CN110245381B - Design and optimization method of special horizontal drilling machine oblique column for steering knuckle bearing seat - Google Patents

Design and optimization method of special horizontal drilling machine oblique column for steering knuckle bearing seat Download PDF

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CN110245381B
CN110245381B CN201910387027.3A CN201910387027A CN110245381B CN 110245381 B CN110245381 B CN 110245381B CN 201910387027 A CN201910387027 A CN 201910387027A CN 110245381 B CN110245381 B CN 110245381B
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王亚辉
史玲
邢少群
张慧鹏
张财
王琪
刘叶
吴金妹
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North China University of Water Resources and Electric Power
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Abstract

The invention discloses a design and optimization method of a special horizontal drilling machine inclined upright column for a steering knuckle bearing pedestal, which aims at experience dependence and inefficiency of traditional machine tool design and combines strong finite element analysis capability of Ansys Workbench to provide a multi-objective optimization method combining a response surface and a genetic algorithm.

Description

Design and optimization method of special horizontal drilling machine oblique column for steering knuckle bearing seat
Technical Field
The invention relates to the field of machine tool design, in particular to a design and optimization method of a special horizontal drilling machine inclined upright column for a knuckle bearing seat based on Ansys Workbench.
Background
With the continuous development of modern design theory and computers, the machine manufacturing industry puts higher demands on the precision, efficiency, reliability and the like of machine tools. The traditional design method relying on experience and analogy has the problems of long design period and the like, and cannot respond to market demands in time.
The column is the core part of the machine tool, bears the motion of the main shaft and the cutter, and the precision of the processed parts is directly influenced by the tiny vibration or deformation of the column, so that the dynamic analysis of the column is necessary to improve the natural frequency and prevent resonance. At present, an effective optimization method for the mode of the inclined upright column of the machine tool is also lacked.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a design and optimization method of a special horizontal drilling machine oblique column for a steering knuckle bearing block, wherein Solidworks software is used for modeling the oblique column of a machine tool, then the oblique column is led into an ANSYS Workbench platform for kinetic modal analysis, five parameters of the length, width, height, wall thickness and plate rib thickness of the column are used as design variables, the mass and first-order natural frequency are used as optimized output variables, and the multi-objective genetic algorithm is used for improving the natural frequency and reducing the quality of the oblique column.
In order to achieve the purpose, the invention adopts the following technical scheme:
a design and optimization method of a special horizontal drilling machine inclined column for a steering knuckle bearing seat comprises the following steps:
s1, modeling a machine tool inclined upright column by using Solidworks software;
s2, importing the machine tool inclined column model obtained in the step S1 into an ANSYS Workbench platform;
s3, grid division: the size of the grid is selected to be 30mm; setting the grid correlation to be 100, and selecting the grid type to be a regular tetrahedron unit;
s4, performing dynamic modal analysis on the oblique stand column model of the machine tool, and selecting and calculating the first 6-order natural frequency;
s5, taking the length, width, height, wall thickness and plate rib thickness of the inclined upright column as design variables, generating a test design point by adopting a central composite test design method, and solving the test design point in an ANSYS Workbench platform; the value of each design variable is set according to +/-10%;
s6, constructing a response surface model according to the experimental design points obtained by the solution in the step S5;
and S7, iterating the response surface model by using a genetic algorithm to obtain a Pareto optimal solution set, and selecting a solution meeting the requirement from the Pareto optimal solution set as a candidate point.
Further, in step S2, an ANSYS Workbench platform is directly opened from the Solidworks software.
Further, in step S2, after the oblique column model of the machine tool is introduced, a simplification process is performed to remove detailed features.
Further, in step S4, a fixed constraint is applied to the bottom surface of the diagonal pillar during the kinetic modal analysis.
Further, in step S7, a response surface model is constructed by using a standard second-order response surface algorithm.
The invention has the beneficial effects that: the invention provides a multi-objective optimization method combining a response surface and a genetic algorithm aiming at experience dependence and inefficiency of traditional machine tool design and combining strong finite element analysis capability of Ansys Workbench.
Drawings
FIG. 1 is a schematic modeling diagram of step S1 in an embodiment of the present invention;
FIG. 2 is a mesh partition model obtained in step S3 according to an embodiment of the present invention;
FIG. 3 is a graph of the mode shape obtained in step S4 according to the embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating the fitting effect obtained in step S5 according to the embodiment of the present invention;
FIG. 5 is a graph illustrating the local sensitivity obtained in step S5 according to an embodiment of the present invention;
FIG. 6 is a plot of the wall thickness and the first-order natural frequency response obtained in step S5 according to an embodiment of the present invention;
FIG. 7 is a graph of the first order natural frequency response obtained in step S5 according to an embodiment of the present invention;
fig. 8 is a schematic diagram of the Pareto solution set obtained in step S7 in the embodiment of the present invention.
