Milling process parameter optimization method for medium-high volume fraction aluminum-based silicon carbide material
By adopting the optimization calculation method of tool wear rate and material removal rate based on genetic algorithm in the milling of aluminum-based silicon carbide materials, the problems of PCD milling cutter wear control and material removal rate improvement are solved, and efficient and economical processing effects are achieved.
Patent Information
- Application Number
- CN202411960423.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-30
- Publication Date
- 2025-03-07
AI Technical Summary
The prior art is difficult to effectively control the wear of PCD milling cutters in the milling process of aluminum-based silicon carbide materials, and the process parameter optimization method is time-consuming, costly and poor flexibility.
The cutting parameter optimization calculation method based on tool wear rate and material removal rate is adopted, and reasonable cutting parameters are calculated through genetic algorithms, and the coefficient ratio of tool wear rate and material removal rate is allocated to achieve wear control and material removal rate improvement of PCD milling cutters during milling aluminum-based silicon carbide materials.
The wear control and material removal rate of PCD milling cutters during milling aluminum-based silicon carbide materials is realized, which reduces tool costs, improves processing efficiency, and provides theoretical support for high-quality and efficient processing.
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Figure CN119566977A_ABST
Abstract
Description
Technical Field
[0001] The invention belongs to the technical field of composite material technology, relates to the cutting of difficult-to-cut materials, and specifically relates to a method for optimizing milling process parameters of medium- and high-volume aluminum-based silicon carbide materials. Background Art
[0002] Aluminum-based silicon carbide composite materials have the characteristics of high specific strength, good dimensional stability, not easy to deform, and stability, so they have been widely used in the fields of inertial navigation platform structural parts manufacturing, optical components, etc. Especially in some high-precision occasions, such as reflector brackets, sensor brackets, azimuth and pitch axis systems, etc.
[0003] The above application scenarios require that the structural parts remain unchanged under frequent alternating hot and cold conditions, and aluminum-based silicon carbide with excellent performance can meet its stringent requirements. Although aluminum-based silicon carbide has many excellent properties, this type of material is hard and brittle, and the hardness of its reinforcing phase particles is second only to diamond. It is a typical difficult-to-process material. When using PCD insert cutters during production, tool wear is still very serious. Therefore, effectively suppressing the wear of PCD milling cutters when processing aluminum-based silicon carbide is the key to achieving high-quality and economical processing of aluminum-based silicon carbide. In order to effectively control the wear of PCD milling cutters and improve the material removal rate of aluminum-based silicon carbide milling, the tool wear rate and material removal rate theoretical calculations are used to optimize the process parameters.
[0004] However, most of the current research on milling aluminum-based silicon carbide composites is based on single-factor experiments, which guide the selection and optimization of parameters by analyzing the influence of parameter changes on surface quality, milling force or tool wear. The conclusions of this method are mostly suggestions, and the specific parameter values are not involved.
[0005] At the same time, some studies are based on multi-factor experiments, which construct empirical equations by fitting experimental results to optimize parameters. However, the experimental method of selecting and optimizing process parameters is time-consuming, costly, and the results are limited by the materials used, which has poor flexibility.
[0006] Therefore, if a cutting parameter optimization calculation method based on tool wear rate and material removal rate can be invented for aluminum-based silicon carbide milling processing, and the simultaneous optimization control of PCD tool flank wear rate and material removal rate can be achieved, it will further improve the aluminum-based silicon carbide cutting process parameter optimization theory and provide theoretical support for the high-quality and efficient processing of aluminum-based silicon carbide materials. Summary of the invention
[0007] In view of the problems existing in the prior art, the present invention provides a cutting parameter optimization calculation method for tool wear rate and material removal rate in milling processing of medium and high volume aluminum-based silicon carbide materials. By allocating the ratio of tool wear rate and material removal rate calculation coefficients, a genetic algorithm is used to optimize and calculate reasonable cutting parameters to improve processing efficiency and reduce tool costs.
