CN111438635B - Method for improving polishing surface uniformity of free-form surface - Google Patents

Method for improving polishing surface uniformity of free-form surface Download PDF

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CN111438635B
CN111438635B CN201811649049.4A CN201811649049A CN111438635B CN 111438635 B CN111438635 B CN 111438635B CN 201811649049 A CN201811649049 A CN 201811649049A CN 111438635 B CN111438635 B CN 111438635B
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grinding
surface roughness
value
feeding speed
uniformity
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CN111438635A (en
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张启平
方灶军
陈思鲁
李俊杰
廉宏远
陈庆盈
张弛
杨桂林
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Ningbo Institute of Material Technology and Engineering of CAS
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B24—GRINDING; POLISHING
    • B24B—MACHINES, DEVICES, OR PROCESSES FOR GRINDING OR POLISHING; DRESSING OR CONDITIONING OF ABRADING SURFACES; FEEDING OF GRINDING, POLISHING, OR LAPPING AGENTS
    • B24B49/00—Measuring or gauging equipment for controlling the feed movement of the grinding tool or work; Arrangements of indicating or measuring equipment, e.g. for indicating the start of the grinding operation
    • B24B49/006—Measuring or gauging equipment for controlling the feed movement of the grinding tool or work; Arrangements of indicating or measuring equipment, e.g. for indicating the start of the grinding operation taking regard of the speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B24—GRINDING; POLISHING
    • B24B—MACHINES, DEVICES, OR PROCESSES FOR GRINDING OR POLISHING; DRESSING OR CONDITIONING OF ABRADING SURFACES; FEEDING OF GRINDING, POLISHING, OR LAPPING AGENTS
    • B24B47/00—Drives or gearings; Equipment therefor
    • B24B47/20—Drives or gearings; Equipment therefor relating to feed movement
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B24—GRINDING; POLISHING
    • B24B—MACHINES, DEVICES, OR PROCESSES FOR GRINDING OR POLISHING; DRESSING OR CONDITIONING OF ABRADING SURFACES; FEEDING OF GRINDING, POLISHING, OR LAPPING AGENTS
    • B24B49/00—Measuring or gauging equipment for controlling the feed movement of the grinding tool or work; Arrangements of indicating or measuring equipment, e.g. for indicating the start of the grinding operation
    • B24B49/02—Measuring or gauging equipment for controlling the feed movement of the grinding tool or work; Arrangements of indicating or measuring equipment, e.g. for indicating the start of the grinding operation according to the instantaneous size and required size of the workpiece acted upon, the measuring or gauging being continuous or intermittent
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B24—GRINDING; POLISHING
    • B24B—MACHINES, DEVICES, OR PROCESSES FOR GRINDING OR POLISHING; DRESSING OR CONDITIONING OF ABRADING SURFACES; FEEDING OF GRINDING, POLISHING, OR LAPPING AGENTS
    • B24B49/00—Measuring or gauging equipment for controlling the feed movement of the grinding tool or work; Arrangements of indicating or measuring equipment, e.g. for indicating the start of the grinding operation
    • B24B49/16—Measuring or gauging equipment for controlling the feed movement of the grinding tool or work; Arrangements of indicating or measuring equipment, e.g. for indicating the start of the grinding operation taking regard of the load
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/12—Computing arrangements based on biological models using genetic models
    • G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming

Abstract

The invention discloses a method for improving the polishing surface uniformity of a free-form surface. The method comprises the following steps: establishing a surface roughness prediction model of the polishing system, and determining a feeding speed adaptation model according to the surface roughness prediction model; selecting an observation point on the polishing track, and establishing a target function of a roughness average value and a roughness variance; optimizing the expected value of the surface roughness by adopting an optimization algorithm to obtain an optimized feeding speed model; and grinding the workpiece according to the optimized feeding speed. The feeding speed of the grinding tool is adjusted and determined by a reasonable method, so that the grinding surface quality of the curved surface workpiece is ensured, the uniformity of the grinding surface quality is improved, and the grinding effect of automatic curved surface polishing is ensured.

