An energy consumption optimization method based on micro-textured tool cutting machining

Abstract

The application discloses an energy consumption optimization method based on micro-textured tool cutting machining, and relates to the technical field of green cutting manufacturing, and comprises the following steps: constructing total cutting energy consumption based on cutting plastic deformation energy, friction heat energy, chip kinetic energy and new surface forming energy; and independently designing and preparing a micro-textured tool according to the micro-textured tool machining characteristics, cutting machining parameters and workpiece material matching, so as to effectively reduce the energy consumption in the cutting process. The application can be widely applied to cutting machining, and can guide the actual cutting machining process and effectively reduce the cutting machining energy consumption according to different machining scenes and different machining materials.

Description

Technical Field

[0001] This invention relates to the field of green cutting, and in particular to an energy consumption optimization method for cutting based on microtextured tools. Background Technology

[0002] Currently, to reduce energy consumption in machining, the main approach is to optimize cutting process parameters by constructing machine tool energy consumption models and measuring machine tool power consumption with power meters. In recent years, research on microtextured cutting tools has shown that fabricating microtextures on the tool surface enables the tool to perform special functions during machining, attracting significant attention from academic and industrial communities both domestically and internationally, and providing a new direction for research in green machining.

[0003] The microtexture on the tool surface greatly reduces the tool-chip contact length and the friction distance of the chip on the rake face, which increases the outflow velocity gradient inside the chip. On the one hand, it can increase the average outflow velocity and reduce the chip deformation. On the other hand, it can also make the chip more curved and more easily detach from the rake face, avoiding chip agglomeration and friction on the rake face, and effectively reducing cutting energy consumption.

[0004] Chinese Patent (201510118325.4), "Multi-objective Optimization Method for Multi-Step CNC Milling Process Parameters Oriented to Energy Efficiency," addresses the high energy consumption and cost issues in milling by optimizing spindle speed, feed per tooth, depth of cut, milling width, and number of steps during the milling process. Chinese Patent (202110291935.X), "An Energy-Saving Optimization Method for CNC Lathe Machining Parameters in the Cutting Stage," analyzes the lathe's energy consumption characteristics during the cutting process and uses an adaptive ant colony algorithm to solve the model, obtaining the optimal turning parameters and optimizing energy consumption during the CNC lathe's cutting stage. Summary of the Invention

[0005] The main objective of this invention is to overcome the shortcomings of existing technologies that optimize cutting energy consumption by adjusting machining parameters, and to propose an energy consumption optimization method based on microtextured cutting tools.

[0006] The present invention adopts the following technical solution:

[0007] An energy consumption optimization method for cutting processes based on micro-textured tools includes:

[0008] S101, Obtain the relationship between the tool-chip contact length and the cutting shear angle, as follows:

[0009] l f =ma c [tanβ+tan(φ-γ0)]

[0010] Among them, l fThe length of the tool-chip contact is represented by m, which is a parameter characterizing the stress distribution on the contact surface between the chip and the rake face; a c Indicates chip thickness; β represents the friction angle; φ represents the cutting shear angle; γ0 represents the tool rake angle;

[0011] S102, Obtain the cutting plastic deformation energy as follows:

[0012]

[0013] Among them, W d Indicates the plastic deformation energy during cutting; a w Indicates the cutting width; Indicates shear strength; v c β represents the cutting speed; β represents the friction angle.

[0014] The relationship between shear strength and cutting speed is as follows:

[0015]

[0016] S103, obtaining frictional heat energy, as follows:

[0017]

[0018] Among them, W f This represents frictional heat energy; C1 is a correction factor.

[0019] S104, obtain the kinetic energy of the chip, as follows:

[0020]

[0021] Among them, W k ρ represents the kinetic energy of the chip; ρ represents the material density.

[0022] S105, Obtain the new surface forming energy, as follows:

[0023] W n =Ra w v c

[0024] Among them, W n R represents the forming energy of the new surface; R represents the fracture energy.

[0025] S106, the total cutting energy consumption is constructed based on cutting plastic deformation energy, frictional heat energy, chip kinetic energy, and new surface forming energy, as follows:

[0026]

[0027] Among them, W c t represents the total cutting energy consumption; t represents the machining time.