[0008] The technical solution adopted by the present invention to solve the technical problem is: a method for optimizing the milling process parameters of medium-high volume fraction aluminum-based silicon carbide materials, comprising the following steps:
[0009] Step 1: Calculation of tool wear rate: Model and calculate the tool material wear volume loss caused by two-body abrasive wear, three-body abrasive wear and adhesive wear respectively. Using the equivalent relationship between the total tool flank wear loss and the tool flank geometric loss, an analytical model of tool flank wear rate for predicting the tool flank wear band width VB is established.
[0010] During a fully immersed milling cycle, the tool flank wear loss caused by the two-body abrasive wear enhancing particles is expressed as: Where A1 is the average cross-sectional area of the grooves formed by the reinforcing particles in two-body abrasive wear, V c is the milling speed, C two-body The wear characteristics of ceramic particle reinforced metal matrix composites in two-body abrasive wear, wear characteristics C two-body The average size of the reinforced particles R 1aver , volume fraction f p , the probability of participating in two-body abrasive wear P two-body , the probability of debonding f debond The relationship between them is expressed as: two-body =c1(1-f debond )f p R 1aver P two-body , where c1 is the model coefficient, N two-body is the number of reinforcing particles involved in two-body abrasive wear in the wear zone of the tool flank, expressed as Where N p is the total number of reinforcing particles acting on the wear zone of the tool flank, f(R1) is the distribution function of the size of the reinforcing particles in the metal matrix, R c Critical particle size for two-body abrasive wear enhancement;
[0011] During a fully immersed milling cycle, the tool flank wear loss caused by the three-body abrasive wear enhancing particles is expressed as: Among them C three-bodyrepresents the wear characteristics of the reinforcing particles in three-body abrasive wear, K1 and i are model constants, p2 is the normal load applied to a single reinforcing particle in three-body abrasive wear, κ is the complementary angle of the reinforcing particle tip angle θ, N three-body is the number of reinforcing particles involved in three-body abrasive wear in the wear zone of the tool flank;
[0012] During a fully submerged milling cycle, the tool flank wear loss caused by adhesive wear can be expressed as: Where P0 represents the probability of forming wear pits on the wear zone of the tool flank, n w V is the number of micro-welding points per unit area on the tool flank wear zone, w Tool volume loss caused by each micro weld that breaks in a weak area of tool material;
[0013] During a fully submerged milling cycle, the tool flank wear loss caused by adhesive wear can be expressed as: The adhesive wear model coefficient is: K adhesion =(P0h w f adhesion ) / (2b), the total tool back surface wear loss is the sum of the wear losses of two-body abrasive wear, three-body abrasive wear and adhesive wear ΔV wear =ΔV two-body +ΔV three-body +ΔV adhesion ; The wear rate of the tool back face wear zone is expressed as:
[0014]
[0015] Under given milling conditions, the tool flank wear rate can be calculated based on the tool flank wear band width VB at different times, and the VB value at each subsequent moment can be calculated, where the model coefficient K two-body , K three-body , K adhesion and c are constant coefficients related to tool and workpiece materials;
[0016] Step 2: Calculate the material removal rate: When the radial cutting depth a≤the radius of the milling cutter R, the cutting area
[0017]
[0018] Wherein the radial milling depth is a, the axial milling depth is b, and the feed per tooth is c;
[0019] The material removal rate is the volume of metal removed per unit time (s), which is a function of the spindle speed N, the number of milling cutter teeth n, the milling cutter radius R, the radial milling depth a, the axial milling depth b, and the feed per tooth c, and is completely determined by the milling parameters; therefore, the milling material removal rate
[0020] Step 3: Based on the tool wear model and material removal rate model, the multi-objective optimization calculation of aluminum-based silicon carbide milling process parameters is realized with the goal of maximizing the material removal rate and maximizing the cutting time for the tool to reach the set wear band width threshold, and the Pareto optimal solution set is obtained:
[0021]
[0022] x=[n,f z ,a p ].