Description

Method for improving polishing surface uniformity of free-form surface
Technical Field
The invention relates to the field of optimization of a grinding and polishing process, in particular to a method for improving the grinding surface uniformity of a free-form surface.
Background
With the increasing of polishing automation in industrial application, curved surface polishing has become an important link for the development of automated processing technology, and how to ensure polishing quality and improve polishing efficiency becomes an important content in the processing process of modern products.
Compared with the traditional manual polishing method, the automatic mechanical polishing method has the advantages of high processing efficiency, mature technology, low cost and the like. However, the existing automatic mechanical polishing mainly faces to a simple workpiece surface, and when the existing automatic mechanical polishing faces to a complex workpiece surface, the roughness of a polished surface is not uniform because a mechanical arm cannot change along with the change of a curved surface, so that the uniformity of polishing is mainly ensured by manually polishing most free curved surfaces.
The process of grinding and polishing is complex, how to control the technological parameters in the grinding process, realize the rapid and uniform removal of the surface material of the workpiece, and improve the polishing quality of the surface of the workpiece, and the method is one of the key problems of an automatic mechanical polishing system. Under the condition that the grinding and polishing equipment and the grinding workpiece are determined, according to actual machining characteristics, better machining quality can be obtained only by selecting optimal machining parameters.
In addition, the ideal process planning is also a dynamic process, namely, in the processing process, the polishing parameters are properly adjusted according to the online detection or prediction result of the prediction model of the surface quality of the polished workpiece. For example, in actual hub grinding, a prediction model result is generally selected, and when a robot performs motion trajectory planning, parameters which are easy to change in real time in curved surface polishing process parameters are optimally controlled on the basis of the prediction model, so that the smooth and uniform surface of the ground hub is ensured.
In the model, the feed speed Vt is adjusted in real time according to the change of the surface curvature of the workpiece to make the surface roughness Ra after grinding as small and constant as possible, and the grinding positive pressure F is adjusted in real timeATo make the material removal depth as constant as possible, so that the ground workpiece surface is as smooth and uniform as possible.
However, when the change in the curvature of the workpiece is small, that is, when the change in Re is small, the feed speed v can be easily determinedt(ii) a However, in curved grinding, the surface curvature of the workpiece varies greatly, for example, in actual hub grinding, the surface curvature of the hub varies greatly (12.37 mm)<Re<81.58mm) the calculated Vt may have to take a margin value because it exceeds the margin value. Therefore, the expected Ra after grinding needs to be determined additionally, the value range of Ra can be determined preliminarily according to the value ranges of other parameters, if the value of Ra is too small, the obtained Vt is possibly too small to exceed the value range in a place with small Re, and the smaller the value of Ra is, the more points of Vt exceeding the range and taking boundary values are, the worse the roughness uniformity of the workpiece is; if Ra takes on valueThe larger the size, the worse the surface quality of the workpiece after grinding. Therefore, it is necessary to determine an appropriate desired value Ra to obtain a good surface quality while ensuring as much as possible the uniformity of the polished surface.
Disclosure of Invention
Aiming at the technical current situation, the invention provides a method for improving the polishing surface uniformity of a free-form surface.
The technical scheme adopted by the invention is as follows: a method for improving the uniformity of a free-form surface to be polished comprises the following steps:
step A:
modeling the relation between the expected value Ra of the grinding surface roughness and the grinding process parameters by a probability statistical method, and establishing a surface roughness prediction model of the grinding system;
the grinding process parameters comprise a feeding speed Vt and a grinding positive pressure FAThe equivalent curvature radius Re and the tangential linear velocity Vo of the grinding tool;
determining and representing the feeding speed Vt, the expected value Ra of the surface roughness and the grinding positive pressure F according to the surface roughness prediction modelAA feed speed adaptation model of the relationship of the equivalent radius of curvature Re and the tangential linear velocity Vo of the grinding tool;
and B:
determining the value range of the polishing process parameters;
and C:
determining the value range of the expected value Ra of the surface roughness according to the value range of the surface roughness prediction model and the grinding process parameters in the step B;
step D:
establishing an objective function f1(x1) And f2(x2)；
Figure BDA0001932595550000021
