CN106021691A - Crawler crane boom reliability optimization method - Google Patents
Crawler crane boom reliability optimization method Download PDFInfo
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Abstract
The present invention provides a crawler crane boom reliability optimization method. According to the method, a bar size in a structure of a crawler crane boom, a material characteristic parameter of the crawler crane boom and a load of the crawler crane boom are used as random variables to build a reliability optimization mathematical model where minimization of a total weight of the structure of the crawler crane boom is taken as an optimization target, the bar size is taken as an optimization design variable, and strength reliability of the crawler crane boom, stiffness reliability of the crawler crane boom, overall stability reliability of the crawler crane boom and bending bar stability reliability are taken as constraints, and to establish an automatic optimization flow process, so as to realize reliability optimization of the boom. The method provided by the present invention considers influence of uncertainty factors in the design process of the crawler crane boom, and the reliability optimization design result is capable of ensuring that the boom meets regulated reliability requirements, and security and economy of the structure of the boom are balanced, and the weight of the boom is decreased to the largest extent.
Description
Technical field
The invention belongs to Design of Mechanical Structure theory and method field, relate to crawler crane boom, be specifically related to one
Crawler crane boom reliability optimization method.
Background technology
Jib is one of main load bearing component of crawler crane, its design always research staff's focus of attention.With
The lifting altitude of crawler crane and being continuously increased of hoisting weight, the more and more large-scale even ultra-large type of crawler crane
Change, so that equipment self-weight is greatly increased.And jib weight occupies significant proportion in complete machine weight, jib is conducted oneself with dignity to complete machine
Lifting performance has very important impact, alleviates jib deadweight and means to increase hoisting weight, can make full use of material
Can, reduce cost.Therefore on the premise of guarantee crawler crane jib support structure is safe and reliable, tackle jib structure to be optimized
Design, reduces jib deadweight to greatest extent.
Although deterministic optimization method for designing plays an important role at structure design aspect, but due to its mathematical model without
Uncertain factor present in the existing engineering reality of body of laws, it is impossible to weigh the safe coefficient of structure, so that its design result is not to the utmost
Rationally, thus cause deterministic optimization result and be actually needed result and have bigger gap, there is potential performance loss and ripple
Dynamic.Accordingly, it is considered to the uncertain factor in reality, fail-safe analysis is combined with optimisation technique, jib structure is carried out
Reliability Optimum Design will be the Main Trends of The Development of jib design undoubtedly.
Summary of the invention
The deficiency existed for prior art, it is an object of the invention to, it is provided that a kind of crawler crane boom reliability
Optimization method, it is achieved the reliability Optimum Design of jib makes jib design safety and reliability, economical rationality, solves prior art
Present in problem.
The rarest about the document of crawler crane boom reliability Optimum Design.Additionally, crawler crane arm
The combining form of frame is many, belongs to multi-point optimization problem, each operating mode " should be connected " and get up to optimize, it is achieved Optimizing Flow automatic
Change.
In order to solve above-mentioned technical problem, the present invention adopts the following technical scheme that and is achieved:
A kind of crawler crane boom reliability optimization method, the method utilizes the integrated finite element of ISIGHT Optimization Platform soft
Part Ansys carries out the reliability optimization of crawler crane boom, the method by the rod member size in crawler crane jib support structure,
The material characteristic parameter of crawler crane boom and the load of crawler crane boom, as stochastic variable, are set up with crawler belt lifting
The minimum optimization aim of horn shelf structure gross weight, a size of optimizes design variable with rod member, strong with crawler crane boom
Degree reliability, the rigidity reliability of crawler crane boom, the stability in the large reliability of crawler crane boom and crawler belt rise
The Compressed Bent Bar stability reliability of heavy-duty machine jib is the reliability optimization mathematical of constraints, builds Automatic Optimal stream
Journey, it is achieved the reliability optimization of jib.
The present invention also has a following distinguishing feature:
The external diameter of shown rod member a size of rod member and the wall thickness of rod member.
The method specifically includes following steps:
Step one, determines the optimization design content of crawler crane boom:
With the minimum optimization aim of crawler crane jib support structure gross weight；
Design variable is a size of optimized with rod member；
With the strength reliability of crawler crane boom, the rigidity reliability of crawler crane boom, crawler crane arm
The stability in the large reliability of frame and the Compressed Bent Bar stability reliability of crawler crane boom are constraints.