[0023] Furthermore, the average cross-sectional area A1 of the grooves formed by the reinforcing particles in the two-body abrasive wear in step 1 can be approximately: Where δ is the relative immersion depth δ = p1 / (2.9πR2Y). The reinforcing particles are pulled out from the workpiece surface under the action of the tool edge and captured in the interface between the workpiece material and the tool flank, causing three-body abrasive wear during the machining process.
[0024] Furthermore, in the step 1, the wear characteristics of the particles in the three-body abrasive wear are enhanced. three-body It is a function of the size and volume fraction of the reinforcing particles and the probability of debonding of the reinforcing particles, expressed as C three-body =c2f p f debond R 1aver , where c2 is the model coefficient.
[0025] Furthermore, the tool volume loss caused by each micro-welding in step 1 is V w =h w A w , where the average cross-sectional area of the micro-weld is A w It can be expressed as the roughness hardness H of the workpiece material w Function A w =(pf adhesion ) / (n w VxD w ).
[0026] The beneficial effects of the present invention are as follows: the present invention proposes a multi-objective optimization calculation method for process parameters based on tool wear rate and material removal rate, which for the first time considers the allocation weights of tool wear rate and material removal rate to the process parameter optimization objectives, thereby realizing the reasonable calculation and optimization of process parameters when PCD milling cutter mills aluminum-based silicon carbide, improving the material removal rate while reducing the tool wear rate. BRIEF DESCRIPTION OF THE DRAWINGS
[0027] Figure 1 A flow chart of an optimization method provided for an embodiment of the present invention;
[0028] Figure 2 A schematic diagram of calculating the geometric loss of flank wear of an aluminum-based silicon carbide flat-bottom milling cutter provided in an embodiment of the present invention;
[0029] Figure 3 A schematic diagram of geometric calculation of the milling area per tooth provided in an embodiment of the present invention;
[0030] Figure 4 The present invention provides a multi-objective optimization Pareto optimal solution for process parameters. DETAILED DESCRIPTION
[0031] The following will describe the implementation of the algorithm for optimizing the milling process parameters of aluminum-based silicon carbide materials based on tool wear rate and material removal rate according to the present invention in combination with the accompanying drawings and specific examples.
[0032] Taking the MATLAB program as an example, an optimization algorithm model for aluminum-based silicon carbide milling process parameters is established, and the method proposed in the present invention is used for optimization calculation. The calculation results of tool wear rate and material removal rate are output by the calculation program, and the optimization target is selected: tool wear rate, material removal rate; input weight coefficient; input initial value range of process parameters; set initial value of parameter; initial value is within the range; perform genetic algorithm optimization calculation; output Pareto chart, and select the optimal process parameters.
[0033] Reference Figure 1 , Figure 2 As shown, the present invention discloses a method for optimizing milling process parameters of medium- and high-volume aluminum-based silicon carbide materials, and the steps are as follows.
[0034] Step 1: Calculation of tool wear rate.
[0035] In the milling of aluminum-based silicon carbide composites, the wear mechanism of the flank of the PCD flat-bottom milling cutter is abrasive wear and adhesive wear. The wear volume loss of tool material caused by two-body abrasive wear, three-body abrasive wear and adhesive wear is modeled and calculated respectively.
[0036] In the calculation of wear loss of different wear types, the wear characteristics of ceramic particle reinforced metal matrix composites, the probability distribution of the real size of the reinforced particles, the agglomeration effect of the reinforced particles in the roughness of the workpiece material, and the scale effect of the micro-milling process are considered. The total tool flank wear loss is equal to the sum of the wear losses of the three wear types. Using the equivalent relationship between the total tool flank wear loss and the tool flank geometric loss, an analytical model of tool flank wear rate for predicting the flank wear band width VB is established.