Is the average value of the surface roughness and is used for measuring the magnitude of the surface roughness;
f2(x2)＝min[D(Ra)]is the variance of the surface roughnessA value for measuring the uniformity of the surface of the workpiece, wherein a smaller value indicates a more uniform surface of the workpiece;
step E:
determining a grinding track of a workpiece, selecting n observation points on the grinding track, and recording equivalent curvatures Rei of the observation points, wherein i is a natural number from 1 to n;
selected grinding positive pressure FAAnd the tangential linear velocity Vo of the sanding tool;
randomly selecting a plurality of surface roughness expected values within the value range of the surface roughness expected value Ra obtained in the step C;
for each selected expected value of the surface roughness, obtaining expected values Vti of the feeding speed of each observation point according to the feeding speed adaptation model in the step A, and modifying the Vti value exceeding the range of the feeding speed Vt determined in the step B to be close to the range boundary value of the value; then, replacing the obtained Vti value back to the surface roughness prediction model in the step A to obtain an actual surface roughness expected value Rai' of each observation point; then, according to the objective function f in step D1(x1) And f2(x2) Obtaining the average value and the variance value of the surface roughness of the n observation points;
and C, optimizing the expected value Ra of the surface roughness by adopting an optimization algorithm for the different selected expected values of the surface roughness, and substituting the optimized Ra into the adaptive model in the step A to obtain the optimized adaptive model of the feeding speed.
Step F:
and E, determining the feeding speed of each point in the grinding track according to the optimized feeding speed adaptive model obtained in the step E, and finishing grinding by adopting the feeding speed in the actual grinding process.
As one implementation mode, in the step A, the surface roughness prediction model framework is
Figure BDA0001932595550000031
Wherein, a1, a2, a3 and a4 are respectively grinding processesPositive pressure F of parameter polishingAEquivalent radius of curvature ReCoefficients of tangential linear velocity Vo and feed velocity Vt of the grinding tool; orthogonal experiments are designed, three levels are selected for each grinding process parameter, grinding experiments are carried out on the workpiece in a grinding system, and surface roughness data are collected, so that a1, a2, a3 and a4 in the model are obtained, and a complete surface roughness prediction model is obtained.
In the step A, the grinding positive pressure F is fixed in the feeding speed adaptation model (2) according to the working conditionA。
In the step A, Vo in the feeding speed adaptive model (2) is the maximum value determined in the step B as far as possible.
In the step E, as an implementation manner, the method for measuring the equivalent curvature radius Re is as follows:
the main curvature radius A, B of each observation point of the workpiece is read in the three-dimensional software, and the equivalent curvature radius of each observation point is obtained
Figure BDA0001932595550000032
In the step E, preferably, the observation points are uniformly distributed on the polishing track.
In the step E, the optimization algorithm includes, but is not limited to, a particle swarm algorithm, a genetic algorithm, an artificial neural network algorithm, and the like. Preferably, genetic algorithms are used for optimization.
In the step E, the expected value Ra of the surface roughness is optimized by using an optimization algorithm, so that the average value is as small as possible while the variance value of the surface roughness is small.
A, B, C, D, E, F is used to distinguish the steps, but there is no alphabetical relationship between the steps, and the feasible sequence in the actual operation process is the content of the invention.
Compared with the prior art, the feeding speed of the grinding tool is adjusted and determined by a reasonable method, the grinding surface quality of the curved surface workpiece is ensured, the grinding surface quality uniformity is improved, the grinding effect of the automatic curved surface polishing is well ensured, and the problems that the surface roughness is not uniform after the polishing and the grinding quality of the curved surface workpiece is poor due to the change of parameters in each direction of the free curved surface polished by the robot are solved.
Drawings
Fig. 1 is a diagram of the relationship between the evolution algebra and the fitness value in embodiment 1 of the present invention.
Detailed Description
The present invention is described in further detail below with reference to examples, which are intended to facilitate the understanding of the present invention without limiting it in any way.
Example 1:
step A: establishing a surface roughness prediction model corresponding to a polishing system
From the angle of engineering, the relation between the expected value Ra of the roughness of the polished surface and the polishing process parameter is modeled, and the polishing process parameter is polishing positive pressure FAThe surface roughness prediction model framework established by the feed speed Vt, the tangential linear velocity Vo of the grinding tool, and the equivalent radius of curvature Re is as follows:
Figure BDA0001932595550000041
