CN117494540A - Cutting processing method for thin-wall arc-shaped part - Google Patents
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Abstract
The invention discloses a cutting processing method of a thin-wall arc-shaped part, and relates to the technical field of cutting processing of thin-wall arc-shaped parts; in order to ensure the processing efficiency, the processing precision is improved; comprising the following steps: initially establishing a cutting parameter optimization model; setting constraint conditions; obtaining a complete cutting parameter optimization model; experiment verification, namely calculating optimal cutting parameters by adopting a particle swarm optimization algorithm and using Matlab written codes; and processing the arc-shaped parts by adopting a cutting method based on the optimal cutting parameters. The invention can improve cutting deformation by preventive optimization of cutting parameters, reduce residual stress deformation and processing time after cutting, improve quality and efficiency, provide parameter selection standard for actual production and processing, and have important value.
Description
Technical Field
The invention relates to the technical field of cutting processing of thin-wall arc-shaped parts, in particular to a cutting processing method of a thin-wall arc-shaped part.
Background
In the aviation and aerospace industry, an arc-shaped part is one of widely used parts, however, the machining of the thin-wall arc-shaped structural part with complex characteristics is not easy, the thin-wall arc-shaped structural part is especially applied to the aviation and aerospace industry, the machining precision of the parts is required to be higher, the existing machining mode of the thin-wall arc-shaped part is mainly milling one by one, certain machining requirements can be met, the machining efficiency is poor, the machining efficiency is ensured by adopting a cutting machining mode, but the phenomenon of cutting deformation is easy to occur based on residual stress, and therefore, the control and adjustment of surface cutting parameters (cutting linear speed vs, axial feeding speed vw and cutting thickness ap) are particularly important.
Through retrieval, the patent with the Chinese patent application number of CN202210313775.9 discloses a processing method of an arc-shaped workpiece, a workpiece blank is fixed on a workbench of a first small numerical control processing center through a first clamping mechanism of the first small numerical control processing center, and a first cutter of the first small numerical control processing center is used for processing the outer contour of the arc-shaped workpiece to form an arc-shaped main body. The arc-shaped main body is fixed on a workbench of the second small numerical control machining center through a second clamping mechanism of the second small numerical control machining center, and a second cutter of the second small numerical control machining center is used for machining the arc-shaped groove. The processing method in the above patent has the following disadvantages: the parameter optimization is not carried out pertinently, the direct processing is difficult to ensure the processing precision, and the improvement is still needed.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provides a thin-wall arc-shaped part cutting processing method.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a cutting processing method of a thin-wall arc-shaped part comprises the following steps:
s1: initially establishing a cutting parameter optimization model;
s2: setting constraint conditions;
s3: obtaining a complete cutting parameter optimization model;
s4: experiment verification, namely calculating optimal cutting parameters by adopting a particle swarm optimization algorithm and using Matlab written codes;
s5: and processing the arc-shaped parts by adopting a cutting method based on the optimal cutting parameters.
Preferably: in the step S1, when the cutting parameters of the annular thin-wall part are optimized, according to the machining motion process, the machining time is obtained, and the machining time calculation formula is as follows:
Y 2 ＝L/Vw
wherein:
Y 2 the processing time is min;
l is the width of the part, and the unit is mm;
vw is the axial feed speed mm/s.
Further: in the step S1, according to the characteristics of optimization of the multi-objective processing parameters, a linear weighting method is adopted to change the multi-objective problem into a single-objective problem to solve, namely, the minimum value of the solution f (x):
minf(x)＝W 1 y 1 +W 2 y 2
wherein:
W 1 、W 2 weight of each object, W 1 、W 2 ∈(0，1)，W 1 +W 2 ＝1。
Further preferred is: in the step S1, dimensions of each target are unified, and normalization processing is performed on each target, where an expression is as follows:
minf(x)＝W 1 (y 1 -y 1min )/(y 1max -y 1min )+W 2 (y 2 -y 2min )/(y 2max -y 2min )。
as a preferred embodiment of the present invention: in the step S2, constraint conditions are set as follows:
(1) cutting linear velocity constraints; in order to ensure the usability of the numerical control equipment, the rotating speed of the main shaft is generally not allowed to exceed the upper limit value specified by a grinding wheel manufacturer in the processing process, but the processing efficiency is influenced by the too low speed, so that the range vs E (a, b) is reasonably selected;
(2) axial feed rate constraints; the axial feeding speed directly determines the processing time, the feeding speed is too high, the tooth surface vibration marks are easy to be generated due to insufficient machine tool rigidity, the processing quality is influenced, the processing time is too long and the production efficiency is influenced due to too small machine tool rigidity, and the range vw epsilon (c, d);
(3) cutting depth constraints; too large a cutting thickness will cause too large a cutting force, too small a cutting force will increase the number of machining times and thus increase the machining time, decreasing the efficiency, range ap e (e, f).
