milling method. The parameters applied in the experiment were determined in consultation with Seco Tools, which was the company that produced the new milling head and initially verified the parameter range in which it could be used. Additionally, we referred to studies [12,39], which analyzed the milling parameters of Inconel alloy. The cutting parameters were also selected with consideration of the milling machine’s capabilities, particularly its maximum cutting power. The main research plan is presented in the table (

Table 1. Research plan.

Parameter Set	Machining Type	fz [mm/tooth]	ae [%D] or [mm]	ap [mm]	Q [cm3/min]	Theoretical Diameter D [mm]	Spindle Speed n [rpm]	Feed Rate vf [mm/min]
1	High feed	0.3	100%	0.5	267.39	70.54	3610	7581
2	High feed	0.4	100%	0.5	356.52	70.54	3610	10,108
3	High feed	0.5	100%	0.5	445.65	70.54	3610	12,635
4	High feed	0.3	100%	1	534.78	72.8	3498	7346
5	Plunging	0.08	1	-	142.61	80	3183	1783
6	Plunging	0.08	1.5 mm	-	213.91	80	3183	1783
7	Plunging	0.08	3 mm	-	427.82	80	3183	1783

).

3. Results and Discussion

3.1. High-Feed Milling

During high-feed milling, a significantly higher value of the F_z force component was observed compared to the F_x and F_y components. In Figure 5a, which shows the progression of the F_z force component during cutting, the force values increased as the cutting-edge wear progressed. In contrast, in Figure 5b,c, which depicts the progression of the F_y and F_x force components, the force values were independent of the cutting-edge wear. The differences in results between the subsequent trials can be attributed to measurement errors in the data-acquisition system, the dynamic nature of the process, and the short cutting-cycle time.

The machine power consumption during machining was read from the graph (Figure 6), which was characterized by the presence of three points, marked in the figure. The first point corresponds to the total instantaneous machine power consumption during the activation of the main spindle rotation. The second point represents the power consumption during the spindle rotation, just before the cutting process begins, and the third point corresponds to the maximum power consumption during cutting for the given trial. The value measured at the third point was used for analysis. In the example presented in the graph, it was 22.86 kW. Regardless of the parameters used, the shape of the graph remained the same, differing only in the maximum values reached.

According to the presented comprehensive evaluation method, the wear of the cutting edge was analyzed. After each machining pass, the cutting inserts were observed and measured under a microscope. Both the flank surface and rake face of the insert were inspected. During the observation of the cutting inserts under a microscope, it was noted that the wear of ceramic inserts followed the accelerated wear mechanism. In the initial phase, micro-cracks and chipping of the cutting edges appeared, which is characteristic of ceramic tools. Then, during the stable phase, wear mainly occurred through uniform abrasion of the flank and rake surfaces. In the final phase, the wear mechanism accelerated rapidly, leading to significant chipping and the breaking away of fragments from the cutting edges of the tools. Figure 7 shows the cutting insert after completing the ninth machining pass with the following parameters: v_c = 800 m/min, f_z = 0.4 mm/tooth, a_e = 100% of the tool diameter (70.54 mm in this case), and a_p = 0.5 mm. On the rake face, chipping was observed, reaching a value of up to 0.85 mm. This distance was measured by drawing a line perpendicular to the cutting edge. No micro-chipping was found on the cutting edge itself, but on the flank face, abrasive wear of up to 1.76 mm was observed.

Based on the measurement of cutting-edge wear, the volume of material predicted to be removed during the tool’s life cycle (G_max) was calculated, taking into account the maximum allowable cutting-edge wear value (V_BBmax) of 3.5 mm, as specified by the manufacturer. The extrapolated volume of material that could be removed before reaching the cutting-edge wear limit was determined to be 106.39 cm³. Figure 8 shows the graph of V_BB wear measured as a function of the volume of machined material, along with the trend line determined by a third-degree polynomial. The data for the graph are presented in

Table 2. Data on cutting-edge wear (V_BB) as a function of the volume of machined material for high-feed milling with the CW100 insert, with the following parameters: v_c = 800 m/min, f_z = 0.3 mm/tooth, a_e = 72.8 mm, a_p = 1 mm.