It is as follows that described jib optimizes design variable, constraints and optimization aim jib weight object function:
Wherein:
μXFor the average of random optimization design variable X, the vector being made up of external diameter and the wall thickness of all kinds of rod members；
μPFor the average of random parameter vector P, it is made up of material characteristic parameter and load；
Wt is jib weight wt under typical conditionhSum；
C is typical condition number；
βhjWithRepresent the achieved reliability index of the jth constraints of jib under h kind operating mode and regulation respectively
Reliability index；
Step 2, determines the typical condition of optimization；
Step 3, jib parametric modeling:
The command stream programming realization response phase method utilizing finite element soft Ansys is asked for each reliability under typical condition and is referred to
Mark, obtains the input input file needed for integrated optimization and output output file；
Step 4, builds crawler crane boom reliability Optimum Design flow process:
Utilize optimization component integrated finite element analysis software Ansys in ISIGHT software, typical condition is together in series excellent
Change, i.e. call, by batch processing script file, the analytical calculation that finite element analysis software Ansys completes in step 3；
The shown file that batch processing script file i.e. suffix is .bat；
Step 5, optimal design-aside:
Optimization in the object function of optimization problem, constraints, optimization design variable, ISIGHT is set in ISIGHT
Algorithm and stopping criterion for iteration.
Step 6, starting guide flow process:
Utilize the optimized algorithm in ISIGHT to be updated optimizing design variable, and updated value is passed to the most defeated
Enter in file, make iteration optimization constantly carry out, it is possible to the change of real time inspection parameter；
Step 7, it is thus achieved that optimum results:
Meet stopping criterion for iteration, stop iteration, export optimal solution.
The shown reliability index under typical condition is tried to achieve by each power function as described below:
1) intensity power function g1(X, P)=σs-σmax (Ⅱ)
2) rigidity power function g2(X, P)=0.02HL-Ux (Ⅲ)
3) stability in the large power function g3(X, P)=Fcr-F (Ⅳ)
4) Compressed Bent Bar power function
Wherein:
gk(X, P) is the kth power function of jib under certain operating mode, k=1～4；
σsYield limit for material；
σmaxFor the maximum Von-mises stress of rod member under a certain material；
UxMaximum lateral displacement for jib；
HLFor jib length；
FcrFor jib the first rank linear buckling critical load；
F is lift heavy；N is rod member axial compressive force；
A is the clean cross-sectional area of rod member；
φ is respectively the coefficient of stability of axially compressive strut and the correction factor of the coefficient of stability；
NEx=π2EA/λ2For Euler's critical force, E is the Young's modulus of elasticity of material, and λ is rod member slenderness ratio；
M, W are respectively moment of flexure and composite bending modulus；
C0Reduction coefficient is not waited for end moment of flexure.
In the solution procedure of the shown reliability index under typical condition, can first ask for retraining under single operating mode reliability
The minima of index, jib is reliability index minima under under typical condition, the minima of reliability index is each operating mode
Minima, the order of the mix () in computer assembly in ISIGHT of asking for directly utilizing of minima realizes.
In step 2, described typical condition is three following operating modes:
1) operating mode 1: principal arm 13 meters, the radius of clean-up 3.7 meters, lift heavy 55 tons；
2) operating mode 2: principal arm 52 meters, the radius of clean-up 12 meters, lift heavy 11 tons；
3) operating mode 3: 52 meters of cantilever lifting operating modes of full extensional main jib.
In step 5, the optimized algorithm in described ISIGHT uses archipelago genetic algorithm.
In step 5, shown stopping criterion for iteration is iterations 1000 times.
In step 6, can optimize during setting procedure control condition, only when each reliability under this operating mode about
Just perform the content of next assembly when Shu Zhibiao meets condition, otherwise terminate this suboptimization and directly proceed to next iteration optimization.
Described response phase method ask for the concrete grammar of each reliability index under typical condition see document " Li Jin equality,
Crawler crane jib support structure fail-safe analysis, Chang An University's journal: natural science edition, in July, 2015, the 4th phase of volume 35:
153-158 page ".
