CN108920806A - A kind of heavy machine tool reliability allocation methods based on Trapezoid Fuzzy Number and ranking method - Google Patents
A kind of heavy machine tool reliability allocation methods based on Trapezoid Fuzzy Number and ranking method Download PDFInfo
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Abstract
The heavy machine tool reliability allocation methods based on Trapezoid Fuzzy Number and ranking method that the invention discloses a kind of, belong to lathe reliability design field, heavy machine tool Reliability Distribution problem is specifically considered as Multiple Attribute Decision Problems, realizes lathe Reliability Distribution using the correlation technique of Multiple Attribute Decision Problems.It is expressed using decision information of the intuition Trapezoid Fuzzy Number to each expert, the decision matrix for merging each expert later obtains integrated decision-making matrix model, finally the Reliability Distribution coefficient of subsystems is obtained using similarity to ideal solution ranking method, the Reliability Distribution for completing heavy machine tool, improves the reliability of domestic heavy digital control machine tool.
Description
Technical field
The present invention relates to the reliability allocation methods of heavy digital control machine tool, belong to lathe reliability design field.
Background technique
Numerically-controlled machine tool is the modern electromechanical equipment of a kind of high-precision, high efficiency, high-tech, the base as advanced manufacturing technology
Plinth and core equipment, are more and more widely used among machinery production, and restrict the hair of manufacturing field and each high and new technology
Exhibition.Current domestic heavy digital control machine tool speed, precision and in terms of make remarkable progress, but reliability index
There are obvious gaps with world level, seriously affect the product reputation of domestic weight equipment and the competitiveness of domestic and international market,
Technical bottleneck as industry.Heavy machine tool is expensive, and possess usually as national economy priority industry field enterprise
Key equipment, processing object are often the kernel component of consumer products, and event is frequently resulted in since the reliability of lathe is relatively low
Barrier is shut down can cause huge economic loss to user.The reliability of product designs first, carries out heavy type numerical control machine
The research of bed Reliability Distribution technology, improves the reliability level of heavy digital control machine tool from source, and forms a set of maturation
Heavy digital control machine tool Reliability Distribution technical solution is the urgent need of industry, has great strategic importance.
The reliability of numerically-controlled machine tool is to measure the important indicator of machine mass quality.The reliability of lathe directly affects processing
Quality, productivity and efficiency, and the confidence of the market competitiveness and user is further influenced, so the numerically-controlled machine tool of tool high reliability
Manufacturing industry there is an urgent need to.The Reliability Distribution of numerically-controlled machine tool is the committed step in lathe reliability design.Utilize lathe
Reliability Distribution technology, we can be designed that the numerically-controlled machine tool of high reliability, can also be improved the reliability of existing lathe.Weight
Type structure of numerically controlled machine-tool is complicated, and workload is very big when being allocated using traditional Cnc ReliabilityintelligeNetwork Network distribution method, calculates
Process is complicated, so propose that one kind is easy to calculate, process is simple and to be easily programmed the reliability allocation methods of realization be current weight
Type Cnc ReliabilityintelligeNetwork Network design work there is an urgent need to.It solves Cnc ReliabilityintelligeNetwork Network assignment problem and needs to complete two weights
Want step：
The first, integrated decision-making matrix model is established using intuition Trapezoid Fuzzy Number；
According to the principle of work and power and structure feature of lathe, lathe is divided into several subsystems, then determining influences reliability
A number of factors of distribution.Then lathe Reliability Distribution is considered as Multiple Attribute Decision Problems, by these subsystems and influence factor
It is considered as the scheme collection and property set of Multiple Attribute Decision Problems, combines intuition Trapezoid Fuzzy Number by industry specialists and Machine Tool design personnel
Theory carries out decision to these subsystems and influence factor, obtains several decision matrixs, then believes the decision of all policymaker
Breath is assembled, and an integrated decision-making matrix is obtained.
The second, each scheme is ranked up using similarity to ideal solution ranking method, obtains the Reliability Distribution power of each subsystem
Weight completes lathe Reliability Distribution task.
Using the approach degree of each scheme in similarity to ideal solution ranking method calculating integrated decision-making matrix to ideal scheme, then
These approach degrees are converted to the Reliability Distribution weight of each subsystem, to complete lathe Reliability Distribution.