Further preferred as the present invention is: in the step S3, the above objective equation and constraint conditions are combined, and the complete cutting parameter optimization model is:
minf(x)＝W 1 (y 1 -y 1min )/(y 1max -y 1min )+W 2 (y 2 -y 2min )/(y 2max -y 2min )；
W 1 +W 2 ＝1；
a≤Vs≤b；
c≤Vw≤d，mm/s；
e≤ap≤f,μm；
wherein:
vs is the cutting line speed in m/s;
vw is the axial feed speed in mm/s;
ap is the cutting thickness in μm.
As still further aspects of the invention: in the step S4, taking typical GH4738 thin-wall annular part cutting as an example, the ranges of Vs, vw, ap are set according to the actual machining conditions:
and after the constraint condition is completed, programming a particle swarm algorithm code in Matlab programming software by utilizing the parameter optimization model.
Based on the scheme: in the step S4, solving a multi-objective optimization problem based on a particle swarm algorithm, and adopting the formula:
V i (t+1)＝ωV i (t)+c 1 r 1 (P i -x i (t))+c 2 r 2 (P b -x i (t))
x i (t+1)＝x i (t)+V i (t+1)
wherein:
V i is the particle velocity;
P i is the current optimal position of the particle;
P b the global current optimal position is the inertial weight of the particle;
c 1 、c 2 is a learning factor;
r 1 、r 2 is two mutually independent random numbers between 0 and 1;
sequentially setting the size of the group, the maximum iteration number, the learning factor and the inertia weight, and calculating to obtain an optimization result; setting a comparison group, and comparing an actual measurement result obtained by using the optimized parameters with the comparison group; the result is obtained.
Preferred on the basis of the foregoing scheme: in the step S5, the specific steps of processing the arc-shaped parts by adopting a cutting method are as follows:
s51: selecting forging blank pieces with proper specifications;
s52: machining the forging blank into a single annular piece through a numerical control lathe;
s53: clamping and fixing the annular piece through a clamp;
s54: cutting the annular piece into a plurality of arc-shaped pieces by adopting a linear cutting processing method;
s55: and (5) finishing to obtain the target part.
Further preferred on the basis of the foregoing scheme is: in the process of machining arc-shaped parts by adopting a cutting method, the parts are subjected to stable heat treatment after a rough machining link and before a finish machining link, so that the residual stress of rough machining of the parts is eliminated.
The beneficial effects of the invention are as follows:
1. the invention can improve cutting deformation by preventive optimization of cutting parameters, reduce residual stress deformation and processing time after cutting, improve quality and efficiency, provide parameter selection standard for actual production and processing, and have important value.
2. The invention establishes a preferred model of the cutting parameters: based on the prediction model of the deformation, a cutting parameter optimization model with the machining efficiency and the deformation as multiple targets is established; and a particle swarm optimization algorithm is adopted, matlab is used for writing codes to calculate optimal cutting parameters, and effects before and after optimization are displayed so as to obtain an optimization effect, control and improve processing quality better.
Drawings
Fig. 1 is a comparison chart of parameters before and after optimization of a thin-wall arc-shaped part cutting processing method provided by the invention.
Detailed Description
The technical scheme of the patent is further described in detail below with reference to the specific embodiments.
Example 1:
a cutting processing method of a thin-wall arc-shaped part comprises the following steps:
s1: initially establishing a cutting parameter optimization model;
s2: setting constraint conditions;
s3: obtaining a complete cutting parameter optimization model;
s4: experiment verification, namely calculating optimal cutting parameters by adopting a particle swarm optimization algorithm and using Matlab written codes;
s5: and processing the arc-shaped parts by adopting a cutting method based on the optimal cutting parameters.
In the step S1, when the cutting parameters of the annular thin-wall part are optimized, the machining efficiency should be improved as much as possible under the condition of ensuring the machining quality, specifically, according to the machining motion process, the machining time calculation formula is as follows:
Y 2 ＝L/Vw
wherein:
Y 2 the processing time is min;
l is the width of the part, and the unit is mm;
vw is the axial feed speed mm/s.
By preventive optimization of cutting parameters, cutting deformation can be improved, residual stress deformation and processing time after cutting can be reduced, and quality and efficiency are improved. The method provides a parameter selection standard for actual production and processing, and has important value.