The present invention compared with prior art, has the following technical effect that
The inventive method considers the impact of uncertain factor, reliability optimization in crawler crane boom design process
Design result can guarantee that jib meets the reliability requirement of regulation, weighs safety and the economy of jib structure, to greatest extent
Ground alleviates jib weight, cost-effective, improves jib designing quality and level.To crawler crane boom and engineering machinery
The multi-state reliability Optimum Design of product has reference value.
Accompanying drawing explanation
Fig. 1 is lower arm joint FEM (finite element) model.
Fig. 2 is 3m insert jib section FEM (finite element) model.
Fig. 3 is 6m insert jib section FEM (finite element) model.
Fig. 4 is 9m insert jib section FEM (finite element) model.
Fig. 5 is upper arm joint FEM (finite element) model.
Fig. 6 is operating mode 1 (principal arm 13 meters, the radius of clean-up 3.7 meters, lift heavy 55 tons) jib FEM (finite element) model.
Fig. 7 is operating mode 3 (principal arm 52 meters, the radius of clean-up 12 meters, lift heavy 11 tons) jib FEM (finite element) model.
Fig. 8 is crawler crane boom reliability Optimum Design flow chart.
Below in conjunction with embodiment, the particular content of the present invention is further explained in detail explanation.
Detailed description of the invention
Defer to technique scheme, the step that the inventive method described further below is complete:
Step one, determines that crawler crane boom optimizes design content.Consider design parameter and the randomness of load, with bar
Part physical dimension is for optimizing design variable, and under each optimization operating mode, the intensity of jib, rigidity, global stability and Compressed Bent Bar are stable
Property reliability be constraints, jib weight the gentliest for optimize design object.
It is as follows that jib described in step one optimizes design variable, constraints and jib weight object function:
Wherein, μXFor the average of random optimization design variable X, the vector being made up of external diameter and the wall thickness of all kinds of rod members；μPFor
The average of random parameter vector P, is made up of material characteristic parameter and load.Wt is jib weight wt under typical conditionhSum；c
For typical condition number；βhjWithRepresent the achieved reliability index of the jth constraints of jib under h kind operating mode respectively
Reliability index with regulation.
Reliability index under each operating mode can be tried to achieve by following each power function.
1) intensity power function g1(X, P)=σs-σmax (Ⅱ)
2) rigidity power function g2(X, P)=0.02HL-Ux (Ⅲ)
3) stability in the large power function g3(X, P)=Fcr-F (Ⅳ)
4) Compressed Bent Bar power function
Wherein, gk(X, P) is the kth power function of jib under certain operating mode, k=1～4；σsYield limit for material；
σmaxFor the maximum Von-mises stress of rod member under a certain material；UxMaximum lateral displacement for jib；HLFor jib length；Fcr
For jib the first rank linear buckling critical load；F is lift heavy；N is rod member axial compressive force；A is the clean cross-sectional area of rod member；φ divides
Wei the coefficient of stability of axially compressive strut and the correction factor of the coefficient of stability；NEx=π2EA/λ2For Euler's critical force, λ is that rod member is long
Carefully than；M, W are respectively moment of flexure and composite bending modulus；C0Do not wait reduction coefficient for end moment of flexure, its computing formula is shown in GB/T3811-
2008 hoist design specifications [S]. Beijing: China Standards Press, 2008.
Above-mentioned each power function is the Implicitly function of X and P, it is possible to use the method seeking inexplicit performance function reliability index
Try to achieve each reliability index βhj, such as response phase method.
Step 2, determines the typical condition of optimization.Owing to crawler crane boom combining form is the most, it is contemplated that optimize
Efficiency, should select typical condition to be optimized.
Step 3, jib parametric modeling, and the reliability index of power function each under typical condition is calculated,
To the input input file needed for integrated optimization and output output file.
Jib parametric modeling includes each basic arm joint parametric modeling and overall jib modeling.Overall jib is by different
Basic arm joint combines.Therefore, first tackle each basic arm joint to utilize finite element analysis software Ansys to carry out parametrization to build
Mould, secondly sets up overall jib model according to operating mode, finally calculates the reliability constraint index under each operating mode.
