CN107480318B - Method for optimizing cutting process of hard and brittle material thin-wall part - Google Patents
Method for optimizing cutting process of hard and brittle material thin-wall part Download PDFInfo
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Abstract
The invention discloses a method for optimizing a cutting process of a thin-wall part made of a hard and brittle material, which comprises the following steps: aiming at the selected hard and brittle material thin-wall part, establishing a parameterized thin-wall structure model of the part, and setting a finite element grid for the model; loading set constraint conditions for the thin-wall structure model; optimizing each procedure in the cutting process through algorithm optimization to obtain a maximum stress value corresponding to the procedure; and obtaining the machining parameters of the current working procedure by combining a cutting force model according to the maximum stress value, the allowable material strength value and the safety coefficient. Compared with the prior art, the method has the following advantages; under the conditions of meeting the machining working conditions of a machine tool, the geometric parameters of the thin-wall part and the like, the maximum stress of the thin-wall part can be quickly obtained by using an algorithm; the maximum stress value and the cutting model are combined to specify the processing parameters to the maximum extent, so that the processing efficiency is improved; and the residual strength of the thin-wall part is utilized in the machining process, so that the reliability of the machining process is ensured.
Description
Technical Field
The invention relates to a part machining process optimization method based on finite elements and an optimization algorithm, in particular to a hard and brittle material thin-wall part machining process optimization method.
Background
With the development of production and science and technology, the increasing progress of microelectronics, photoelectronics, sensor technology and material technology, the application of hard and brittle materials such as hard alloy, quenched steel, optical glass, ceramics, photoelectric crystal, granite and the like in industry is gradually becoming common due to the excellent properties such as strong wear resistance, high hardness and the like. Meanwhile, the thin-wall structure of the hard and brittle material is widely applied to high-performance aerospace products and electromechanical products due to the advantages of light weight, material saving, compact structure and the like. The thin-wall part is easy to deform and yield, break and damage and other problems in the manufacturing process due to low rigidity, and the light-weight processing of the thin-wall part made of hard and brittle materials is easy to form micro-crack expansion to cause structural damage due to high brittleness of the materials, so that stricter requirements are provided for the processing technology.
The low plasticity, brittle fracture, microcracking and high cost of hard and brittle materials place high demands on their processing methods. At present, the machining of hard and brittle material thin-wall parts is mainly based on grinding machining technology. In general, in machining of hard and brittle thin-walled parts, a craftsman often selects machining process parameters such as cutting speed, feed amount, cutting depth, cutting width and the like of a machining system according to experience or a manual. For safety reasons, cutting parameters which are conservative are usually selected and kept unchanged, and the selection of the cutting parameters usually needs a large amount of process tests and tests, which affect the processing efficiency, and especially for hard and brittle materials, the hard and brittle materials are broken when not processed to a specified size, and the manufacturing period is seriously prolonged. Therefore, the optimization of the processing technology is directly related to the reasonable use of the cutter and the machine tool, and has important effects on improving the productivity and reducing the production cost. In order to solve the problem, various processing technology optimization methods are proposed in succession. The conventional and existing machining optimization methods are mainly embodied in the selection of machining methods and the specific optimization of machining parameters, the selection of machining parameters does not fully utilize residual materials, and the relationship between the machining parameters and residual strength and cutting force cannot be established. Therefore, the selection of the processing parameters is often blind, the processing efficiency is influenced when the processing parameters are too small, the risk of material fracture and damage is caused when the processing parameters are too large, and the achieved efficiency and precision are not ideal.
Therefore, the research of the optimization of the machining process is carried out, the relation between the machining parameters and the residual strength and the cutting force is established, the proper machining process is worked out by utilizing finite element analysis, the proper and larger machining parameters are selected, the residual materials can be fully utilized in each step, a structure with higher bearing capacity is formed, the maximum stress of the part in the machining process is minimized, the machining efficiency is further improved, and the thin-wall part is prevented from being broken and damaged.
Disclosure of Invention
Aiming at the problems, the invention provides an optimization method of a cutting process of a thin-wall part made of a hard and brittle material, which comprises the following steps:
establishing a parameterized thin-wall structure model of the selected hard and brittle material thin-wall part, and setting a finite element grid for the model;
-loading set constraints for said thin-wall structural model; optimizing each procedure in the cutting process through algorithm optimization to obtain a maximum stress value corresponding to the procedure;
and obtaining the machining parameters of the current working procedure by combining a cutting force model according to the maximum stress value, the allowable material strength value and the safety coefficient.