The present invention is merged using decision information of the intuition Trapezoid Fuzzy Number to expert and designer, obtains integrated decision-making
Then matrix model obtains the Reliability Distribution weight of each subsystem using similarity to ideal solution ranking method.
Summary of the invention
The object of the present invention is to provide a kind of heavy digital control machine tool based on Intuitionistic Fuzzy Numbers and similarity to ideal solution ranking method
Reliability allocation methods, hereinafter referred to as ITrFNs method.It is asked first lathe Reliability Distribution problem is considered as multiple attribute decision making (MADM)
Topic, resolves into several subsystems for lathe, then scheme collection of these subsystems as Multiple Attribute Decision Problems determines several again
These factors, are considered as the property set of Multiple Attribute Decision Problems by the factor for influencing Reliability Distribution.According to Multiple Attribute Decision Problems
Resolving ideas, indicate decision information using intuition Trapezoid Fuzzy Number by multidigit expert and designer, finally obtain integrated decision-making
Matrix model.Each scheme is ranked up using similarity to ideal solution ranking method, is assigned to lathe overall goal reasonably respectively
On a subsystem, to improve the reliability of domestic heavy digital control machine tool.
The technical solution adopted by the present invention is a kind of heavy machine tool Reliability Distribution based on Trapezoid Fuzzy Number and ranking method
Heavy digital control machine tool Reliability Distribution problem is considered as Multiple Attribute Decision Problems by method, this method, by heavy digital control machine tool subsystem
System and the factor for influencing heavy digital control machine tool Reliability Distribution are considered as the scheme collection and property set of Multiple Attribute Decision Problems, by straight
Feel that Trapezoid Fuzzy Number expresses the decision information of expert and designer, and all decision informations are merged and are integrated
Then decision matrix is ranked up each scheme using similarity to ideal solution ranking method, by the approach degree of each scheme and ideal solution
Coefficient is converted into the weight vectors of Reliability Distribution, is finally completed the Reliability Distribution of heavy digital control machine tool and improves domestic heavy
The reliability level of type numerically-controlled machine tool.
Specifically comprise the following steps：
Step 1：According to the structure feature and the principle of work and power of heavy digital control machine tool, system subdivision is carried out to it and is enumerated
Come.These subsystems constitute the scheme collection O={ o of heavy digital control machine tool Reliability Distribution problem1,o2,…,om}.Wherein O is
The set for the scheme that all subsystems are constituted.M indicates the number of divided subsystem and the number of scheme.o1,
o2,…,omExpression scheme 1, scheme 2 and scheme m, wherein each scheme is a subsystem.
Heavy digital control machine tool is divided into eight subsystems, as shown in table 1 below：
The system subdivision result of the common lathe of table 1
Step 2：The reliability index that heavy digital control machine tool entirety is determined by heavy digital control machine tool designer, then will affect
The factor of heavy digital control machine tool Reliability Distribution itemizes out.These influence factors constitute heavy digital control machine tool reliability
Property set C={ the c of assignment problem1,c2,…,cn, wherein C indicates the set of all properties, and considered influence heavy type
The set of the factor of Cnc ReliabilityintelligeNetwork Network distribution.N indicates the number of influence factor, and the number of attribute.c1,c2,…,cn
Indicate attribute 1, attribute 2 and attribute n, wherein each attribute is an influence factor.
Considered influence heavy digital control machine tool Reliability Distribution because being known as：Complexity, reliability, maintainability, safety and human factors, technical level,
Working environment, cost and working time.
Step 3：Decision information is provided for scheme collection and property set by related fields expert, lists the decision of each expert
Matrix Rk, wherein RkIndicate that the decision matrix of kth position expert, k=1,2 ..., h indicate to share h experts.
Expert carries out using language when decision, for that need to convert language to intuition Trapezoid Fuzzy Number convenient for calculating
Form, the following table 2 gives the transformational relation between language and intuition Trapezoid Fuzzy Number.Language is converted into intuition ladder
After shape fuzzy number, expert decision-making matrix RkIn element intuition Trapezoid Fuzzy Number is all converted to by language, at this time For
Intuition Trapezoid Fuzzy Number form.