Before calculating the reliability index of Compressed Bent Bar, need first to determine respectively from single rod member bearing capacity aspect
Weak rod member under operating mode.Its method is two points of iterative methods to be tied mutually with the secondary exploitation technology of finite element analysis software Ansys
Close, determine, by continuous iteration, the weak rod member that crawler crane boom is corresponding under ultimate load effect.Main thought is
Loaded by two way classification iteration, load the loaded state checking all rod members after solving every time, until certain rod member in jib structure
Reach its critical state, so that it is determined that go out the weak rod member of jib structure.It is CN 103914596 A referring specifically to publication No.
Chinese invention patent " a kind of determine the method for truss structure weakness rod member in handling machinery ".
Above-mentioned parameter modeling program and structural analysis program are that follow-up ISIGHT integrated optimization provides required input
Input file and output output file.
Step 4, builds crawler crane boom reliability Optimum Design flow process.Utilize optimization component in ISIGHT software
Integrated finite element analysis software Ansys, " connects typical condition " and gets up to optimize, and i.e. by batch processing script file, (suffix is
.bat) analytical calculation that finite element analysis software Ansys completes in step 3 is called.
The main contents performed are needed to include under each operating mode: jib weakness rod member is analyzed, weak rod member reliability index
Calculate, web member strength reliability index calculates, main chord strength reliability index calculates, jib stability and reliability index meter
Calculate.
Step 5, arranges the object function of optimization problem, constraints, optimization design variable in ISIGHT, optimizes and calculate
Method and stopping criterion for iteration etc..
Step 6, starting guide flow process, utilize the optimized algorithm in ISIGHT to be updated optimizing design variable, and will
Updated value passes in corresponding input file, makes iteration optimization constantly carry out, it is possible to the change of real time inspection parameter.
Step 7, meets and optimizes end condition, i.e. stopping criterion for iteration, stops iteration, exports optimal solution.
Additionally, in order to improve optimization efficiency, can during optimizing setting procedure control condition, only when under this operating mode
Each reliability constraint index just performs the content of next assembly when meeting condition, otherwise terminate this suboptimization and directly proceed to next
Secondary iteration optimization, so processes and can be greatly saved the optimization time.
For the ease of determining the failure mode that under certain operating mode, jib is most likely to occur, constraint under this operating mode can be asked for reliable
The minima of property index.Jib reliability index minima under the minima of reliability index is each operating mode under typical condition
Minima.The order of the mix () in computer assembly in ISIGHT of asking for directly utilizing of minima realizes.
The specific embodiment of the present invention given below, it should be noted that and the invention is not limited in and implement in detail below
Example, all equivalents done on the basis of technical scheme each fall within protection scope of the present invention.
Embodiment:
Deferring to technique scheme, as shown in Figures 1 to 8, the present embodiment provides a kind of crawler crane boom reliability
Optimization method, the existing principal arm operating mode with certain type crawler crane carries out reliability Optimum Design for object.
Step 1, determine crawler crane boom optimize design content.Consider design parameter and the randomness of load, with bar
Part physical dimension is for optimizing design variable, and under each optimization operating mode, the intensity of jib, rigidity, global stability and Compressed Bent Bar are stable
Property reliability be constraints, jib weight the gentliest for optimize design object.
It is as follows that jib described in step 1 optimizes design variable, constraints and gross mass object function:
Wherein, μXFor the average of random optimization design variable X, the vector being made up of external diameter and the wall thickness of all kinds of rod members；μPFor
The average of random parameter vector P, is made up of material characteristic parameter and load.Wt is jib weight wt under typical conditionhSum；c
For typical condition number；βhjWithRepresent respectively under h kind operating mode the achieved reliability index of the jth constraints of jib and
The reliability index of regulation；M is reliability index constraint number under h kind operating mode.
Research shows material characteristic parameter and physical dimension Normal Distribution, owing to working environment is wanted by crawler crane
Asking the highest, hoisting process is mild, and domestic and foreign literature recommends load to use normal distribution mostly.Table 1 lists the change of stochastic variable
The different coefficient ratio of average (standard deviation with).