Volume of Milled Material [cm3]	VBB [mm]
0	0
9.1	0.517
18.2	0.832
27.3	0.858
54.6	1.154
81.9	1.532

.

In analyzing the relationships, a summary table (

Table 3. Summary results for high-feed milling with the ceramic CW100 insert.

Parameter Set	Feed per Tooth fz [mm/tooth]	Width of Cut ae [%D]	Depth of Cut ap [mm]	Volumetric Material Removal Rate Q [cm3/min]	Predicted Material Volume to be Removed in Tool Life Cycle Gmax [cm3]	Average Maximum Power Consumption Pc [kW]	Average Maximum Cutting-Force Component Fz [N]
1	0.3	70.54	0.5	267.39	57.32	22.36	1935
2	0.4	70.54	0.5	356.52	54.042	27.28	2524
3	0.5	70.54	0.5	445.65	57.89	27.96	3174
4	0.3	72.8	1	534.78	106.39	38.87	3096

) was compiled with the parameters used for further analysis. The following factors were considered: the volumetric material removal rate (Q), the average maximum power consumption, the volume of material predicted to be removed during the tool’s life cycle (G_max), and the average maximum cutting-force component (F_z).

3.2. Plunge Milling

During plunge milling, a higher value of the F_z force component was observed compared to the F_x and F_y components. However, this difference was smaller than in the case of high-feed milling. In Figure 9a, which shows the progression of the F_z force component during cutting, the force values increased as the cutting-edge wear progressed, but this occurred less dynamically compared to high-feed milling. The F_z force values increased in a manner closer to linear. In Figure 9b,c, which shows the progression of the F_x and F_y force components, the force values were independent of cutting-edge wear, but the values were higher than those observed in high-feed milling. The differences in results between trials can be attributed to measurement errors in the data-acquisition system, the dynamic nature of the process, and the short cutting-cycle time.

Similar to high-feed milling, an analysis of cutting insert wear on both the rake face and flank face was conducted (Figure 10). The wear mechanism in plunge milling followed the same accelerated wear pattern as observed in high-feed milling. Micro-chipping was observed on the rake face, and the abrasive wear reached a value of up to 0.6 mm. This distance was measured by drawing a line perpendicular to the cutting edge. Micro-chipping was also found on the cutting edge itself, while abrasive wear on the flank face was observed, also reaching up to 0.6 mm.

Based on the cutting-edge wear measurements, the volume of material predicted to be removed during the tool’s life cycle (G_max) was calculated. The extrapolated volume of material that could be removed before reaching the cutting-edge wear limit was determined. Figure 11 shows the graph of V_BB wear measured as a function of the volume of machined material, along with the trend line determined by a third-degree polynomial. In both high-feed milling and plunge milling of Inconel alloy, an initial phase of accelerated tool wear was observed during machining. The data for the graph are presented in

Table 4. Data on the relationship between cutting-edge wear (V_BB) and the volume of machined material for plunge milling using the CW100 insert with the following parameters: v_c = 800 m/min, f_z = 0.08 mm/tooth, and a_e = 3 mm.

Volume of Milled Material [cm3]	VBB [mm]
0	0
6	0.209
12	0.42
18	0.49
36	0.51
54	0.598

.

When analyzing the relationships, a summary table (

Table 5. Summary of plunge milling test results using the ceramic CW100 insert.