wherein a1, a2, a3 and a4 are respectively grinding positive pressure FAThe equivalent radius of curvature Re, the tangential linear velocity Vo of the sanding tool, and the feed speed Vt.
Designing an orthogonal experiment, selecting three levels for each polishing process parameter, performing a polishing test on the workpiece in a polishing system to obtain each coefficient in the model, and selecting an orthogonal table L9(34) Designing an orthogonal test, testing in a grinding system under study according to an orthogonal table, collecting data, and obtaining a1, a2, a3 and a4 in the model through a regression equation.
Taking logarithms of two sides of the surface roughness prediction model shown in the formula (1), converting the logarithms into linear functions, and solving each coefficient according to a linear regression method by combining test data to obtain a complete surface roughness prediction model:
Figure BDA0001932595550000042
the feed rate adaptation model obtained from the surface roughness prediction model shown in the above equation (2) is as follows:
Figure BDA0001932595550000043
and B:
determining the value range of the grinding technological parameters as follows: 5N<FA<15N，10mm/s<Vt<60mm/s，12.37mm<Re<81.58mm，125m/min<Vo<754m/min。
And C:
and (3) determining the value range of the expected value Ra of the surface roughness according to the surface roughness prediction model shown in the formula (2) in the step A and the value range of the grinding process parameters in the step B, and optimizing the value range of the expected value Ra of the surface roughness to be 0.4-5 mu m by combining with the actual situation.
Step D: establishing an objective function f1(x1) And f2(x2)
Figure BDA0001932595550000044
Is the average value of the surface roughness and is used for measuring the magnitude of the surface roughness;
f2(x2)＝min[D(Ra)]the variance value of the surface roughness is used for measuring the uniformity of the surface of the workpiece, and the smaller the variance value is, the more uniform the surface of the workpiece is;
step E:
determining the grinding track of a workpiece, reading the grinding track in three-dimensional software, selecting 5 observation points which are uniformly distributed on the surface of the workpiece as far as possible, and recording the equivalent curvature radius Re of each observation point1＝15.31mm，Re2＝35.93mm，Re3＝50.34mm，Re4＝59.76mm，Re5＝79.31mm；
Selected grinding positive pressure FA5.731N, the maximum value of the tangential linear velocity Vo of the grinding tool is 754 m/min;
randomly selecting m surface roughness expected values within the value range of the expected value Ra of the optimized surface roughness obtained in the step C, wherein m is a natural number;
for each selected expected value of the surface roughness, obtaining expected values Vti of the feeding speed of each observation point according to a feeding speed adaptation model shown in formula (3) in the step A, wherein i is a natural number from 1 to 5, and modifying the Vti value exceeding the range of the feeding speed Vt determined in the step B to be close to the range boundary value of the Vti value; then, the obtained VtiSubstituting the surface roughness prediction model shown in the formula (2) in the step A to obtain the actual surface roughness expected value Rai' of each observation point; then, according to the objective function f in step D1(x1) And f2(x2) Obtaining the average value and the variance value of the surface roughness;
and optimizing the expected value Ra of the surface roughness by adopting an optimization algorithm for the different selected expected values of the surface roughness. Optimized by using genetic algorithm, fitness function is
G(x)＝K-w1f1(x1)-w2f2(x2)
The evolution algebra is 100, the population number is 50, the cross probability is 0.8, the mutation probability is 0.05, the character length is 40, the digit number of each variable is 20, K is 1, w is1＝0.1，w2＝5。
The relationship between the evolution algebra and the fitness value is shown in fig. 1, and it can be seen from fig. 1 that as the evolution algebra increases, the fitness value tends to increase as a whole. When the process is further advanced to 76 generations, the adaptability value reaches 0.7512, the optimized roughness value Ra is 1.593 mu m, and the values are substituted into the formula (3) and F in the step AAThe constant value 5.731N is taken, Vo is taken as the constant value 754m/min, and the optimized speed self-adaptive model is obtained as
Figure BDA0001932595550000051
Step F:
and E, determining the feeding speed of each point in the grinding track according to the optimized feeding speed adaptive model shown in the formula (4) obtained in the step E, and finishing grinding by adopting the feeding speed in the actual grinding process, so that the grinding surface quality of the curved surface workpiece is ensured, the uniformity of the grinding surface quality is also improved, and the grinding effect of the automatic curved surface polishing is well ensured.
The embodiments described above are intended to illustrate the technical solutions of the present invention in detail, and it should be understood that the above-mentioned embodiments are only specific embodiments of the present invention, and are not intended to limit the present invention, and any modification, supplement or similar substitution made within the scope of the principles of the present invention should be included in the protection scope of the present invention.