Detailed Description
The present invention will be further described with reference to the accompanying drawings, and it should be noted that the present embodiment is based on the technical scheme, and a detailed implementation manner and a specific operation process are provided, but the protection scope of the present invention is not limited to the present embodiment.
The embodiment provides a method for designing and optimizing a special horizontal drilling machine inclined column for a steering knuckle bearing seat, which comprises the following steps of:
s1, modeling a machine tool inclined upright column by using Solidworks software;
s2, importing the machine tool inclined column model obtained in the step S1 into an ANSYS Workbench platform;
in order to ensure that the model built by the Solidworks software and the ANSYS Workbench platform build a bidirectional parameterized link without data loss, the ANSYS Workbench platform is directly opened from the Solidworks software in the embodiment.
Further, the details of the model such as the bolt hole, the chamfer, the small diameter hole and the like have little influence on the calculation result, but a large amount of calculation time of the computer is increased, so in order to improve the efficiency, in the present embodiment, the simplified processing is performed after the oblique stand column model of the machine tool is introduced, and the detail feature is removed.
In this example, gray cast iron was selected as the material in Engineering Data of ANSYS Workbench, and the density ρ =7.2 × 10 3 kg/m 3 Young's modulus E =110GPa, poisson ratio v =0.28.
S3, grid division;
in the present embodiment, the mesh size is selected to be 30mm; the grid correlation is set to 100 and the grid type is selected to be regular tetrahedral cells.
The mesh division is an important step in finite element analysis, the size of the mesh directly determines the calculation accuracy, the mesh size value should be small to ensure that the result has higher accuracy, but the calculation time of the computer is greatly increased at the same time, in order to balance the mesh quality and the calculation time, trial mesh division is firstly performed in the embodiment, and the test result is shown in table 1.
TABLE 1
Figure BDA0002055179770000051
As can be seen from table 1, the first-order natural frequency changes little, but the grid quality changes greatly, so after comprehensive analysis, the size of the grid in this embodiment is selected to be 30mm, the grid correlation is set to be 100, and the grid type is selected to be a regular tetrahedron unit. Finally, 82439 nodes and 42523 units are generated. In this embodiment, a mesh division effect diagram is shown in fig. 2.
S4, performing dynamic modal analysis on the oblique stand column model of the machine tool, and selecting and calculating the first 6-order natural frequency;
because the bottom surface of the inclined upright post is connected with a machine tool through a bolt in actual work, in order to simulate the real working condition of the upright post, fixed constraint is applied to the bottom surface of the inclined upright post during analysis.
The mode shape diagram generated by the calculation result in this embodiment is shown in fig. 3, where (a) - (f) are mode shape diagrams of the first-order mode shape to the sixth-order mode shape, respectively.
In the embodiment, the rotating speed of the main shaft of the machine tool is set to be 1500r/min, the number of teeth of the milling cutter is generally 2 and 4, so that the machine tool can generate 100Hz exciting force during working, and resonance is likely to be generated when the frequency of the inclined vertical column is lower than the exciting force, so that the machining precision of parts is influenced. As can be seen from table 1, the first order natural frequency of the oblique posts must be increased.
S5, taking the length, width, height, wall thickness and plate rib thickness of the inclined upright column as design variables, generating a test design point by adopting a central composite test design method, and solving the test design point in an ANSYS Workbench platform;
the response surface method is a method of predicting a response value of a non-test sample point by testing a generated experimental design point by using an experimental design theory, generating a response surface model by using the experimental design point, and continuing the response surface model. The larger the number of trial design points, the closer the fitted model is to the real model, but too many design points will result in a significant increase in computation time. The selection of a scientific experimental design method to generate experimental design points can shorten a large amount of calculation time under the condition of sacrificing certain precision.
In this example, a Central Composite Design method (Central Composite Design) was used. At two levels, n factors, the experimental design point is centered at 1, 2n axial directions and 2 n-ξ The individual cause constitutes. Experimental design point calculations are shown in table 2.ξ is 1 when the number of design variables is 5, so 1+10+16=27 experimental design points will be generated in this embodiment.
TABLE 2
Figure BDA0002055179770000071
The value range of the design variable can be set and adjusted in a self-defined way according to the actual situation. In the embodiment, the values of the design variables are set to ± 10%, and the value ranges of the design variables obtained after calculation are shown in table 3.
TABLE 3
Figure BDA0002055179770000072
The experimental design points were solved in ANSYS Workbench, and the results obtained after updating are shown in table 4. Wherein P6 is the mass of the inclined vertical column in kilogram; p7 is the first order natural frequency of the oblique column in Hertz.
TABLE 4
Name(s) P1 P2 P3 P4 P5 P6 P7
1 20 33 850 1750 1550 1767.886 98.90278
2 18 33 850 1750 1550 1762.229 98.23578
3 22 33 850 1750 1550 1773.544 99.66362
4 20 297 850 1750 1550 163926 9140975
5 20 36.3 850 1750 1550 1895.839 106.4833
6 20 33 765 1750 1550 1679.762 101.6236
7 20 33 935 1750 1550 1856.837 95.81809
8 20 33 850 1575 1550 1643.116 106.2717