[0028] S107. Based on the cutting characteristics of microtextured tools, key feature parameters of microtexture on the tool surface are selected, and a three-dimensional element model of microtexture is established.

[0029] S108 optimizes the key characteristic parameters of microtexture by incorporating the constitutive parameters of the material into the finite element analysis and combining different cutting parameter experiments with orthogonal experimental methods, with the total cutting energy consumption as the optimization objective.

[0030] S109, using MATLAB software to perform topology optimization on the simulated and optimized microtexture;

[0031] S110, microtextured tools are prepared based on optimized key feature parameters and topology-optimized microtextures;

[0032] S111 uses a microtextured tool for cutting to reduce cutting energy consumption.

[0033] Preferably, the fabrication of microtextured tools based on optimized key feature parameters and topology-optimized microtextures specifically includes:

[0034] Remove the oxide layer from the surface of the tool body, and then perform ultrasonic cleaning, nitrogen blowing and vacuum drying in sequence;

[0035] An experimental system and monitoring platform for energy field-assisted liquid phase laser processing of microtextures were established;

[0036] Microtextured tools were prepared using an energy field-assisted liquid phase laser processing method.

[0037] Preferably, the removal of the oxide layer on the surface of the tool body is achieved by micro-blasting.

[0038] Preferably, the vacuum drying is performed using a vacuum oven.

[0039] Preferably, the energy field auxiliary is an electric field or a magnetic field.

[0040] Preferably, the energy field liquid phase laser processing method includes the design of liquid materials, liquid phase parameters, and laser processing parameters.

[0041] Preferably, the key feature parameters include: cross-sectional morphology, edge distance, edge angle, arrangement density, and depth.

[0042] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0043] This invention discloses an energy consumption optimization method for cutting machining based on microtextured tools. It systematically analyzes the cutting energy consumption model in the cutting process of microtextured tools, and designs and prepares microtextures on the tool surface based on the cutting characteristics of microtextured tools, cutting parameters and workpiece material matching. When applied to actual cutting machining, it can effectively reduce cutting energy consumption. Attached Figure Description

[0044] Figure 1 This is a flowchart of an energy consumption optimization method for microtextured tool cutting in an embodiment of the present invention;

[0045] Figure 2 This is a schematic diagram of the energy consumption of the cutting unit in an embodiment of the present invention;

[0046] Figure 3 This is a schematic diagram of a microtextured tool according to an embodiment of the present invention;

[0047] Figure 4 This is a schematic diagram of the key feature parameters of the microtexture on the tool surface in an embodiment of the present invention. Detailed Implementation

[0048] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, it should be understood that after reading the teachings of this invention, those skilled in the art can make various alterations or modifications to the invention, and these equivalent forms also fall within the scope defined by the appended claims.

[0049] Example 1

[0050] During the cutting process, the intense deformation caused by the mutual squeezing and friction between the tool and the workpiece material, as well as the changes in the microstructure of the chips and the machined surface, inevitably consume a large amount of cutting energy. The specific analysis is as follows.

[0051] 1) The influence of cutting parameters on cutting energy consumption: The higher the cutting speed, the smaller the deformation of the chip. This is mainly because the propagation speed of plastic deformation is relatively slow. After increasing the cutting speed, the flow speed of the chip will be faster than the slip speed of the internal lattice. This means that when the chip flows to the initial slip plane, the internal lattice has not yet generated dislocation slip. The chip only begins to deform after moving backward a certain distance. That is, the initial shear plane moves backward, causing the first deformation zone to move backward, the shear angle φ to increase, and the slip amount to decrease. In addition, increasing the depth of cut, cutting speed, and feed rate can effectively reduce the friction coefficient between the tool and the chip, thereby reducing friction, thinning the friction retention layer between the chip and the rake face, increasing the average outflow velocity in the chip thickness direction, and reducing the chip deformation.

[0052] 2) The influence of microtexture on cutting energy consumption: Compared with the effect of cutting parameter optimization on chip deformation, the main mechanism of microtexture is that after introducing microtexture, the tool-chip contact length is greatly reduced, the friction distance of the chip on the rake face is reduced, and the outflow velocity gradient inside the chip is increased. On the one hand, it can increase the average outflow velocity and reduce the chip deformation. On the other hand, it can also make the chip more curved, so that it can detach from the rake face more smoothly and avoid chip agglomeration and friction on the rake face. Further analysis will be carried out in the subsequent experimental section based on experimental data.