[0037] The higher the volume fraction of the metal matrix reinforcement particles, the larger the average particle size, and the greater the wear of the relative action part. In a fully immersed milling cycle, the tool back face wear loss caused by the two-body abrasive wear reinforcement particles can be expressed as Where A1 is the average cross-sectional area of the grooves formed by the reinforcing particles in two-body abrasive wear, N two-body is the number of reinforcing particles involved in two-body abrasive wear in the wear zone of the tool flank, V c is the milling speed, C two-body Represents the wear characteristics of ceramic particle reinforced metal matrix composites in two-body abrasive wear.
[0038] Wear characteristics of ceramic particle reinforced metal matrix composites in two-body abrasive wear two-body It is related to the size of the reinforcement particles, volume fraction, probability of participating in two-body abrasive wear, probability of debonding, etc., and can be expressed as: C two-body =c1(1-f debond )f p R 1aver P two-body , where c1 is the model coefficient, f p is the volume fraction of reinforcing particles, R 1aver is the average size of the reinforcement particles, P two-body is the probability of reinforcing particles participating in two-body abrasive wear, f debond It is the probability of enhancing particle debonding.
[0039] The number of reinforcing particles N involved in two-body abrasive wear in the wear zone of the tool flank two-body It can be expressed as: Where N p is the total number of reinforcing particles acting on the wear zone of the tool flank, f(R1) is the distribution function of the size of the reinforcing particles in the metal matrix, R c Critical particle size for two-body abrasive wear enhancement.
[0040] The average cross-sectional area A1 of the grooves formed by the reinforcing particles in two-body abrasive wear can be approximated as: Where δ is the relative immersion depth δ = p1 / (2.9πR2Y). The reinforcing particles are pulled out from the workpiece surface under the action of the tool edge and captured in the interface between the workpiece material and the tool flank, causing three-body abrasive wear during the machining process.
[0041] Considering the wear characteristics of the composite material, the tool flank wear loss caused by the three-body abrasive wear enhancement particles during a full immersion milling cycle can be expressed as:
[0042]
[0043] Among them C three-body represents the wear characteristics of the reinforcing particles in three-body abrasive wear, K1 and i are model constants, p2 is the normal load applied to a single reinforcing particle in three-body abrasive wear, κ is the complementary angle of the reinforcing particle tip angle θ, N three-body It is the number of reinforcing particles involved in three-body abrasive wear in the wear zone of the tool flank.
[0044] Wear characteristics of reinforcing particles in three-body abrasive wear three-body It is a function of the size and volume fraction of the reinforcing particles and the probability of debonding of the reinforcing particles, which can be expressed as: C three-body =c2f p f debond R 1aver , where c2 is the model coefficient.
[0045] Normal load p2 applied to a single reinforcing particle in three-body abrasive wear and the number of reinforcing particles N involved in three-body abrasive wear in the tool flank wear zone three-body The product of is equal to the total normal load p2N applied to the three-body abrasive wear enhancement particle three-body = pf three-body , where p is the total apparent normal contact load.
[0046] During the milling process of aluminum-based silicon carbide composites, the tool flank is often accompanied by the formation of adhesion and accumulation layers of the workpiece material, and the tool material is susceptible to severe adhesive wear.
[0047] During a fully submerged milling cycle, the tool flank wear loss caused by adhesive wear can be expressed as: Where P0 represents the probability of forming wear pits on the wear zone of the tool flank, n w V is the number of micro-welding points per unit area on the tool flank wear zone, w The tool volume loss caused by each micro weld that breaks in a weak area of the tool material.
[0048] Tool volume loss caused by each micro-weld: V w =hw A w , where the average cross-sectional area of the micro-weld is A w It can be expressed as the roughness hardness H of the workpiece material w Function A w =(pf adhesion ) / (n w VxD w ).