Claims (8)

1. A method for improving the polishing surface uniformity of a free-form surface is characterized by comprising the following steps: the method comprises the following steps:
step A:
modeling the relation between the expected value Ra of the grinding surface roughness and the grinding process parameters by a probability statistical method, and establishing a surface roughness prediction model of the grinding system;
the grinding process parameters comprise a feeding speed Vt and a grinding positive pressure FAEquivalent radius of curvature ReTangential linear velocity Vo with the sanding tool;
determining and representing the feed speed Vt, the expected value Ra of the surface roughness and the grinding positive pressure F according to the surface roughness modelAA feed speed adaptation model of the relationship of the equivalent radius of curvature Re and the tangential linear velocity Vo of the grinding tool;
and B:
determining the value range of the polishing process parameters;
and C:
determining the value range of the expected value Ra of the surface roughness according to the value range of the surface roughness prediction model and the grinding process parameters in the step B;
step D:
establishing an objective function f1(x1) And f2(x2)；
Figure FDA0003105311730000011
Figure FDA0003105311730000012
Is the average value of the surface roughness and is used for measuring the magnitude of the surface roughness;
f2(x2)＝min[D(Ra)]d (ra) is the variance value of the surface roughness, which is used to measure the uniformity of the workpiece surface;
step E:
determining a grinding track of a workpiece; selecting n observation points on the grinding track, and recording the equivalent curvature radius R of each observation pointeiI is a natural number from 1 to n;
selected grinding positive pressure FAAnd the tangential linear velocity Vo of the sanding tool;
randomly selecting m surface roughness expected values in the value range of the surface roughness expected Ra obtained in the step C;
for each selected expected value of the surface roughness, obtaining expected values Vti of the feeding speed of each observation point according to the feeding speed adaptation model in the step A, and modifying the Vti value exceeding the range of the feeding speed Vt determined in the step B to be close to the range boundary value of the value; then, the obtained Vti value is substituted back to the surface roughness prediction model in step a to obtain the actual expected surface roughness value Rai of each observation point’(ii) a Then, according to the objective function f in step D1(x1) And f2(x2) Obtaining the average value and the variance value of the surface roughness of the n observation points;
optimizing the surface roughness expected value Ra by adopting an optimization algorithm for different selected surface roughness expected values, and substituting the optimized Ra into the feeding speed adaptation model in the step A to obtain an optimized feeding speed adaptation model;
step F:
and E, determining the feeding speed of each point in the grinding track according to the optimized feeding speed adaptive model obtained in the step E, and finishing grinding by adopting the feeding speed in the actual grinding process.
2. The method of improving the uniformity of a free-form polishing surface of claim 1, wherein: in the step A, the surface roughness prediction model frame is
Figure FDA0003105311730000021
Wherein K1 is a constant; a1, a2, a3 and a4 are grinding process parameters grinding positive pressure F respectivelyAEquivalent radius of curvature ReCoefficients of tangential linear velocity Vo and feed velocity Vt of the grinding tool; designing orthogonal experiments, selecting three levels for each grinding process parameter, carrying out grinding experiments on the workpiece in a grinding system, collecting surface roughness data, and obtaining a1, a2, a3 and a4 in the model through a regression equation to obtain a complete surface roughness prediction model.
3. The method of improving the uniformity of a free-form polishing surface of claim 1, wherein: in the step A, the grinding positive pressure F is fixed according to the working condition in the feeding speed adaptation modelA。
4. The method of improving the uniformity of a free-form polishing surface of claim 1, wherein: in step a, Vo is the maximum value determined in step B as much as possible in the feed speed adaptation model.
5. The method of improving the uniformity of a free-form polishing surface of claim 1, wherein: in the step E, the equivalent curvature radius ReThe measurement method of (2) is as follows:
reading the minimum principal curvature A and the maximum principal curvature B of each observation point of the workpiece in three-dimensional software, and further obtaining the equivalent curvature radius of each observation point
Figure FDA0003105311730000022
6. The method of improving the uniformity of a free-form polishing surface of claim 1, wherein: and E, uniformly distributing the observation points on the grinding track.
7. The method of improving the uniformity of a free-form polishing surface of claim 1, wherein: in the step E, the optimization algorithm comprises a particle swarm algorithm, a genetic algorithm and an artificial neural network algorithm.
8. The method of improving the uniformity of a free-form polishing surface according to any one of claims 1 to 7, wherein: in the step E, the expected value Ra of the surface roughness is optimized by using an optimization algorithm, so that the average value of the surface roughness is as small as possible while the variance value of the surface roughness is small.
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
ES2435443A2 (en) * 2012-06-13 2013-12-19 Universitat Politècnica De Catalunya Method for the control of a process of flushing surfaces to generate rugs with valleys and flat mesetas (Machine-translation by Google Translate, not legally binding)
CN105268998A (en) * 2015-10-28 2016-01-27 沈阳黎明航空发动机(集团)有限责任公司 Circular-arc-shaped area turning surface roughness control method
JP2016113356A (en) * 2014-12-12 2016-06-23 旭硝子株式会社 Method for finish-working the surface of pre-polished glass substrate surface
CN105798734A (en) * 2016-03-15 2016-07-27 西安建筑科技大学 Polishing device and method for mould free curved surface
CN107378728A (en) * 2017-08-02 2017-11-24 辽宁科技大学 The best processing technology method that inner surface polishes at the irregular elbow curvature in space