[0053] 3) Cutting parameters and microtexture affect cutting energy consumption by changing the cutting shear angle φ. Studies have found that the tool-chip contact length l f Closely related to the shear angle, the blade-chip contact length not only affects the degree of chip deformation, but also the energy dissipation generated by friction between the chip and the rake face, as well as the chip bending.

[0054] Based on the above analysis, a theoretical model of the cutting energy consumption of micro-textured tools is constructed. (See also...) Figure 2 As shown, energy consumption in metal cutting mainly occurs in three deformation zones; the energy consumption of the cutting unit includes the plastic deformation energy of the chips in the first deformation zone, the heat energy generated by friction and the kinetic energy of the chips flowing out in the second deformation zone, and the forming energy (elastic deformation energy) of the newly processed surface of the workpiece and the energy consumed by adhesion in the third deformation zone.

[0055] Based on the above analysis, this embodiment presents an energy consumption optimization method for cutting machining using microtextured tools, comprising the following steps.

[0056] S101, obtain the relationship between the tool-chip contact length and the cutting shear angle. Specifically:

[0057] l f =ma c [tanβ+tan(φ-γ0)]

[0058] Among them, l f The length of the contact between the tool and the chip is represented by m, which is a parameter characterizing the stress distribution on the contact surface between the chip and the rake face. To simplify the calculation, m is always substituted with 1; a c β represents the chip thickness; φ represents the friction angle; φ represents the cutting shear angle; and γ0 represents the tool rake angle.

[0059] S102, to obtain cutting plastic deformation energy.

[0060] Specifically, the chip deformation energy is analyzed based on factors such as grain dislocations, grain refinement, and phase transformation during chip formation, as follows:

[0061]

[0062] Among them, W d Indicates the plastic deformation energy during cutting; a w Indicates the cutting width; Indicates shear strength; v c β represents the cutting speed; β represents the friction angle.

[0063] There is a certain conversion relationship between shear strength and cutting speed, as follows:

[0064]

[0065] S103, to obtain frictional heat energy.

[0066] Specifically, the frictional heat energy is analyzed based on the friction between the tool's rake and flank faces and the chips and workpiece, as follows:

[0067]

[0068] Among them, W f It represents frictional heat energy; C1 is a correction factor, which is generally 1.3 in TC4 cutting.

[0069] S104, obtain the kinetic energy of the chip.

[0070] Specifically, the kinetic energy of the chips is analyzed based on the chip flow state during the cutting process, as follows:

[0071]

[0072] Among them, W k ρ represents the kinetic energy of the chip; ρ represents the material density.

[0073] S105, to obtain new surface forming energy.

[0074] Specifically, the forming energy of the new surface is analyzed based on the elastic deformation of the already processed surface, as follows:

[0075] W n =Ra w v c

[0076] Among them, W n R represents the forming energy of the new surface; R represents the fracture energy, which is related to the inherent properties of the material.

[0077] S106, the total cutting energy consumption is constructed based on cutting plastic deformation energy, frictional heat energy, chip kinetic energy, and new surface forming energy. As follows:

[0078]

[0079] After the total cutting energy consumption model is constructed, the design and preparation of tool surface microtexture based on workpiece material matching includes the extraction and optimization of key feature parameters of microtexture and the preparation of tool surface microtexture. Its core lies in the independent design of tool surface microtexture morphology based on workpiece material matching and cutting parameters, and the precise preparation of microtextured tools, including the following steps.

[0080] S107. Based on the cutting characteristics of microtextured tools, key feature parameters of microtexture on the tool surface are selected, and a three-dimensional meta-model of microtexture is established.

[0081] S108 optimizes the key characteristic parameters of microtexture by incorporating the constitutive parameters of the material into the finite element analysis and combining orthogonal experimental methods with different cutting parameter tests, with the total cutting energy consumption as the optimization objective.

[0082] S109 uses MATLAB software to perform topology optimization on the simulated and optimized microtexture.

[0083] S110, a microtextured tool is prepared based on optimized key feature parameters and topology-optimized microtexture.