[0049] The roughness hardness of aluminum-based silicon carbide composites is an exponential function of the wear band width VB of the tool flank: H w =be cVB .
[0050] Therefore, the tool flank wear loss caused by adhesive wear during a fully submerged milling cycle can be expressed as: The adhesive wear model coefficient K adhesion =(P0h w f adhesion ) / (2b) Tool wear is mainly manifested in tool flank wear. The total tool flank wear loss is the sum of the wear losses of two-body abrasive wear, three-body abrasive wear and adhesive wear ΔV wear =ΔV two-body +ΔV three-body +ΔV adhesion .
[0051] The wear rate of the tool flank wear zone is expressed as:
[0052]
[0053] Under given milling conditions, the tool flank wear rate can be calculated based on the tool flank wear band width VB at different times, and the VB value at each subsequent moment can be calculated, where the model coefficient K two-body , K three-body , K adhesion and c are constant coefficients related to the tool and workpiece material and do not change with machining conditions and tool geometry. Therefore, a calibration can be applied to a variety of machining situations.
[0054] Step 2: Calculate the material removal rate. The magnitude of the milling force determines the power consumed during the milling process and the deformation of the machined parts, and has a direct impact on the machining accuracy and quality. In the calculation of the milling force, the calculation of the milling area and the material removal rate are important links.
[0055] Figure 3 The figure shows the geometric calculation of the milling area per tooth: When the radial cutting depth a≤milling cutter radius R, the cutting area represented by the shaded part is Wherein the radial milling depth is a, the axial milling depth is b, and the feed per tooth is c.
[0056] The material removal rate is the volume of metal removed per unit time (s), which is a function of the spindle speed N, the number of milling cutter teeth n, the milling cutter radius R, the radial milling depth a, the axial milling depth b, and the feed per tooth c, which is completely determined by the milling parameters. Therefore, the milling material removal rate
[0057] Step 3: Multi-objective optimization calculation of process parameters. Based on the tool wear model and material removal rate model, with the maximum material removal rate and the maximum cutting time for the tool to reach the set wear band width threshold as the goal, the multi-objective optimization calculation of aluminum-based silicon carbide milling process parameters is realized, and the Pareto optimal solution set is obtained, such as Figure 4 shown.
[0058]
[0059] x=[n,f z ,a p ].
[0060] In the above model formula, the default radial cutting width is equal to the tool diameter D. Among them, the tool wear threshold is set in the initial file. The multi-objective optimization function is to maximize the material removal rate and the maximum cutting time for the tool to reach the proposed wear band width threshold.
[0061] Tool life and material removal rate are in a contradictory relationship, one increases while the other decreases. The Pareto diagram clearly shows the corresponding relationship between material removal rate and tool life. The diagram shows the specific values of tool life corresponding to the material removal rate. The material removal rate is quantified by cutting parameters, and the theory and practice are the same. For tool life, the accuracy of the model is verified in tool wear.
[0062] The above embodiments are only illustrative of the principles and effects of the present invention, as well as some embodiments of its application. For those skilled in the art, several modifications and improvements may be made without departing from the creative concept of the present invention, and all of these belong to the protection scope of the present invention.