In the step S1, according to the characteristics of optimization of the multi-objective processing parameters, a linear weighting method is adopted to change the multi-objective problem into a single-objective problem to solve, namely, the minimum value of the solution f (x):
minf(x)＝W 1 y 1 +W 2 y 2
wherein:
W 1 、W 2 weight of each object, W 1 、W 2 ∈(0，1)，W 1 +W 2 ＝1；
In order to unify the dimensions of each target, normalization processing is performed on each target, and the expression is as follows:
minf(x)＝W 1 (y 1 -y 1min )/(y 1max -y 1min )+W 2 (y 2 -y 2min )/(y 2max -y 2min )。
in the step S2, constraint conditions are set as follows:
(1) cutting linear velocity constraints; in order to ensure the usability of the numerical control equipment, the rotating speed of the main shaft is generally not allowed to exceed the upper limit value specified by a grinding wheel manufacturer in the processing process, but the processing efficiency is influenced by the too low speed, so that the range vs E (a, b) is reasonably selected;
(2) axial feed rate constraints; the axial feeding speed directly determines the processing time, the feeding speed is too high, the tooth surface vibration marks are easy to be generated due to insufficient machine tool rigidity, the processing quality is influenced, the processing time is too long and the production efficiency is influenced due to too small machine tool rigidity, and the range vw epsilon (c, d);
(3) cutting depth constraints; too large a cutting thickness will cause too large a cutting force, too small a cutting force will increase the number of machining times and thus increase the machining time, decreasing the efficiency, range ap e (e, f).
In the step S3, the target equation and the constraint condition are combined, and the complete cutting parameter optimization model is as follows:
minf(x)＝W 1 (y 1 -y 1min )/(y 1max -y 1min )+W 2 (y 2 -y 2min )/(y 2max -y 2min )；
W 1 +W 2 ＝1；
a≤Vs≤b；
c≤Vw≤d，mm/s；
e≤ap≤f,μm；
wherein:
vs is the cutting line speed in m/s;
vw is the axial feed speed in mm/s;
ap is the cutting thickness in μm.
In the step S4, taking typical GH4738 thin-wall annular part cutting as an example, the range is set according to the actual processing situation:
3≤Vs≤18m/s；4≤Vw≤15mm/s；2≤ap≤10μm
according to formula Y 2 The greater the axis speed, the smaller the machining time, and the higher the efficiency, therefore, comprehensively consider W 1 Set to 0.6, W2 set to 0.4; and after the constraint condition is completed, programming a particle swarm algorithm code in Matlab programming software by using a parameter optimization model of the upper section.
The particle swarm algorithm solves the multi-objective optimization problem by adopting the formula:
V i (t+1)＝ωV i (t)+c 1 r 1 (P i -x i (t))+c 2 r 2 (P b -x i (t))
x i (t+1)＝x i (t)+V i (t+1)
wherein:
V i is the particle velocity;
P i is the current optimal position of the particle;
P b for global current bestPosition, which is the inertial weight of the particle;
c 1 、c 2 is a learning factor;
r 1 、r 2 is two mutually independent random numbers between 0 and 1;
setting the population size as 300, setting the maximum iteration number as 500 and learning factor c 1 ＝c 2 =2, inertial weight ω=1, r 1 ＝r 2 =0.95, the calculation gives the optimal result: vs=3 m/s; vw=6.42 mm/s; ap=10 μm.
Setting a comparison group, and comparing an actual measurement result obtained by using the optimized parameters with the comparison group; the result is obtained: the processing time is reduced, the efficiency is improved, the deformation is reduced, and the deformation quality control is improved.
In the step S5, the specific steps of processing the arc-shaped parts by adopting a cutting method are as follows:
s51: selecting forging blank pieces with proper specifications;
s52: machining the forging blank into a single annular piece through a numerical control lathe;
s53: clamping and fixing the annular piece through a clamp;
s54: cutting the annular piece into a plurality of arc-shaped pieces by adopting a linear cutting processing method;
s55: and (5) finishing to obtain the target part.
In addition, in the process of processing the arc-shaped parts by adopting the cutting method, the parts are subjected to stable heat treatment after the rough machining link and before the finish machining link, so that the residual stress of rough machining of the parts is eliminated.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should make equivalent substitutions or modifications according to the technical scheme of the present invention and the inventive concept thereof, and should be covered by the scope of the present invention.
Claims (10)
1. The cutting processing method of the thin-wall arc-shaped part is characterized by comprising the following steps of:
s1: initially establishing a cutting parameter optimization model;
s2: setting constraint conditions;
s3: obtaining a complete cutting parameter optimization model;
s4: experiment verification, namely calculating optimal cutting parameters by adopting a particle swarm optimization algorithm and using Matlab written codes;
s5: and processing the arc-shaped parts by adopting a cutting method based on the optimal cutting parameters.
