Deformation Analysis of Continuous Milling of Inconel718 Nickel-Based Superalloy
2. Establishment of the Prediction Model of Cutting Force and Cutting Temperature
- In the milling process, it is necessary to ignore the influence of the machine tool itself, clamping conditions, workpiece shape, material, etc. on the cutting force and cutting temperature in the process, and set the machine tool as a rigid body without deformation.
- The tool will inevitably wear out during the machining process. This factor will affect the various parameters of the tool, which will lead to changes in the cutting force and cutting temperature. In the modeling process of cutting force and cutting temperature, it is assumed that the tool is always free of wear, and the size of the cutting force and cutting temperature are not affected by tool wear.
- It is assumed that the entire machining process has been orthogonal milling, ignoring the influence of possible milling angles on cutting force and cutting temperature.
2.1. Derivation of Empirical Formula
2.2. Orthogonal Simulation Experiment Design
- Spindle speed : ;
- Axial depth of cut : ;
- Feed per tooth : .
2.3. Data Processing and Determination of Model Coefficients
2.4. Significance Tests for Predictive Models
- Cutting force regression coefficient group: , the confidence interval of is (−0.1699,4.4204), , the confidence interval of is (0.1722,0.7563), , the confidence interval of is (−0.3151,0.9670), , and the confidence interval of is (0.3998,0.7841). Statistics variable stats get: , , , obviously . Perform residual analysis on the coefficient and enter in the MATLAB window: rcoplot(r,rint), the residual graph is shown in Figure 1a:
- Cutting temperature regression coefficient group: , the confidence interval of is (0.6733,4.3544), , the confidence interval of is (−0.1066,0.3617), , the confidence interval of is (−0.3883,0.6399), , and the confidence interval of is (0.2069,0.5152). Statistics variable stats get: , , , obviously . Perform residual analysis on the coefficient and enter in the MATLAB window: rcoplot(r,rint), the residual graph is shown in Figure 1b.
2.5. Influence Rule of Cutting Process Parameters
- The degree of influence of each milling parameter on the cutting force () is in descending order, where has a larger degree of influence, has a medium influence on cutting force, and has a smaller influence on cutting force. From the perspective of reducing the cutting force, the best combination of milling parameters is: .
- The degree of influence of each milling parameter on the cutting temperature () is in descending order, where has a larger degree of influence, and the influence of and on cutting temperature is close. From the perspective of reducing the cutting temperature, the best combination of milling parameters is: .
- From Figure 2a,b, it can be seen that the milling force first increases and then decreases with the increase in the rotational speed. Since the shear angle increases with the increase in the rotational speed, the cutting deformation of the workpiece during the milling process gradually decreases, and the cutting force gradually decreases. When the rotation speed is lower than 4000 r/min, it can be seen that the change in cutting temperature shows an obvious upward trend, because during the machining process, severe friction occurs between the front and rear rake surfaces of the tool, the workpiece surface and the chip surface. These mutual frictions generate a lot of heat and gradually transfer it to other parts, so the temperature continues to increase. However, when the rotation speed continues to increase, the flow rate of the chips is also faster and more heat is taken away by the chip flow, so the temperature increases slowly.
- It can be seen from Figure 2c,d that after the increase in the feed per tooth, the thickness of the material to be cut from the workpiece increases with each turn of the milling cutter, so the cutting force gradually increases. However, at the same time, after the feed per tooth increases, the cutting deformation coefficient of the workpiece material will decrease to a certain extent due to the influence of temperature, so the change in cutting force will gradually become gentle. When the feed per tooth is increased, more material is removed per revolution of the tool, so the tool will generate more and more heat when cutting the workpiece, and the cutting temperature changes significantly.
- It can be seen from Figure 2e,f that with the gradual increase in the axial depth of cut, the area of the workpiece to be cut gradually increases, so the cutting force also increases. When the axial depth of cut is gradually increased, more heat needs to be generated to cut the workpiece material of the same path, so the change in cutting temperature will increase. However, in the actual machining process, as the axial depth of cut continues to increase, the contact length between the workpiece and the tool, the machining volume, etc. will increase significantly, and the machining environment at this time has changed significantly. Therefore, the cutting temperature does not increase in the same proportion.