Claims
1. A method for optimizing milling process parameters of medium- and high-volume aluminum-based silicon carbide materials, characterized in that: The steps include: Step 1: Model and calculate the tool material wear volume loss caused by two-body abrasive wear, three-body abrasive wear and adhesive wear respectively. Using the equivalent relationship between the total tool flank wear loss and the tool flank geometric loss, establish a tool flank wear rate analytical model for predicting the tool flank wear band width VB: During a full immersion milling cycle, the tool flank wear loss caused by two-body abrasive wear is expressed as: Where A1 is the average cross-sectional area of the grooves formed by the reinforcing particles in two-body abrasive wear, V c is the milling speed, C two-body The wear characteristics of ceramic particle reinforced metal matrix composites in two-body abrasive wear, wear characteristics C two-body The average size of the reinforced particles R 1aver , volume fraction f p , the probability of participating in two-body abrasive wear P two-body , the probability of debonding f debond The relationship between them is expressed as: two-body =c1(1-f debond )f p R 1aver P two-body , where c1 is the model coefficient, and the number of reinforcing particles involved in two-body abrasive wear in the wear zone of the tool flank is expressed as Where Np is the total number of reinforcement particles acting on the wear zone of the tool flank, f(R1) is the distribution function of the size of the reinforcement particles in the metal matrix, and R c Critical particle size for two-body abrasive wear enhancement; During a fully immersed milling cycle, the tool flank wear loss caused by the three-body abrasive wear enhancing particles is expressed as: Among them C three-body represents the wear characteristics of the reinforcing particles in three-body abrasive wear, K1 and i are model constants, p2 is the normal load applied to a single reinforcing particle in three-body abrasive wear, κ is the complementary angle of the reinforcing particle tip angle θ, N three-body is the number of reinforcing particles involved in three-body abrasive wear in the wear zone of the tool flank; During a fully immersed milling cycle, the tool flank wear loss caused by adhesive wear is expressed as Where P0 represents the probability of forming wear pits on the wear zone of the tool flank, n w V is the number of micro-welding points per unit area on the tool flank wear zone, w Tool volume loss caused by each micro weld that breaks in a weak area of tool material; During a fully immersed milling cycle, the tool flank wear loss caused by adhesive wear is expressed as The adhesive wear model coefficient is expressed as K adhesion =(P0h w f adhesion ) / (2b), the total tool back surface wear loss is the sum of the wear losses of two-body abrasive wear, three-body abrasive wear and adhesive wear △V wear =△V two-body +△V three-body +△V adhesion ; The wear rate of the tool back face wear zone is expressed as: Under given milling conditions, the tool flank wear rate is calculated according to the tool flank wear band width VB at different times, and the VB value at each subsequent moment can be calculated, where the model coefficient K two-body , K three-body , K adhesion and c are constant coefficients related to tool and workpiece materials; Step 2: When the radial cutting depth a≤milling cutter radius R, the cutting area Where the radial milling depth is a, the axial milling depth is b, and the feed per tooth is c; therefore, the milling material removal rate is Step 3: Based on the tool wear model and material removal rate model, the multi-objective optimization calculation of aluminum-based silicon carbide milling process parameters is realized with the goal of maximizing the material removal rate and maximizing the cutting time for the tool to reach the set wear band width threshold, and the Pareto optimal solution set is obtained: x=[n,f z ,a p ]。 2. The method for optimizing milling process parameters of medium- and high-volume aluminum-based silicon carbide materials according to claim 1 is characterized in that: In the step 1, the average cross-sectional area A1 of the grooves formed by the reinforcing particles in the two-body abrasive wear is approximately Where δ is the relative immersion depth δ = p1 / (2.9πR2Y). The reinforcing particles are pulled out from the workpiece surface under the action of the tool edge and captured in the interface between the workpiece material and the tool flank, causing three-body abrasive wear during the machining process.
3. The method for optimizing milling process parameters of medium- and high-volume aluminum-based silicon carbide materials according to claim 2, characterized in that: In the step 1, the wear characteristics of the enhanced particles in the three-body abrasive wear C three-body It is a function of the size and volume fraction of the reinforcing particles and the probability of debonding of the reinforcing particles, expressed as C three-body =c2f p f debond R 1aver , where c2 is the model coefficient.
4. The method for optimizing milling process parameters of medium- and high-volume aluminum-based silicon carbide materials according to claim 3 is characterized in that: The tool volume loss caused by each micro-welding in step 1 is V w =h w A w , where the average cross-sectional area of the micro-weld is A w Expressed as the roughness hardness H in the workpiece material w Function A w =(pf adhesion ) / (n w VxD w ).