[0084] Microtextured tools are fabricated based on optimized key feature parameters and topology-optimized microtextures, specifically including:

[0085] Remove the oxide layer from the surface of the tool body, and then perform ultrasonic cleaning, nitrogen blowing and vacuum drying in sequence;

[0086] An experimental system and monitoring platform for energy field-assisted liquid phase laser processing of microtextures were established;

[0087] Microtextured tools were prepared using an energy field-assisted liquid phase laser processing method.

[0088] S111 uses a microtextured tool for cutting to reduce cutting energy consumption.

[0089] The microtextured cutting tool prepared based on the above method is shown in [reference]. Figure 3 As shown.

[0090] In this embodiment, see Figure 4 As shown, the key characteristic parameters of the microtexture on the tool surface include: cross-sectional morphology, edge distance, edge angle, arrangement density, and depth.

[0091] In this embodiment, material constitutive parameters are physical parameters that describe the response and behavior of a material under external loading. They are used to describe the mechanical properties of a material, i.e., its response to force or deformation. These parameters can be used to establish a constitutive model of the material, i.e., to simulate the mechanical behavior of the material through mathematical equations. The selection and description of material constitutive parameters depend on the type of material and the mechanical behavior being studied. Some common material constitutive parameters include:

[0092] 1. Elastic modulus: describes the degree of elastic deformation of a material under stress. It represents the unit strain caused by a unit stress.

[0093] 2. Shear modulus: describes the degree of deformation of a material when subjected to shear stress.

[0094] 3. Poisson's ratio: describes the ratio of lateral shrinkage to longitudinal elongation of a material under stress.

[0095] 4. Yield strength: describes the strength of a material when it begins to plastically deform after being subjected to stress exceeding its elastic limit.

[0096] 5. Fracture toughness: describes a material's ability to resist crack propagation.

[0097] 6. Hardness: Describes a material's ability to resist localized pressure.

[0098] 7. Fatigue strength: describes the ability of a material to resist fracture under cyclic loading.

[0099] 8. Plastic constitutive parameters: These describe the plastic behavior of a material after it exceeds its yield strength, such as flow stress and plastic strain.

[0100] 9. Temperature-dependent parameters: Temperature-dependent parameters also need to be considered for the material behavior under high or low temperature conditions.

[0101] Example 2

[0102] The following design focuses on the microtexture for titanium alloy machining with low energy consumption. Based on the characteristics of laser processing, the microtexture is set as a trapezoidal cross-section (in this embodiment, a trapezoidal cross-section is selected as the cross-sectional shape of the microtexture, and the width of the texture is used to represent the trapezoidal cross-section). The edge distance, edge angle, depth, width, and spacing of the texture are optimized. To ensure the accuracy, comprehensiveness, and reliability of the experimental results, an orthogonal experimental design is used for this experiment. Five levels are selected for each factor, as shown in Tables 1 and 2 below.

[0103] During machining, immense pressure and high temperatures are generated between the rake face and the chip, causing adhesion between the rake face and the bottom layer of the chip. This adhesion layer (tool tip and bottom layer of chip) exhibits internal shear slip and frictional losses, resulting in a certain amount of cutting energy loss. On one hand, this energy consumption is extremely small compared to the entire cutting process; on the other hand, the energy dissipation in this process can be included in the plastic deformation energy of the chip and the frictional energy between the chip and the rake face. Therefore, the adhesion energy loss is not quantitatively analyzed when constructing the total cutting energy consumption model, but a certain amount of adhesion energy loss is included in the simulation.

[0104] Table 1 Orthogonal Experiment

[0105]

[0106] Select L 25 (5 6 The experiment was arranged using an orthogonal array. The experimental results of five parts of cutting energy consumption, namely cutting plastic deformation energy, frictional heat energy, chip kinetic energy, new surface forming energy, and adhesion energy consumption, were obtained through cutting simulation. The results are shown in Table 2.

[0107] Table 2. Orthogonal experimental parameters and results

[0108]

[0109]

[0110] As shown in the range analysis in Table 2, within the selected cutting parameter range, the order of influence of the five factors on the cutting energy consumption is as follows: texture edge distance > spacing > depth > edge angle > upper width. The range value of texture edge distance is 20.778, which means that it is the factor with the greatest impact on cutting energy consumption among the five factors. Based on the range analysis results, the texture parameter combination with the lowest energy consumption is selected. Combined with the actual cutting conditions, the final optimal parameters are determined as follows: upper width 40μm, spacing 30μm, depth 30μm, edge distance 60μm, and edge angle 60°.