Transfer standard between 2 language of table and intuition Trapezoid Fuzzy Number
For expert when carrying out decision, since everyone experience is different, the decision made is different, so expert in order to prevent
Opinion there is disagreement, so brainstrust needs standard when making decision, this decision criteria is as shown in table 3 below：
3 decision criteria of table
Step 4：Assemble the decision matrix of all experts, constructs integrated decision-making matrix R.
The decision matrix of all policymaker is collected using intuition Trapezoid Fuzzy Number weighted average operator (ITrFNWA)
Knot.
Intuition Trapezoid Fuzzy Number weighted average operator (ITrFNWA) is defined as follows：
If Aβ(β=1,2 ..., p) is one group of intuition Trapezoid Fuzzy Number, w=(w1,w2,…,wp)TIt is AβWeight vectors,
Then have：
Work as wβWhen=1/p, formula (1) becomes：
In addition, setting Aβ=<(aβ1,aβ2,aβ3,aβ4),(bβ1,bβ2,bβ3,bβ4)>(β=1,2) are that two intuition are trapezoidal fuzzy
Number, then have：
A1+A2=<(a11+a21,a12+a22,a13+a23,a14+a24),(b11+b21,b12+b22,b13+b23,b14+b24)>(3)
The decision matrix of all experts is assembled using formula (2) and (3), obtains integrated decision-making matrix R=
(rij)m×n, i=1,2 ..., m, j=1,2 ..., n, wherein
Step 5：Use the desired value of intuition Trapezoid Fuzzy Number as the weight of attribute.
The desired value of intuition Trapezoid Fuzzy Number is defined as follows：
If A=<(a1,a2,a3,a4),(b1,b2,b3,b4)>It is an intuition Trapezoid Fuzzy Number, under its desired value passes through
Formula obtains：
Using these desired values, the weight matrix U=(u of all properties is obtainedij)m×n, wherein
uij=EV (rij), (i=1,2 ... m, j=1,2 ..., n) (6)
Step 6：The positive ideal dematrix of definition and minus ideal result matrix.
The positive ideal solution of decision matrixAnd minus ideal resultWherein
Step 7：It calculates and weights positive distance measure and negative distance measure.
It is calculated separately using formula (9) and (10) and assembles the Intuitionistic Fuzzy Decision matrix R and positive ideal solution R of decision matrix+With it is negative
Ideal solution R-Between Weighted distanceWith
Wherein uijFor the element in attribute weight matrix U,Indicate intuition Trapezoid Fuzzy Number rijWithBetween
Distance,Indicate intuition Trapezoid Fuzzy Number rijWithThe distance between.The distance between two intuition Trapezoid Fuzzy Numbers are fixed
Justice is as follows：
If Aβ=<(aβ1,aβ2,aβ3,aβ4),(bβ1,bβ2,bβ3,bβ4)>(β=1,2) is two intuition Trapezoid Fuzzy Numbers, then
A1With A2The distance between be：
Step 8：Calculate relative similarity degree coefficient lambda.
Scheme oiRelative similarity degree coefficient lambdaiCalculation formula is as follows：
Step 9：Calculate Reliability Distribution coefficient k.
Scheme oiReliability Distribution coefficient kiCalculation formula is as follows：
Step 10：The Reliability Distribution of heavy digital control machine tool is completed according to Reliability Distribution coefficient.
The reliability R of subsystems is obtained according to Reliability Distribution coefficientiCalculation formula：
Wherein RsIt is the reliability of lathe entirety, RiIt is allocated to the reliability of i-th of subsystem.
Detailed description of the invention
Fig. 1 is the flow chart that the method for the present invention is implemented.
Specific embodiment
The present invention verifies above-mentioned heavy machine tool reliability allocation methods by taking certain heavy type numerical control planer-type milling machine as an example.
Specifically comprise the following steps：
Step 1：System subdivision is carried out to heavy CNC planer type milling machine, 8 sons are divided into according to the mechanism of lathe
System, as shown in table 4 below.This 8 subsystems constitute the scheme collection of Multiple Attribute Decision Problems, i.e. O={ o1,o2,…,o8, often
A subsystem is all a kind of scheme.
The system subdivision result of 4 heavy type numerical control planer-type milling machine of table
Step 2：It is required according to user, the reliability R of lathe entirety is determined by designers, then in conjunction with actual conditions
Determine the factor and Reliability Distribution principle for influencing lathe Reliability Distribution.