The coefficient of variation of table 1 stochastic variable
| Variable name | The coefficient of variation | Variable name | The coefficient of variation |
| DENS | 0.02 | ZT_369 | 0.034 |
| EX | 0.04 | XCFD_369s | 0.04 |
| sigma_20 | 0.07 | XCFT_369s | 0.05 |
| sigma_770 | 0.07 | CFD_sx | 0.04 |
| Load_rating | 0.2 | CFT_sx | 0.04 |
| ZD_all | 0.035 | XFD_x | 0.04 |
| ZT_Sx | 0.034 | XFT_x | 0.04 |
Optimize the reliability index of each power function set under clocking requirement typical condition not less than prototype structure design
Minimum reliability index 4.455.Under typical condition, crawler crane boom weight sum is the gentliest for reliability Optimum Design mesh
Mark.
Reliability index under each operating mode can be tried to achieve by each power function.
1) intensity power function g1(X, P)=σs-σmax (Ⅱ)
2) rigidity power function g2(X, P)=0.02HL-Ux (Ⅲ)
3) stability in the large power function g3(X, P)=Fcr-F (Ⅳ)
4) Compressed Bent Bar power function
Wherein, gk(X, P) is the kth power function of jib under certain operating mode, k=1～4；σsYield limit for material；
σmaxFor the maximum Von-mises stress of rod member under a certain material；UxMaximum lateral displacement for jib；HLFor jib length；Fcr
For jib the first rank linear buckling critical load；F is lift heavy；N is rod member axial compressive force；A is rod member cross-sectional area；φ is respectively
The coefficient of stability and the correction factor of the coefficient of stability for axially compressive strut；NEx=π2EA/λ2For Euler's critical force, λ is that rod member length is thin
Ratio；M, W are respectively moment of flexure and composite bending modulus；C0Do not wait reduction coefficient for end moment of flexure, its computing formula is shown in GB/T3811-2008
Hoist design specification [S]. Beijing: China Standards Press, 2008.
Above-mentioned each power function is the Implicitly function of X and P, it is possible to use ask the method for inexplicit performance function try to achieve each reliably
Property index, such as response phase method.
Step 2, determine the typical condition of optimization.Owing to crawler crane boom combining form is many, operating mode is the most, examines
Consider to optimizing efficiency, be typically chosen typical condition when optimizing.The typical condition that present case determines is as follows:
1) operating mode 1: principal arm 13 meters, the radius of clean-up 3.7 meters, lift heavy 55 tons；
2) operating mode 2: principal arm 52 meters, the radius of clean-up 12 meters, lift heavy 11 tons；
3) operating mode 3: 52 meters of cantilever lifting operating modes of full extensional main jib.
Step 3, jib parametric modeling, and the reliability index of power function each under typical condition is calculated,
To the input input file needed for integrated optimization and output output file.
Secondary development language APDL using finite element analysis software Ansys writes the weak rod member analysis under typical condition
The response phase method program that program and each reliability index calculate.
Step 4, build crawler crane boom reliability Optimum Design flow process.Utilize optimization component collection in ISIGHT software
Becoming finite element analysis software Ansys, " connected " by typical condition and get up to optimize, i.e. by batch processing script file, (suffix is
.bat) call Ansys to be analyzed.Optimizing Flow is as shown in Figure 8.
The main contents performed are needed to be under each operating mode: jib weakness rod member is analyzed, weak rod member reliability index meter
Calculate, web member strength reliability index calculates, main chord strength reliability index calculates, jib stability and reliability index calculates.
Step 5, the object function of optimization problem, constraints, optimization design variable, optimized algorithm are set in ISIGHT
And stopping criterion for iteration etc..Selecting archipelago genetic algorithm, iterations is set to 1000 times.
Step 6, starting guide flow process, utilize the optimized algorithm in ISIGHT to be updated optimizing design variable, and will
Updated value passes in corresponding input file, makes iteration optimization constantly carry out.
Step 7, the satisfied end condition that optimizes, stopping iteration, export optimal solution.
Table 2 gives optimum results.