Parameter Set	Feed per Tooth fz [mm/tooth]	Width of Cut ae [%D]	Depth of Cut ap [mm]	Volumetric Material Removal Rate Q [cm3/min]	Predicted Material Volume to be Removed in Tool Life Cycle Gmax [cm3]	Average Maximum Power Consumption Pc [kW]	Average Maximum Cutting-Force Component Fz [N]
5	0.08	1	25	142.61	33.61	15.41	2078
6	0.08	1.5	25	213.91	50.58	21.91	2769
7	0.08	3	25	427.82	73.16	31.08	3638

) was compiled with the parameters used for further analysis. Similar to high-feed milling, the following factors were considered: the volumetric material removal rate (Q), the average maximum power consumption, the volume of material predicted to be removed during the tool’s life cycle (G_max), and the average maximum cutting-force component (F_z).

3.3. Evaluation Method Results

The values of the four parameters analyzed in this evaluation method were calculated according to the presented methodology. First, the normalized indicators of the summary results were computed (

Table 6. Normalized indicator values from the summary results of the main tests.

Parameter Set	Normalized Volumetric Material Removal Rate Q [-]	Normalized Volume of Material Predicted to be Removed in Tool Life Cycle Gmax [-]	Normalized Average Maximum Power Consumption Pc [-]	Normalized Average Maximum Cutting-Force Component Fz [-]
1	0.318	0.326	0.704	0.770
2	0.545	0.281	0.494	1.000
3	0.773	0.334	0.465	0.517
4	1.000	1.000	0.000	0.547
5	0.000	0.000	1.000	0.944
6	0.182	0.233	0.723	0.675
7	0.727	0.543	0.332	0.000

).

Based on the normalized indicators, the grey relational coefficients (GRC) were calculated (

Table 7. Grey relational coefficient values—summary of the main test results.

Parameter Set	GRC for Volumetric Material Removal Rate ξi(Q) [-]	GRC for Volume of Material Predicted to be Removed in Tool Life Cycle ξi(Gmax) [-]	GRC for Average Maximum Power Consumption ξi(Pc) [-]	GRC for Average Maximum Cutting-Force Component ξi(Fz) [-]
1	0.423	0.426	0.628	0.685
2	0.524	0.410	0.497	1.000
3	0.688	0.429	0.483	0.509
4	1.000	1.000	0.333	0.525
5	0.333	0.333	1.000	0.900
6	0.379	0.395	0.643	0.606
7	0.647	0.523	0.428	0.430

).

In the final step, the grey relational grade (GRG) was determined. The parameter sets were ranked from best to worst according to the evaluation method. The summary results of the analysis are presented in the table (

Table 8. Results of the comprehensive evaluation method for high-performance milling of Inconel 718.

Parameter Set	Machining Type	fz [mm/ tooth]	ae [mm]	ap [mm]	Q [cm3/min]	Grey Relational Grade (GRG)	Ranking Based on the Applied Evaluation Method
1	High feed	0.3	70.54	0.5	267.39	0.541	4
2	High feed	0.4	70.54	0.5	356.52	0.608	3
3	High feed	0.5	70.54	0.5	445.65	0.527	5
4	High feed	0.3	72.8	1	534.78	0.715	1
5	Plunging	0.08	1	25	142.61	0.642	2
6	Plunging	0.08	1.5	25	213.91	0.506	7
7	Plunging	0.08	3	25	427.82	0.507	6

).

The main objective of this article was to develop, present, and verify a comprehensive evaluation method for the high-performance milling of Inconel 718, based on an experiment conducted under accelerated wear conditions. The experiment examined parameters such as the distribution of cutting-force components, total machine power consumption, cutting-edge wear, and material removal rate. The developed method allows for the classification of the applied machining methods and process parameters from the most to the least favorable, in accordance with the established process boundary conditions. The experimental studies and verification of the evaluation method demonstrated its usefulness for assessing machining processes based on different process kinematics. By conducting the experiment under accelerated wear conditions, it is possible to determine an extrapolated value of the material volume likely to be removed during the tool’s life cycle. It is also worth noting that the presented evaluation method can be applied to other types of hard-to-machine materials. Furthermore, it can be expanded to include additional factors, such as vibration measurement or temperature in the cutting zone, depending on specific needs.