In a preferred embodiment, the shape of the part is determined by a limited number of design variables DiAnd i is 1 and 2 … … m, and constitutes a parameterized thin-wall structure model with a finite element mesh.
As a preferred embodiment, the loading process of the thin-wall structure model is as follows:
setting the system load value to F, usingGenetic algorithmOptimizing;
-calculating the design variable of this step as the coordinates (x, y) of the loading position, the output variable being the maximum stress σ of the finite element modelmax2，σmax2Varies with the coordinate (x, y) of the loading position;
maximum stress sigma with optimization target of finite element modelmax2Maximum, solving for the corresponding maximum stress σmax1I.e. sigmamax1＝max(σmax2)。
Further, the optimization process for each procedure in the cutting process by optimization is as follows:
-setting the optimization target to the maximum stress value σmax1Min σ minmax1(ii) a The constraint condition is the setting corresponding to the procedure that the model volume is less than or equal toThe value:
s.t.Vk≤[V]k(k＝1,2…l)
wherein: vkRepresenting the volume of the workpiece after the k procedure; [ V ]]kRepresenting the theoretical maximum volume of the workpiece after the k procedure; t represents half the final thickness of the workpiece; di kRepresents half of the thickness of the workpiece at the position i after the k procedure; di k-1Represents half the thickness of the workpiece at position i after the (k-1) th process, TsRepresents half the thickness of the blank;
setting the shape of the workpiece after machining not to be coincident with the shape of the workpiece before machining, wherein the design variable is a model size variable Di(i＝1,2…m)
Obtaining an optimized model dimension variable through optimization calculation, and determining the shape of the thin-wall part processed by the working procedure;
and (3) combining the shape of the thin-wall part optimized in the previous process to obtain the specific shape of the material removed (cut) in the previous process.
Further, the cutting force model used in the cutting process:
Fg＝fg(A,fu,N,ap,ae,f)
due to the load value F and the maximum stress value sigmamaxIn a linear relationship, reference is made to the grinding force model and the allowable strength [ sigma ] of the materialmax]An appropriate safety factor R is specified, and the maximum allowable grinding force of the process is obtained through the following formula;
from the intensity value [ sigma ] of the material]Maximum stress value σmaxAnd a safety factor R, calculating the cutting force Fg。
In a preferred embodiment, the finite element mesh is a hexagonal mesh.
The optimization algorithm is
A gradient optimization algorithm comprising: modifying a Feasible direction Method of reactive orientations, a Generalized Reduced Gradient Method, a sequence Quadratic Programming, a multifunctional Optimization System technology, a Mixed Integer Quadratic Programming, and a Modified-Integer Quadratic Programming;
the direct search method comprises the following steps: Hooke-Jivis Direct Search Method Hooke-Jeeves Direct Search Method, Downhill Simplex Method;
and (3) global optimization algorithm: Multi-Island Genetic Algorithm, evolution Optimization, Adaptive Simulated Annealing, Particle Swarm Optimization).
By adopting the technical scheme, the method for optimizing the machining process of the thin-wall part made of the hard and brittle material, disclosed by the invention, has the following advantages compared with the prior art; 1. under the conditions of meeting the machining working conditions of a machine tool, the geometric parameters of the thin-wall part and the like, the maximum stress of the thin-wall part can be quickly obtained by using an algorithm; 2. the maximum stress value and the cutting model are combined to specify the processing parameters in the maximum range, so that the processing efficiency is greatly improved; 3. in the machining process, the residual strength of the thin-wall part is fully utilized, and the reliability of the machining process is ensured.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flow chart of the optimization steps of the machining process of the thin-wall part made of the hard and brittle material provided by the embodiment of the invention.
Fig. 2 is a flow chart of optimization of a single-pass grinding process processing technology of a thin-wall part made of a hard and brittle material according to an embodiment of the invention.
FIG. 3 is a schematic view of a thin-walled part specifically machined according to an embodiment of the present invention.
Fig. 4 is a schematic view of the cross-sectional shape of a workpiece after each processing step in conventional processing.
Fig. 5 is a schematic cross-sectional shape of the thin-wall model created in the example of the present invention.