3. Analysis of the Influence Law of Velocity Field, Strain Field and Strain Rate Field in the Main Shear Deformation Zone
3.1. The Influence Law of Cutting Speed
3.2. The Influence of Tool Rake Angle
4. Analysis of High Temperature Alloy Milling Process
4.1. Cutting Superalloy Test
- Test machine
- Cutting test materials
- Experimental detection device
4.2. Validation of the Prediction Model of Cutting Force and Cutting Temperature
4.3. Analysis of the Formation Process of Sawtooth Chips
4.4. The Relationship between Cutting Force, Chip Morphology and Surface Quality
4.5. Research on the Wear Morphology of Cemented Carbide Tools
- Flank wear
- Wear of rake face
- Layers of flaking
- Tool chipping
- Through orthogonal experiment analysis, the empirical formula of the cutting force prediction model is established as: ; the empirical formula of the cutting temperature prediction model is:
- Through the range analysis of the result data, it is concluded that from the perspective of reducing the cutting force, the combination of the best milling parameters is: , from the perspective of reducing the cutting temperature, the combination of the best milling parameters is: .
- The actual orthogonal cutting test was carried out on the machine tool to verify the reliability and accuracy of the prediction model of cutting force and cutting temperature.
- With the velocity field, strain field and strain rate field model of the shear deformation zone, the influence curves of cutting speed and tool rake angle on shear speed, shear strain and shear strain rate are calculated and drawn. The influence of cutting speed and tool rake angle on shear speed, shear strain and shear strain rate are analyzed.
- The mechanism of the formation of sawtooth chips is analyzed, and the distribution of stress, strain and temperature and the causes of formation are analyzed in detail by means of distribution clouds. Through a combination of theory and experiment, the relationship among cutting force, chip shape and surface quality in the milling process is analyzed. Finally, as the cutting force increases, the sawtooth of the chips becomes more and more serious, and the roughness of the machined surface becomes larger and larger.
- The formation causes and formation processes of different tool wear morphologies are analyzed. In the high-efficiency milling of Inconel718 of cemented carbide end mills, the forms of tool wear are mainly blade spalling, tool chipping, tool surface pits and surface scratches. At the same time, tool front and flank wear will occur in high-speed cutting.
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|Melting temperature||1570 °C|
|Excessive temperature||20 °C|
|Numbering||Rotating Speed||Axial Depth of Cut||Feed per Tooth||(N)||(°C)|
|Level||Rotating Speed||Axial Depth of Cut||Feed per Tooth|
|Primary and secondary order|
|Level||Rotating Speed||Axial Depth of Cut||Feed per Tooth|
|Primary and secondary order|
|Test Number||Rotating Speed|
|Axial Depth of Cut|
|Feed per Tooth|
|F (N)||T (°C)|
|Predictive Value||Measured Value||Error (%)||Predictive Value||Measured Value||Error (%)|
|Average Error (%)||6.04||7.14|
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Li, X.; Wang, Y.; Miao, L.; Zhang, W. Deformation Analysis of Continuous Milling of Inconel718 Nickel-Based Superalloy. Micromachines 2022, 13, 683. https://doi.org/10.3390/mi13050683
Li X, Wang Y, Miao L, Zhang W. Deformation Analysis of Continuous Milling of Inconel718 Nickel-Based Superalloy. Micromachines. 2022; 13(5):683. https://doi.org/10.3390/mi13050683Chicago/Turabian Style
Li, Xueguang, Yahui Wang, Liqin Miao, and Wang Zhang. 2022. "Deformation Analysis of Continuous Milling of Inconel718 Nickel-Based Superalloy" Micromachines 13, no. 5: 683. https://doi.org/10.3390/mi13050683