Table 2 original design and reliability Optimum Design result
In table 2 the 4th row before numeral "+" represent optimum results value added on the basis of structure original design, "-"
Represent optimum results decreasing value on the basis of structure original design.Visible, optimum results is ensureing that jib minimum reliability refers to
When mark is not less than original design minimum reliability index 4.455, after optimization, minimum reliability index is 4.809, corresponding reliability
Being not less than 0.99999924, failure probability is less than 0.00000076, and jib gross weight wt loss of weight 342.6kg, loss of weight percentage ratio is
2.8%.Additionally, the safety of jib structure and economy are opposition.Reliability index is the highest, and structure is the safest, steel used
Material is the most.The minimal reliability of the crawler crane boom being proposed to be used in general lifting occasion at this is not less than 0.99999；Special
The minimal reliability of the jib of occasion lifting is not less than 0.999999.This method optimum results can ensure that jib structure meets
On the premise of reliability requirement, carrying out jib loss of weight, loss of weight ratio is relevant with the reliability index of regulation.
Table 3 lists the reliability index of original design and each constraints of reliability Optimum Design, and wherein "-" represents this
Reliability index is very big, can not consider that it lost efficacy.
Table 3 original design and each reliability index of reliability Optimum Design
As shown in Table 3, during operating mode 1, the reliability constraint of Compressed Bent Bar stability is tight constraint, the entirety of jib after optimization
Stability has obtained bigger raising.During operating mode 2, the stability in the large reliability constraint of jib is tight constraint.Visible, jib can
By property optimization design in the case of ensureing that structure meets regulation reliability, it is contemplated that the impact of uncertain factor, maximum journey
Degree ground reduces jib structure deadweight, also can the safe coefficient of quantitative description jib structure.
In order to check above-mentioned reliability optimization result the most also to meet other working condition requirement, table 4 gives 5 kinds of inspection mans
Condition.
The checking operating mode of table 4 optimum results
| Operating mode | Principal arm length/m | The radius of clean-up/m | Load/t |
| Operating mode 4 | 13 | 4.5 | 47 |
| Operating mode 5 | 16 | 4.5 | 45 |
| Operating mode 6 | 16 | 5.5 | 35 |
| Operating mode 7 | 31 | 7 | 24 |
| Operating mode 8 | 49 | 12 | 11 |
For making jib not be the vulnerable area in complete machine, the load under checking operating mode all takes than the rated load of original design
Must be big.Table 5 lists optimum results reliability index of each constraints of jib under checking operating mode herein.Wherein "-"
Represent that this index is very big, can not consider that it lost efficacy.Visible, under galianconism operating mode, jib rigidity reliability is high, it is not necessary to meter
Calculate.The reliability index of Compressed Bent Bar stability is still that minimum.Under long-armed operating mode, jib is susceptible to overall collapse.By table
5 results understand, and under checking operating mode, minimum reliability index β of optimum results is all not less than the reliability index of regulation, the fullest
Foot reliability constraints, shows that the optimum results of this method is effective.
Jib reliability index under operating mode verified by table 5
Claims (9)
1. a crawler crane boom reliability optimization method, the method utilizes the integrated finite element software of ISIGHT Optimization Platform
Ansys carries out the reliability optimization of crawler crane boom, it is characterised in that: the method is by crawler crane jib support structure
The load of rod member size, the material characteristic parameter of crawler crane boom and crawler crane boom, as stochastic variable, is set up
With the minimum optimization aim of crawler crane jib support structure gross weight, a size of optimize design variable with rod member, with crawler belt lifting
The strength reliability of horn frame, the rigidity reliability of crawler crane boom, crawler crane boom stability in the large reliable
The Compressed Bent Bar stability reliability of degree and crawler crane boom is the reliability optimization mathematical of constraints, builds certainly
Dynamic Optimizing Flow, it is achieved the reliability optimization of jib.
2. the optimization method as shown in claim 1, it is characterised in that: the external diameter of shown rod member a size of rod member and rod member
Wall thickness.
3. the optimization method as shown in claim 1, it is characterised in that: the method specifically includes following steps:
Step one, determines the optimization design content of crawler crane boom:
With the minimum optimization aim of crawler crane jib support structure gross weight；
Design variable is a size of optimized with rod member；
With the strength reliability of crawler crane boom, the rigidity reliability of crawler crane boom, crawler crane boom
The Compressed Bent Bar stability reliability of stability in the large reliability and crawler crane boom is constraints.