Fig. 6 is a schematic shape diagram of a thin-walled part corresponding to a process obtained based on optimization of a machining process according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the following describes the technical solutions of the embodiments of the present invention clearly and completely with reference to the accompanying drawings in the embodiments of the present invention:
as shown in fig. 1-6:
the method takes the grinding process of the RB-SiC thin-wall part as a research object. The embodiment of the invention is described with reference to fig. 1, and the method for optimizing the machining process of the thin-wall part made of the hard and brittle material mainly comprises the following steps:
step one, determining the shapes of a blank and a final workpiece.
And step two, determining the working procedures of part machining, and determining 30% of the volume of the remaining removed material removed each time, thereby determining the remaining volume of each working procedure. The same processing parameters are adopted in the single process, and the processing parameters of each process are optimized.
And step three, optimizing according to the sizes of the workpieces in different procedures, and determining the optimal shape of the workpiece.
Step four, according to the load F and the maximum stress sigma of different working proceduresmaxDetermining the processing parameters of the process: in the present embodiment, the spindle speed n, the feed speed f, the axial cutting depth h and the radial cutting depth a are preferredpMachining parameters of (1).
The main steps for optimizing the processing technology of the single processing procedure based on a certain algorithm in the preprocessing are as follows, as shown in the attached figure 2:
step one, establishing a parameterized thin-wall part model by taking the machining process of the RB-SiC thin-wall part as a research object.
And step two, using ANSYS finite element analysis software to assign material attributes to the model, dividing grids, and adding constraint conditions.
Loading the finite element model with the load value of F, performing optimization calculation by using certain optimization algorithm (such as genetic algorithm) to obtain the maximum stress sigmamax1。
Step four, design variables of the processing technology optimization: the model size variables are: d1,D2…DmThe objective function is: min σmax1。
Step five, as a preferred implementation manner, the genetic algorithm (or other optimization algorithms) is adopted in the embodiment to optimize the thin-wall shape after the single process, and the corresponding maximum stress value σ is obtainedmax。
Many algorithms can accomplish the optimization task of the present invention, and as a preferred embodiment, the following optimization algorithms can be selected, such as:
a gradient optimization algorithm comprising: modifying a Feasible direction Method of reactive orientations, a Generalized Reduced Gradient Method, a sequence Quadratic Programming, a multifunctional Optimization System technology, a Mixed Integer Quadratic Programming, and a Modified-Integer Quadratic Programming;
the direct search method comprises the following steps: Hooke-Jivis Direct Search Method Hooke-Jeeves Direct Search Method, Downhill Simplex Method;
and (3) global optimization algorithm: Multi-Island Genetic Algorithm, evolution Optimization, Adaptive Simulated Annealing, Particle Swarm Optimization).
Further, the specific process of process optimization: passing the workpiece shape through a finite number of design variables (D) based on the workpiece shapei(i-1, 2 … … m)) represents DiForming a parameterized thin-wall part model for model dimension variables:
S＝dS(D1,D2…Dm)
and then, importing the model into finite element analysis software, specifying material properties and dividing grids, and adding appropriate constraint conditions for the model according to working conditions.
Loading the finite element model with the load value of F, and performing optimization calculation by using a genetic algorithm (the design variable of the step is the coordinate (x, y) of the loading position, and the output variable is the maximum stress sigma of the finite element modelmax2，σmax2Varying with the coordinates (x, y) of the loading position, the optimization objective being the maximum stress σ of the finite element modelmax2Max), the corresponding maximum stress σ is solvedmax1I.e. sigmamax1＝max(σmax2)。
Performing optimization calculation by using genetic algorithm, wherein the optimization target is maximum stress value sigmamax1Minimum, the constraint condition is that the model volume is less than or equal to a certain value (relevant to the process), the shape of the workpiece after processing is not coincident with the shape of the workpiece before processing, and the design variable is the model size variable Di(i is 1,2 … m), finally, obtaining optimized model dimension variables through optimization calculation, and determining the shape of the thin-wall part after the processing of the working procedure. And (3) combining the shape of the thin-wall part optimized in the previous process to obtain the specific shape of the material removed in the process:
optimizing the target: min σmax1
Constraint conditions are as follows: s.t.Vk≤[V]k(k＝1,2…l)
Wherein: vkRepresenting the volume of the workpiece after the k procedure; [ V ]]k represents the theoretical maximum volume of the workpiece after the kth procedure; t represents half the final thickness of the workpiece; di kRepresents half of the thickness of the workpiece at the position i after the k procedure; di k-1Represents half the thickness of the workpiece at position i after the (k-1) th process, TsRepresenting half the thickness of the blank.