9 20 33 850 1925 1550 1894.91 93.19413
10 20 33 850 1750 1395 1652.489 107.4062
11 20 33 850 1750 1705 1884.142 92.28092
12 19.43332 32.06498 825.9161 1700.416 1593.918 1702.639 97.18194
13 20.56668 32.06498 825.9161 1700.416 1506.082 1642.746 101.8087
14 19.43332 33.93502 825.9161 1700.416 1506.082 1708.671 105.8903
15 20.56668 33.93502 825.9161 1700.416 1593.918 1777.564 101.8636
16 19.43332 32.06498 874.0839 1700.416 1506.082 1687.629 99.68602
17 20.56668 32.06498 874.0839 1700.416 1593.918 1754.591 96.06298
18 19.43332 33.93502 874.0839 1700.416 1593.918 1824.588 99.88825
19 20.56668 33.93502 874.0839 1700.416 1506.082 1761.191 104.5186
20 19.43332 32.06498 825.9161 1799.584 1506.082 1707.307 97.82772
21 20.56668 32.06498 825.9161 1799.584 1593.918 1775.341 93.93793
22 19.43332 33.93502 825.9161 1799.584 1593.918 1846.966 97.69657
23 20.56668 33.93502 825.9161 1799.584 1506.082 1782.628 102.6171
24 19.43332 32.06498 874.0839 1799.584 1593.918 1822.424 92.08075
25 20.56668 32.06498 874.0839 1799.584 1506.082 1760.176 96.61536
26 19.43332 33.93502 874.0839 1799.584 1506.082 1830.605 100.4751
27 20.56668 33.93502 874.0839 1799.584 1593.918 1902.225 96.53699
S6, constructing a response surface model according to the experimental design points obtained by the solution in the step S5;
specifically, in this embodiment, a Response Surface model is constructed by using a Standard second Order Response Surface algorithm (Standard Response Surface-Full 2nd Order multinomials) according to the experimental design point obtained by the solution in step S5.
It should be noted that, when the number of design variables is n, the quadratic polynomial response surface model is shown as follows:
Figure BDA0002055179770000081
in the formula x i For the design variables, n is the number of design variables and the unknown β can be mathematically determined.
In this embodiment, the experimental design point obtained by the solution in step S5 is used to construct a response surface model by using a standard second-order response surface algorithm, the fitting effect is shown in fig. 4, and the local sensitivity is shown in fig. 5. It can be seen from fig. 4 that the experimental design points are in a linear relationship, and the fitting effect of the experiment is good. As can be seen from fig. 5, the thickness P1 of the stud has a small influence on the first-order natural frequency of the oblique stud. The wall thickness P2, the height P4, and the length P5 have a large influence on the natural frequency. The wall thickness is proportional to the magnitude of the natural frequency, and the length and the height are inversely proportional to the magnitude of the natural frequency. The effect of height and length on the natural frequency is substantially the same, so increasing the first order natural frequency would increase the wall thickness while decreasing the height and decreasing the length. Fig. 6 and 7 show the wall thickness and the first order natural frequency response surface pattern, and the height and the first order natural frequency response surface pattern, respectively.
And S7, iterating the response surface model by using a genetic algorithm to obtain a Pareto optimal solution set, selecting a solution meeting the requirement from the Pareto optimal solution set as a candidate point, and obtaining an optimal solution according to the requirement.
In this embodiment, five parameters, namely, the length, the width, the height, the wall thickness, and the plate rib thickness of the oblique column, are used as design variables, the first-order natural frequency of the oblique column is improved as an optimization target, and the mass of the oblique column is minimized while conforming to the lightweight design principle, so the optimal design mathematical model in this embodiment is as follows:
Figure BDA0002055179770000101
in this embodiment, a multi-objective genetic algorithm (MOGA) algorithm is selected for solving, the initial population number is set to be 100, the maximum allowable Pareto proportion is set to be 70%, and the maximum iteration number is 20; in the target and constraint, no restriction is placed on the size parameters, the mass is set to minimum, and the first order natural frequency is set to maximum.
The Pareto solution obtained in this embodiment is shown in fig. 8.
The Pareto solutions are a series of effective solutions generated in the multi-objective optimization process, and the solutions are not divided into good solutions and bad solutions, so that the solutions meeting the requirements need to be selected according to actual conditions and design requirements. It can be seen from fig. 8 that the Pareto solution set has a stable region after increasing, and then has a downward-sliding tendency, the natural frequency increases with the increase of the mass before the mass is 1571.3kg, and the natural frequency starts to decrease with the continuous increase of the mass, so that if the first-order natural frequency is pursued to be the maximum, the turning point can be selected as a set of optimization candidate points.
Table 5 shows three candidate point schemes given in this example.
TABLE 5
Figure BDA0002055179770000111
From the three groups of optimization candidate points, it can be seen that the first-order natural frequency all reaches more than 100Hz and all meets the design requirements, and the optimization result of the candidate point three is the lowest quality, so that the quality is reduced by 13.32%, and therefore the candidate point three can be selected as the optimal solution of the optimization design.
The dimensional parameters were rounded to take into account the actual machining conditions and the results are shown in table 6.
TABLE 6
Figure BDA0002055179770000112
And solving the rounded size again, and calculating to obtain that the mass of the rounded inclined vertical column is reduced by 12.07 percent, the first-order inherent frequency is improved by 26.8 percent, and the experimental result meets the design requirement. Various corresponding changes and modifications can be made by those skilled in the art based on the above technical solutions and concepts, and all such changes and modifications should be included in the protection scope of the present invention.