2. The method for cutting and machining a thin-wall arc-shaped part according to claim 1, wherein in the step S1, when the cutting parameters of the annular thin-wall part are optimized, the machining time is obtained according to the machining motion process, and the machining time calculation formula is as follows:
Y 2 ＝L/Vw
wherein:
Y 2 the processing time is min;
l is the width of the part, and the unit is mm;
vw is the axial feed speed mm/s.
3. The method for cutting and processing the thin-wall arc-shaped part according to claim 2, wherein in the step S1, according to the characteristics of optimization of the multi-objective processing technological parameters, a linear weighting method is adopted to change a multi-objective problem into a single-objective problem to solve, namely, to solve the minimum value of f (x):
minf(x)＝W 1 y 1 +W 2 y 2
wherein:
W 1 、W 2 weight of each object, W 1 、W 2 ∈(0，1)，W 1 +W 2 ＝1。
4. The method for cutting and processing the thin-wall arc-shaped part according to claim 3, wherein in the step S1, dimensions of each target are unified, normalization processing is performed on each target, and the expression is as follows:
minf(x)＝W 1 (y 1 -y 1min )/(y 1max -y 1min )+W 2 (y 2 -y 2min )/(y 2max -y 2min )。
5. the method for cutting a thin-walled arc-shaped part according to claim 4, wherein in the step S2, constraint conditions are set as follows:
(1) cutting linear velocity constraints; in order to ensure the usability of the numerical control equipment, the rotating speed of the main shaft is generally not allowed to exceed the upper limit value specified by a grinding wheel manufacturer in the processing process, but the processing efficiency is influenced by the too low speed, so that the range vs E (a, b) is reasonably selected;
(2) axial feed rate constraints; the axial feeding speed directly determines the processing time, the feeding speed is too high, the tooth surface vibration marks are easy to be generated due to insufficient machine tool rigidity, the processing quality is influenced, the processing time is too long and the production efficiency is influenced due to too small machine tool rigidity, and the range vw epsilon (c, d);
(3) cutting depth constraints; too large a cutting thickness will cause too large a cutting force, too small a cutting force will increase the number of machining times and thus increase the machining time, decreasing the efficiency, range ap e (e, f).
6. The method according to claim 5, wherein in the step S3, the target equation and the constraint condition are combined, and the complete cutting parameter optimization model is:
minf(x)＝W 1 (y 1 -y 1min )/(y 1max -y 1min )+W 2 (y 2 -y 2min )/(y 2max -y 2min )；
W 1 +W 2 ＝1；
a≤Vs≤b；
c≤Vw≤d，mm/s；
e≤ap≤f,μm；
wherein:
vs is the cutting line speed in m/s;
vw is the axial feed speed in mm/s;
ap is the cutting thickness in μm.
7. The method of claim 6, wherein in the step S4, taking typical GH4738 thin-wall annular part cutting as an example, the ranges of Vs, vw, ap are set according to actual machining conditions:
and after the constraint condition is completed, programming a particle swarm algorithm code in Matlab programming software by utilizing the parameter optimization model.
8. The method for cutting and processing the thin-wall arc-shaped part according to claim 7, wherein in the step S4, the multi-objective optimization problem is solved based on a particle swarm algorithm, and the formula is adopted:
V i (t+1)＝ωV i (t)+c 1 r 1 (P i -x i (t))+c 2 r 2 (P b -x i (t))
x i (t+1)＝x i (t)+V i (t+1)
wherein:
V i is the particle velocity;
P i is the current optimal position of the particle;
P b the global current optimal position is the inertial weight of the particle;
c 1 、c 2 is a learning factor;
r 1 、r 2 is two mutually independent random numbers between 0 and 1;
sequentially setting the size of the group, the maximum iteration number, the learning factor and the inertia weight, and calculating to obtain an optimization result; setting a comparison group, and comparing an actual measurement result obtained by using the optimized parameters with the comparison group; the result is obtained.
9. The method for cutting and processing the thin-wall arc-shaped part according to claim 8, wherein in the step S5, the specific steps of processing the arc-shaped part by adopting a cutting method are as follows:
s51: selecting forging blank pieces with proper specifications;
s52: machining the forging blank into a single annular piece through a numerical control lathe;
s53: clamping and fixing the annular piece through a clamp;
s54: cutting the annular piece into a plurality of arc-shaped pieces by adopting a linear cutting processing method;
s55: and (5) finishing to obtain the target part.
10. The method for cutting and processing the arc-shaped thin-wall part according to claim 9, wherein in the process of processing the arc-shaped part by adopting a cutting method, the part is subjected to stable heat treatment after a rough machining link and before a finish machining link, so that the residual stress of rough machining of the part is eliminated.
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