After optimization, the maximum stress value sigma of the procedure can be obtainedmax. Due to the load value F and the maximum stress value sigmamaxIn a linear relationship, reference is made to the grinding force model and the allowable strength [ sigma ] of the materialmax]And a proper safety factor R is specified, so that the maximum grinding force which can be adopted in the process can be obtained, and the formula is shown as follows:
obtaining the maximum grinding force FgAnd then, reversely solving all processing parameters according to a grinding force model of the ultrasonic auxiliary grinding, such as: and (3) specifying the ultrasonic amplitude A, the ultrasonic frequency fu, the main shaft rotating speed N, the radial cutting depth ap and the axial cutting depth ae according to experience to obtain the feed f, and finishing the optimization of the processing strategy of the working procedure:
Fg＝fg(A，fu，N，ap，ae，f)
examples
As shown in the attached figure 3, the RB-SiC thin-wall part to be processed and optimized has the length of 40mm, the height of 40mm, the initial thickness of 7.5mm and the processed thickness of 1.5 mm.
Firstly, planning the machining process, namely, planning the machining process by adopting a method of removing 1/3 of the residual removed volume of the material in each process, wherein the total number of the processes is divided into 7. If the traditional processing strategy is adopted, namely the shape of the workpiece after each procedure is a cuboid, the processing is carried out in the sequence from top to bottom, the processing parameters of each procedure are the same, the volume and the wall thickness of the workpiece after each procedure are shown in the following table, the cross section shape of the workpiece is shown in the attached figure 4, and the solid line shows the shape of the workpiece after the procedure.
7. And optimizing the process aiming at each procedure. The optimization step of the first procedure is that firstly, in the inventor, a parameterized thin-wall part model is established, in order to simplify the calculation, a thin-wall model with the same longitudinal section is established, the section shape is shown as the attached figure 5, the left and right lines are symmetrical and are spline curves determined by 5 points, the 5 points are uniformly distributed along the longitudinal direction, the space is 10mm, and the distances from the center line of the model are D respectively1、D2、D3、D4、D5(model dimension variable), the 5 variable parameters are design variables, the shape of the model will change by changing the values of the 5 parameters, and then the parameterized model is imported into ANSYS Workbench.
In step 1, the initial value of each design variable was set to 2.75. Then, selecting hexahedron units for grid division. And respectively applying fixed constraints to the bottom and the left and right sides of the model according to the working conditions. Loading the finite element model with the load value of F, and performing optimization calculation by using a sequence quadratic programming method (NLPQL) (the design variable of the step is the coordinate (x, y) of the loading position, and the output variable is the maximum stress sigma of the finite element modelmax2，σmax2With coordinates (x, y) of loading positionChange by change, the optimization target being the maximum stress sigma of the finite element modelmax2Max), the corresponding maximum stress σ is solvedmax1I.e. sigmamax1＝max(σmax2)，σmax1Is the output parameter of the step. Namely:
optimizing the target: max sigmamax2(x,y)
Constraint conditions are as follows: s.t.0-x 40, 0-y 40
Optimization calculation is carried out by using Design optimization integrated in ANSYS Workbench, and the optimization target is the maximum stress value sigmamax1Minimum, with the constraint that the model volume is 8800mm or less3The design variable being a model dimension variable D1、D2、D3、D4、D5The optimization method adopts a genetic algorithm, and is detailed in table 2. Namely:
optimizing the target: min σmax1(D1,D2,D3,D4,D5)
Constraint conditions are as follows: s.t.V1≤8800；
Optimization calculations are performed next. Finally, optimized D can be obtained1～D5And corresponding maximum stress sigmamaxThe value of (c). As shown in the table. Compared with the maximum stress value before optimization, the maximum stress value after optimization is obviously reduced, and the amplitude is reduced by 34.65%. Similar optimization of the machining process can be carried out on the other 5 working procedures, and the workpiece after the 7 th working procedure is fixed in shape, so that the optimization is not needed. The following table shows the maximum stress values for the output before and after optimization.
The shape of the workpiece after each process is optimized is shown in figure 6.
The method comprises the following steps of 1, determining machining parameters by combining a grinding force model according to the optimized maximum stress value, specifying an ultrasonic amplitude A, an ultrasonic frequency fu, a spindle rotating speed N, a radial cutting depth ap and an axial cutting depth ae to obtain a feed f, further calculating a material removal rate MRR (total material removal rate) ap ae f, taking out a volume V according to materials of each procedure, and calculating machining time t V/MRR. The results are shown in the table. .