Claims (5)

1. A design and optimization method of a special horizontal drilling machine oblique column for a steering knuckle bearing seat is characterized by comprising the following steps:
s1, modeling a machine tool inclined upright column by using Solidworks software;
s2, importing the machine tool inclined column model obtained in the step S1 into an ANSYS Workbench platform;
s3, grid division: the size of the grid is selected to be 30mm; setting the grid correlation to be 100, and selecting the grid type to be a regular tetrahedron unit;
s4, performing dynamic modal analysis on the oblique stand column model of the machine tool, and selecting and calculating the first 6-order natural frequency;
s5, taking the length, width, height, wall thickness and plate rib thickness of the inclined upright column as design variables, generating a test design point by adopting a central composite test design method, and solving the test design point in an ANSYS Workbench platform; the value of each design variable is set according to +/-10%;
s6, constructing a response surface model according to the experimental design points obtained by the solution in the step S5;
and S7, iterating the response surface model by using a genetic algorithm to obtain a Pareto optimal solution set, and selecting a solution meeting the requirement from the Pareto optimal solution set as a candidate point.
2. The method of claim 1, wherein in step S2, an ANSYS Workbench platform is opened directly from Solidworks software.
3. The method according to claim 1, wherein in step S2, after the oblique post model of the machine tool is introduced, a simplification process is performed to remove detailed features.
4. The method of claim 1, wherein in step S4, the kinetic modal analysis is performed by applying a fixed constraint to the bottom surface of the diagonal post.
5. The method of claim 1, wherein in step S7, a response surface model is constructed using a standard second-order response surface algorithm.
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