The reliability R of this heavy type numerical control planer-type milling machine entirety determines according to actual conditionssIt is 0.85.Influence lathe reliability
Distribution because being known as 6, shown in these influence factors table 5 specific as follows.
The factor and Reliability Distribution principle of the influence Reliability Distribution of table 5
The factors composition property set C={ c of Multiple Attribute Decision Problems of this 6 influence Reliability Distributions1,c2,…,c6,
Each influence factor is an attribute.Wherein, in order to consistent with the distribution principle of other influences factor, for technical level
With two influence factors of working environment, we provide expert when judging the two influence factors, consider the non-maturity of technology and
Bad environments degree carries out decision, and in this way when expert carries out decision to the two influence factors, the decision value that the two obtains is got over
The reliability of height, distribution is lower, this is consistent with the distribution principle of other factors, is convenient for next calculating.
Step 3：List all expert decision-making matrix Rk。
It invites three experts to carry out decision under 6 influence factors to this 8 subsystems, obtains three decision matrixs.
Step 4：Assemble the decision matrix of each expert, constructs the comprehensive trapezoidal fuzzy decision matrix R of intuition.
Using formula (2), (3), (4) and table 2, the decision matrix of three experts is assembled, available one comprehensive
The trapezoidal fuzzy decision matrix R of intuition is closed, it is represented with the form of table.
Step 5：The desired value for calculating the intuition Trapezoid Fuzzy Number in the comprehensive trapezoidal fuzzy matrix R of intuition, these it is expected
It is worth the weight as attribute.
The desired value that intuition Trapezoid Fuzzy Number is calculated using formula (5) and (6), finally obtains the weight matrix U of attribute, such as
Shown in lower：
Step 6：Positive ideal solution and minus ideal result are determined using formula (7) and (8), then according to the weight matrix U of attribute,
The positive distance measure D of weighting is calculated in conjunction with formula (9), (10) and (11)i +With negative distance measure Di+.Its result such as the following table 6 institute
Show.
Table 6 weights positive distance measure and negative distance measure
Step 7：Relative similarity degree λ and Reliability Distribution coefficient k are calculated using formula (12) and (13), as a result such as the following table 7
It is shown.
The Reliability Distribution coefficient of table 7 relative similarity degree coefficient and each subsystem
Step 8：It is as a result as follows as a result, calculating the reliability that each subsystem distributes in conjunction with formula (14) according to table 7
Shown in table 8.In addition the method used in AHP method and this patent carries out Comparative result, to illustrate the method in this patent
Validity and accuracy.
8 Reliability Distribution result of table and compare
Reliability Distribution result：
The method proposed according to this patent, the reliability for obtaining subsystems are as follows：It is hydraulic reliable with pneumatic system
Degree is 0.9803；Feed system reliability is 0.98；Axis system reliability is 0.9818；Servo-system reliability is
0.9761；Lubricating system reliability is 0.9785；Cooling system reliability is 0.9771；Automatic tool changer reliability is
0.9848；Digital control system reliability is 0.9805.