The processing is carried out according to the traditional processing technology, namely, only simple procedure planning is carried out, and the processing time is shown in the table 5:
comparing the machining time of the embodiment with the machining time of common machining, the machining efficiency can be improved by 219% by the machining process optimization method provided by the invention on the premise of ensuring safe machining of the RB-SiC thin-walled workpiece, and the advantages of the machining process optimization method are verified.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (4)
1. A method for optimizing a cutting process of a hard and brittle material thin-wall part is characterized by comprising the following steps:
establishing a parameterized thin-wall structure model of the selected hard and brittle material thin-wall part, and setting a finite element grid for the model;
-loading set constraints for said thin-wall structural model; optimizing each procedure in the cutting process by algorithm optimization to obtain the optimal workpiece shape and the maximum stress value corresponding to the procedure;
obtaining the machining parameters of the current working procedure by combining a cutting force model according to the maximum stress value, the allowable material strength value and the safety coefficient;
the thin-wall structure model is loaded in the following process:
setting a system load value as F, and performing optimization calculation by using an optimization algorithm;
-calculating the design variable of this step as the coordinates (x, y) of the loading position, the output variable being the maximum stress σ of the finite element modelmax2，σmax2Varies with the coordinate (x, y) of the loading position;
maximum stress sigma with optimization target of finite element modelmax2Maximum, solving for the corresponding maximum stress σmax1I.e. sigmamax1＝max(σmax2)；
Cutting force model used in the cutting process:
Fg＝gg(A,fu,N,ap,ae,f)；
wherein A is ultrasonic amplitude, fu is ultrasonic frequency, N is main shaft rotation speed, ap is radial cutting depth, ae is axial cutting depth, and FgF is the feed for the maximum grinding force;
due to the load value F and the maximum stress value sigmamaxIn a linear relationship, reference is made to the grinding force model and the allowable strength [ sigma ] of the materialmax]Specifying a safety coefficient R to be 3-5, and calculating the maximum allowable grinding force of the process according to the following formula;
material made of woodIntensity value [ sigma ] of material]Maximum stress value σmaxAnd a safety factor R, calculating the cutting force Fg；
The optimization algorithm is
A gradient optimization algorithm comprising: modifying a Feasible direction Method of reactive orientations, a Generalized Reduced Gradient Method, a sequence Quadratic Programming, a multifunctional Optimization System technology, a Mixed Integer Quadratic Programming, and a Modified-Integer Quadratic Programming;
the direct search method comprises the following steps: Hooke-Jivis Direct Search Method Hooke-Jeeves Direct Search Method, Downhill Simplex Method;
and (3) global optimization algorithm: the Multi-Island Genetic Algorithm, evolution Optimization, Adaptive Simulated Annealing, Particle Swarm Optimization, Multi-Island Genetic Algorithm, evolution Optimization, Adaptive Simulated Annealing, Particle Swarm Optimization, and the like.
2. The method for optimizing the cutting process of the thin-walled part made of hard and brittle material according to claim 1, wherein the shape of the part is determined by a finite number of design variables DiAnd i is 1 and 2 … … m, and constitutes a parameterized thin-wall structure model with a finite element mesh.
3. The method for optimizing the cutting machining process of the thin-wall part made of the hard and brittle material according to claim 1, wherein the optimization of each procedure in the cutting machining process is as follows:
-setting the optimization target to the maximum stress value σmax1Min σ minmax1(ii) a The constraint condition is that the model volume is less than or equal to a set value corresponding to the working procedure:
s.t.Vk≤[V]k，k＝1,2…l
wherein: vkRepresenting the volume of the workpiece after the k procedure; [ V ]]kRepresenting the theoretical maximum volume of the workpiece after the k procedure; t represents half the final thickness of the workpiece; di kRepresents half of the thickness of the workpiece at the position i after the k procedure; di k-1Represents half the thickness of the workpiece at position i after the (k-1) th process, TsRepresents half the thickness of the blank;
setting the shape of the workpiece after machining not to be coincident with the shape of the workpiece before machining, wherein the design variable is a model size variable DiWherein i is 1,2 … m;
obtaining an optimized model dimension variable through optimization calculation, and determining the shape of the workpiece processed by the working procedure;
and combining the shape of the thin-wall part optimized in the previous process to obtain the specific shape of the material removed, namely the cut material, in the previous process.
4. The method for optimizing the cutting process of the thin-wall part made of the hard and brittle material as claimed in claim 1, wherein the finite element mesh is a hexagonal mesh.
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