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
ES2435443A2 (en) * 2012-06-13 2013-12-19 Universitat Politècnica De Catalunya Method for the control of a process of flushing surfaces to generate rugs with valleys and flat mesetas (Machine-translation by Google Translate, not legally binding)
JP2016113356A (en) * 2014-12-12 2016-06-23 旭硝子株式会社 Method for finish-working the surface of pre-polished glass substrate surface
CN105268998A (en) * 2015-10-28 2016-01-27 沈阳黎明航空发动机(集团)有限责任公司 Circular-arc-shaped area turning surface roughness control method
CN105798734A (en) * 2016-03-15 2016-07-27 西安建筑科技大学 Polishing device and method for mould free curved surface
CN107378728A (en) * 2017-08-02 2017-11-24 辽宁科技大学 The best processing technology method that inner surface polishes at the irregular elbow curvature in space

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Inventor after: Zhang Qiping

Inventor after: Fang Zaojun

Inventor after: Chen Silu

Inventor after: Li Junjie

Inventor after: Lian Hongyuan

Inventor after: Chen Qingying

Inventor after: Zhang Chi

Inventor after: Yang Guilin

Inventor before: Zhang Qiping

Inventor before: Fang Zaojun

Inventor before: Chen Silu

Inventor before: Li Junjie

Inventor before: Lian Hongyuan

Inventor before: Chen Qingying

Inventor before: Zhang Chi

Inventor before: Yang Guilin