Claims (1)
1. a kind of heavy machine tool reliability allocation methods based on Trapezoid Fuzzy Number and ranking method, it is characterised in that：This method will
Heavy digital control machine tool Reliability Distribution problem is considered as Multiple Attribute Decision Problems, by heavy digital control machine tool subsystem and influences heavy number
The factor of control lathe Reliability Distribution is considered as the scheme collection and property set of Multiple Attribute Decision Problems, passes through intuition Trapezoid Fuzzy Number pair
The decision information of expert and designer are expressed, and all decision informations are merged to obtain integrated decision-making matrix, then
Each scheme is ranked up using similarity to ideal solution ranking method, converts the approach degree coefficient of each scheme and ideal solution to reliably
Property distribution weight vectors, be finally completed the Reliability Distribution of heavy digital control machine tool；
Specifically comprise the following steps：
Step 1：According to the structure feature and the principle of work and power of heavy digital control machine tool, system subdivision is carried out to it and is enumerated to come；
These subsystems constitute the scheme collection O={ o of heavy digital control machine tool Reliability Distribution problem1,o2,…,om}；Wherein O is all
The set for the scheme that subsystem is constituted；M indicates the number of divided subsystem and the number of scheme；o1,o2,…,om
Expression scheme 1, scheme 2 and scheme m, wherein each scheme is a subsystem；
Heavy digital control machine tool is divided into eight subsystems, as shown in table 1 below：
The system subdivision result of the common lathe of table 1
Step 2：The reliability index that heavy digital control machine tool entirety is determined by heavy digital control machine tool designer, then will affect heavy type
The factor of Cnc ReliabilityintelligeNetwork Network distribution itemizes out；These influence factors constitute heavy digital control machine tool Reliability Distribution
Property set C={ the c of problem1,c2,…,cn, wherein C indicates the set of all properties, and considered influence heavy type numerical control
The set of the factor of lathe Reliability Distribution；N indicates the number of influence factor, and the number of attribute；c1,c2,…,cnIt indicates
Attribute 1, attribute 2 and attribute n, wherein each attribute is an influence factor；
Considered influence heavy digital control machine tool Reliability Distribution because being known as：Complexity, reliability, maintainability, safety and human factors, technical level, work
Environment, cost and working time；
Step 3：Decision information is provided for scheme collection and property set by related fields expert, lists the decision matrix of each expert
Rk, wherein RkIndicate that the decision matrix of kth position expert, k=1,2 ..., h indicate to share h experts；
Expert carries out using language when decision, for the shape that need to convert language to convenient for calculating intuition Trapezoid Fuzzy Number
Formula, the following table 2 give the transformational relation between language and intuition Trapezoid Fuzzy Number；Language is converted into the trapezoidal mould of intuition
After pasting number, expert decision-making matrix RkIn element intuition Trapezoid Fuzzy Number is all converted to by language, at this time
I=1,2 ..., m, j=1,2 ..., n, k=1,2 ..., h,For intuition ladder
Shape fuzzy number form；
Transfer standard between 2 language of table and intuition Trapezoid Fuzzy Number
For expert when carrying out decision, since everyone experience is different, the decision made is different, so the meaning of expert in order to prevent
See disagreement occur, so brainstrust needs a standard when making decision, this decision criteria is as shown in table 3 below：
3 decision criteria of table
Step 4：Assemble the decision matrix of all experts, constructs integrated decision-making matrix R；
The decision matrix of all policymaker is assembled using intuition Trapezoid Fuzzy Number weighted average operator ITrFNWA；
Intuition Trapezoid Fuzzy Number weighted average operator ITrFNWA is defined as follows：
If Aβ(β=1,2 ..., p) is one group of intuition Trapezoid Fuzzy Number, w=(w1,w2,…,wp)TIt is AβWeight vectors, then have：
Work as wβWhen=1/p, formula (1) becomes：
In addition, setting Aβ=<(aβ1,aβ2,aβ3,aβ4),(bβ1,bβ2,bβ3,bβ4)>(β=1,2) is two intuition Trapezoid Fuzzy Numbers, then
Have：
A1+A2=<(a11+a21,a12+a22,a13+a23,a14+a24),(b11+b21,b12+b22,b13+b23,b14+b24)> (3)
The decision matrix of all experts is assembled using formula (2) and (3), obtains integrated decision-making matrix
R=(rij)m×n, i=1,2 ..., m, j=1,2 ..., n, wherein
Step 5：Use the desired value of intuition Trapezoid Fuzzy Number as the weight of attribute；