An additional goal of this article was to compare the machining capabilities of Inconel 718 using two different machining methods, namely high-feed milling and plunge milling. Currently, there are no milling heads on the market with a low approach angle that allow for both plunge milling and high-feed milling. The milling head used to verify the evaluation method was custom-made. The evaluation results indicate that plunge milling using a low approach angle can be competitive compared to other rough milling methods for Inconel 718. Although four out of the top five results were achieved with the high-feed milling method, one set of plunge milling parameters ranked second among all machining trials. It is worth noting that the greatest advantage of this type of machining is the lower maximum F_z force component during the cutting process. As indicated by the literature studies [40], reducing the value of this force can significantly impact the accuracy of the machine tool during its operation. In the conducted studies, the maximum F_z force component for plunge milling was 25% lower than for high-feed milling. However, this did not result in a lower average power consumption across all machining cycles.

Based on the obtained results, it can be observed that the depth of cut (a_p), feed per tooth (f_z), and milling type have a significant impact on energy consumption, tool wear, and cutting forces. As the depth of cut increases, both energy consumption and cutting forces rise, as greater depth requires more power to remove the material. For example, in high-feed milling, increasing the depth of cut from 0.5 mm to 1 mm leads to an approximately 30% increase in the average maximum power consumption. A higher feed per tooth results in greater energy consumption and increased cutting forces due to the larger amount of material being removed, while only slightly influencing the volume of material removed by the tool during its work cycle. In both high-feed milling and plunge milling, higher feed rates cause a significant increase in axial forces (F_z), which directly affects the energy consumption of the machining process. Comparing the two milling methods, high-feed milling generally consumes more energy and leads to faster tool wear due to higher cutting forces and the larger volume of material being removed. On the other hand, plunge milling, though slower in terms of material removal, is characterized by lower cutting forces and reduced energy consumption, which can prolong tool life and reduce vibrations. In summary, increasing both the depth of cut and feed per tooth leads to higher energy consumption, faster tool wear, and greater cutting forces, with high-feed milling being more efficient for material removal but more demanding in terms of energy consumption and tool durability compared to plunge milling.

4. Conclusions

The article presents a comprehensive evaluation method for the high-performance milling of Inconel 718 alloy, which takes into account key process parameters, such as cutting forces, tool wear, machine power consumption, and material removal rate. Based on the research, it can be concluded that:

• The method has been verified for two different Inconel milling processes, allowing for the assessment of its effectiveness and applicability in real machining conditions;
• The research results showed that high-feed milling ensures higher material removal rates and lower tool wear, making it more efficient for roughing operations;
• Plunge milling was characterized by a lower axial force component (F_z), which can positively influence machining accuracy and process stability;
• The use of accelerated tool wear allows for a detailed analysis and comparison of different milling techniques and tools used in the machining process;
• Appropriate cutting parameters, such as spindle speed, feed rate, and depth of cut, significantly affect tool life and the quality of the machined surface;
• The proposed evaluation method is universal and can also be applied to other hard-to-machine materials, not just Inconel alloys;
• Optimization of process parameters, such as cutting forces and power consumption, is crucial for increasing efficiency and reducing the machining costs of high-strength alloys like Inconel 718;
• The optimal machining method, according to the assumptions of the verified evaluation method, was high-feed milling with the parameters: v_c = 800 m/min, f_z = 0.3 mm/tooth, a_e = 100% of the tool diameter (72.8 mm), and a_p = 1 mm, which is consistent with the results found in the literature [12,39].

The developed method not only allows for the analysis of a greater number of parameters simultaneously but also takes into account the aspect of energy consumption, which has not been widely studied in the literature so far. Furthermore, the use of a custom-designed tool enabled the comparison of two milling methods, which represents an innovative element in the context of the previous research on milling Inconel 718.