It is as follows that described jib optimizes design variable, constraints and optimization aim jib weight object function:
Wherein:
μXFor the average of random optimization design variable X, the vector being made up of external diameter and the wall thickness of all kinds of rod members；
μPFor the average of random parameter vector P, it is made up of material characteristic parameter and load；
Wt is jib weight wt under typical conditionhSum；
C is typical condition number；
βhjWithRepresent the achieved reliability index of the jth constraints of jib under h kind operating mode and the reliable of regulation respectively
Property index；
Step 2, determines the typical condition of optimization；
Step 3, jib parametric modeling:
The command stream programming realization response phase method utilizing finite element soft Ansys asks for each reliability index under typical condition,
To the input input file needed for integrated optimization and output output file；
Step 4, builds crawler crane boom reliability Optimum Design flow process:
Utilizing optimization component integrated finite element analysis software Ansys in ISIGHT software, be together in series optimization by typical condition, i.e.
The analytical calculation that finite element analysis software Ansys completes in step 3 is called by batch processing script file；
The shown file that batch processing script file i.e. suffix is .bat；
Step 5, optimal design-aside:
In ISIGHT, arrange the optimization in the object function of optimization problem, constraints, optimization design variable, ISIGHT calculate
Method and stopping criterion for iteration.
Step 6, starting guide flow process:
Utilize the optimized algorithm in ISIGHT to be updated optimizing design variable, and pass to updated value input literary composition accordingly
In part, iteration optimization is made constantly to carry out, it is possible to the change of real time inspection parameter；
Step 7, it is thus achieved that optimum results:
Meet stopping criterion for iteration, stop iteration, export optimal solution.
Optimization method the most as stated in claim 3, it is characterised in that: the shown reliability index under typical condition is by such as
Lower described each power function is tried to achieve:
1) intensity power function g1(X, P)=σs-σmax (Ⅱ)
2) rigidity power function g2(X, P)=0.02HL-Ux (Ⅲ)
3) stability in the large power function g3(X, P)=Fcr-F (Ⅳ)
4) Compressed Bent Bar power function
Wherein:
gk(X, P) is the kth power function of jib under certain operating mode, k=1～4；
σsYield limit for material；
σmaxFor the maximum Von-mises stress of rod member under a certain material；
UxMaximum lateral displacement for jib；
HLFor jib length；
FcrFor jib the first rank linear buckling critical load；
F is lift heavy；N is rod member axial compressive force；
A is the clean cross-sectional area of rod member；
φ is respectively the coefficient of stability of axially compressive strut and the correction factor of the coefficient of stability；
NEx=π2EA/λ2For Euler's critical force, E is the Young's modulus of elasticity of material, and λ is rod member slenderness ratio；
M, W are respectively moment of flexure and composite bending modulus；
C0Reduction coefficient is not waited for end moment of flexure.
5. the optimization method as shown in claim 4, it is characterised in that: solving of the shown reliability index under typical condition
During, can first ask for retraining under single operating mode the minima of reliability index, jib is reliability index under typical condition
Minima is the minima of reliability index minima under each operating mode, minima ask for directly utilizing in ISIGHT calculate
Mix () order in device assembly realizes.
Optimization method the most as stated in claim 3, it is characterised in that: in step 2, described typical condition be three as follows
Operating mode:
1) operating mode 1: principal arm 13 meters, the radius of clean-up 3.7 meters, lift heavy 55 tons；
2) operating mode 2: principal arm 52 meters, the radius of clean-up 12 meters, lift heavy 11 tons；
3) operating mode 3: 52 meters of cantilever lifting operating modes of full extensional main jib.
Optimization method the most as stated in claim 3, it is characterised in that: in step 5, the described optimized algorithm in ISIGHT
Use archipelago genetic algorithm.
Optimization method the most as stated in claim 3, it is characterised in that: in step 5, shown stopping criterion for iteration is iteration
Number of times 1000 times.
Optimization method the most as stated in claim 3, it is characterised in that: in step 6, can setting procedure control during optimizing
Condition processed, only just performs the content of next assembly, otherwise when each reliability constraint index under this operating mode meets condition
Terminate this suboptimization and directly proceed to next iteration optimization.
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