The desired value of intuition Trapezoid Fuzzy Number is defined as follows：
If A=<(a1,a2,a3,a4),(b1,b2,b3,b4)>It is an intuition Trapezoid Fuzzy Number, its desired value is obtained by following formula
It arrives：
Using these desired values, the weight matrix U=(u of all properties is obtainedij)m×n, wherein
uij=EV (rij), (i=1,2 ... m, j=1,2 ..., n) (6)
Step 6：The positive ideal dematrix of definition and minus ideal result matrix；
The positive ideal solution of decision matrixAnd minus ideal resultWherein
Step 7：It calculates and weights positive distance measure and negative distance measure；
It is calculated separately using formula (9) and (10) and assembles the Intuitionistic Fuzzy Decision matrix R and positive ideal solution R of decision matrix+And minus ideal result
R-Between Weighted distanceWith
Wherein uijFor the element in attribute weight matrix U,Indicate intuition Trapezoid Fuzzy Number rijWithThe distance between,Indicate intuition Trapezoid Fuzzy Number rijWithThe distance between；The distance between two intuition Trapezoid Fuzzy Numbers define such as
Under：
If Aβ=<(aβ1,aβ2,aβ3,aβ4),(bβ1,bβ2,bβ3,bβ4)>(β=1,2) is two intuition Trapezoid Fuzzy Numbers, then A1With A2
The distance between be：
Step 8：Calculate relative similarity degree coefficient lambda；
Scheme oiRelative similarity degree coefficient lambdaiCalculation formula is as follows：
Step 9：Calculate Reliability Distribution coefficient k；
Scheme oiReliability Distribution coefficient kiCalculation formula is as follows：
Step 10：The Reliability Distribution of heavy digital control machine tool is completed according to Reliability Distribution coefficient；
The reliability R of subsystems is obtained according to Reliability Distribution coefficientiCalculation formula：
Wherein RsIt is the reliability of lathe entirety, RiIt is allocated to the reliability of i-th of subsystem.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109901515A (en) * | 2019-03-28 | 2019-06-18 | 北京工业大学 | A kind of heavy machine tool reliability allocation methods based on OWA operator |
| CN110704986A (en) * | 2019-10-18 | 2020-01-17 | 重庆大学 | Mechanical system reliability distribution method based on minimum variability OWGA and fuzzy DEMATEL |
| CN112632739A (en) * | 2020-09-30 | 2021-04-09 | 北京工业大学 | Machine tool reliability distribution method based on intuitive trapezoidal fuzzy and grey correlation |
| CN112904294A (en) * | 2021-03-04 | 2021-06-04 | 西安电子科技大学 | Radar interference effect evaluation method based on intuitive trapezoidal fuzzy multi-attribute decision |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5262941A (en) * | 1990-03-30 | 1993-11-16 | Itt Corporation | Expert credit recommendation method and system |
| US20140279801A1 (en) * | 2013-03-15 | 2014-09-18 | International Business Machines Corporation | Interactive method to reduce the amount of tradeoff information required from decision makers in multi-attribute decision making under uncertainty |
| CN107220498A (en) * | 2017-05-26 | 2017-09-29 | 中南大学 | A kind of mechanical material evaluation method and its system |
-
2018
- 2018-06-26 CN CN201810665649.3A patent/CN108920806B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5262941A (en) * | 1990-03-30 | 1993-11-16 | Itt Corporation | Expert credit recommendation method and system |
| US20140279801A1 (en) * | 2013-03-15 | 2014-09-18 | International Business Machines Corporation | Interactive method to reduce the amount of tradeoff information required from decision makers in multi-attribute decision making under uncertainty |
| CN107220498A (en) * | 2017-05-26 | 2017-09-29 | 中南大学 | A kind of mechanical material evaluation method and its system |
Non-Patent Citations (2)
| Title |
|---|
| 米金华 等: "基于模糊理论的数控机床液压系统故障树分析", 《制造技术与机床》 * |
| 谭壮 等: "基于模糊评判的数控机床零部件制造工艺FMECA研究", 《南京信息工程大学学报(自然科学版)》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109901515A (en) * | 2019-03-28 | 2019-06-18 | 北京工业大学 | A kind of heavy machine tool reliability allocation methods based on OWA operator |
| CN110704986A (en) * | 2019-10-18 | 2020-01-17 | 重庆大学 | Mechanical system reliability distribution method based on minimum variability OWGA and fuzzy DEMATEL |
| CN112632739A (en) * | 2020-09-30 | 2021-04-09 | 北京工业大学 | Machine tool reliability distribution method based on intuitive trapezoidal fuzzy and grey correlation |
| CN112632739B (en) * | 2020-09-30 | 2024-02-23 | 北京工业大学 | Machine tool reliability distribution method based on intuitive trapezoidal fuzzy and gray correlation |
| CN112904294A (en) * | 2021-03-04 | 2021-06-04 | 西安电子科技大学 | Radar interference effect evaluation method based on intuitive trapezoidal fuzzy multi-attribute decision |
| CN112904294B (en) * | 2021-03-04 | 2023-06-30 | 西安电子科技大学 | Radar interference effect evaluation method based on intuitive trapezoidal fuzzy multi-attribute decision |
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