[0111] It should be noted that the energy consumption formulas above belong to the theoretical analysis of cutting energy consumption. The most crucial parameter in the theoretical analysis of the influence of cutting parameters and tool structure on cutting energy consumption is the shear angle, which can be converted into the chip-tool contact length for practical analysis. Introducing microtextures can alter the tool's shear angle and chip-tool contact length; however, different microtextures and their density have varying effects on these parameters. Therefore, this embodiment selects five parameters representing the main characteristics of the microstructure to optimize cutting energy consumption.

[0112] The above are merely specific embodiments of the present invention, but the design concept of the present invention is not limited thereto. Any non-substantial modifications made to the present invention using this concept shall be considered as infringing upon the protection scope of the present invention.

Claims

1. An energy consumption optimization method for cutting processes based on micro-textured tools, characterized in that, include: S101, Obtain the relationship between the tool-chip contact length and the cutting shear angle, as follows: l f =and c [tanβ+tan(ϕ-γ0)] Among them, l f The length of the tool-chip contact is represented by m, which is a parameter characterizing the stress distribution on the contact surface between the chip and the rake face; a c Indicates chip thickness; β represents the friction angle; φ represents the cutting shear angle; γ0 represents the tool rake angle; S102, Obtain the cutting plastic deformation energy as follows: Among them, W d Indicates the plastic deformation energy during cutting; a w Indicates the cutting width; Indicates shear strength; v c β represents the cutting speed; β represents the friction angle. The relationship between shear strength and cutting speed is as follows: S103, obtaining frictional heat energy, as follows: Among them, W f This represents frictional heat energy; C1 is a correction factor. S104, obtain the kinetic energy of the chip, as follows: Among them, W k ρ represents the kinetic energy of the chip; ρ represents the material density. S105, Obtain the new surface forming energy, as follows: W n =Ra w v c Among them, W n R represents the forming energy of the new surface; R represents the fracture energy. S106, the total cutting energy consumption is constructed based on cutting plastic deformation energy, frictional heat energy, chip kinetic energy, and new surface forming energy, as follows: Among them, W c t represents the total cutting energy consumption; t represents the machining time. S107. Based on the cutting characteristics of microtextured tools, key feature parameters of microtexture on the tool surface are selected, and a three-dimensional element model of microtexture is established. S108 optimizes the key characteristic parameters of microtexture by incorporating the constitutive parameters of the material into the finite element analysis and combining different cutting parameter experiments with orthogonal experimental methods, with the total cutting energy consumption as the optimization objective. S109, using MATLAB software to perform topology optimization on the simulated and optimized microtexture; S110, microtextured tools are prepared based on optimized key feature parameters and topology-optimized microtextures; S111 uses a microtextured tool for cutting to reduce cutting energy consumption.

2. The energy consumption optimization method for cutting machining based on microtextured tools according to claim 1, characterized in that, Microtextured tools are fabricated based on optimized key feature parameters and topology-optimized microtextures, specifically including: Remove the oxide layer from the surface of the tool body, and then perform ultrasonic cleaning, nitrogen blowing and vacuum drying in sequence; An experimental system and monitoring platform for energy field-assisted liquid phase laser processing of microtextures were established; Microtextured tools were prepared using an energy field-assisted liquid phase laser processing method.

3. The energy consumption optimization method for cutting machining based on microtextured tools according to claim 2, characterized in that, The oxide layer on the surface of the tool body is removed by micro-blasting.

4. The energy consumption optimization method for cutting machining based on microtextured tools according to claim 2, characterized in that, The vacuum drying is performed using a vacuum oven.

5. The energy consumption optimization method for cutting machining based on microtextured tools according to claim 2, characterized in that, The energy field auxiliary is an electric field or a magnetic field.

6. The energy consumption optimization method for cutting machining based on microtextured tools according to claim 2, characterized in that, The energy field liquid phase laser processing method includes the design of liquid materials, liquid phase parameters, and laser processing parameters.

7. The energy consumption optimization method for cutting machining based on microtextured tools according to claim 1, characterized in that, The key characteristic parameters include: cross-sectional morphology, edge distance, edge angle, arrangement density, and depth.

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