CN110348954A - A kind of complicated technology module partition method of mass customization - Google Patents
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Abstract
The invention discloses a kind of complicated technology module partition methods of mass customization, by the characteristic relation between process corresponding with workpiece, it proposes to face the method that model is divided in process layer, pass through the Fuzzy Correlation matrix between building technical module, conversion solution is carried out to it using Fuzzy Cluster Analysis method, obtain transitive closure matrix, module dendrogram is formed according to different subregions threshold series, by choosing different λ values, obtain different module splitting schemes, logic is close, control is accurate, efficiently, to realize the high-level requirement of the personalized finished product and replenishment of process for producing adaptation market and technique variation rapidly and efficiently.
Description
Technical field
The invention belongs to complicated technology planning technology field, the complicated technology module for being related to a kind of mass customization is drawn
Divide method.
Background technique
Currently, economic fast development and being constantly progressive for technology promote personalized customization technique gradually to win economic market
Favor, for client provide customization service, comprehensively improve client satisfaction, have become one competed between modern enterprise
Kind trend.
The advantage of mass customization production mode combination customized production and mass production is meeting customer personalized demand
While, moreover it is possible to guarantee in low cost and compared with production task is completed in short production cycle, causes manufacture to be looked forward to its unique advantage
The extensive concern of industry.Large-scale customization core technology first is that modularization divides method, in the different function feature of product family, or
On the basis of identical function difference geometrical characteristic, performance characteristic carry out structured analysis, mark off with relatively independent function, knot
Structure, the general module of performance, customized module and personality module, can be fast and efficiently real by the matching combination of intermodule
Now the module of complex product components is divided, produces the handicraft product for meeting customer personalized demand and service.
Modular design method has been widely applied in product family's system module partition at present, but existing division methods
What is more considered is the traditional performance division of product, and there is no the thoughts for incorporating individual demand, and are not suitable for creating product
Complicated technical module.The creation of technical module is compared with the creation of product component module, and need to incorporate is the technique of product
Information, to solve this problem, the present invention design a kind of complicated technology module partition method of mass customization, with technique
Characteristic attribute is that criterion carries out intelligent division to the processing technology of product, to realize the individual demand of client.
Summary of the invention
In view of this, the object of the present invention is to provide one kind towards big rule to solve above-mentioned the deficiencies in the prior art
The complicated technology module partition method of mould customization, logic is close, and control is accurate, efficient, by between process corresponding with workpiece
Characteristic relation, propose to face the method that is divided of model in process layer, pass through the Fuzzy Correlation between building technical module
Matrix carries out conversion solution to it using Fuzzy Cluster Analysis method, transitive closure matrix is obtained, according to different subregions threshold value sequence
Column form module dendrogram, by choosing different λ values, different module splitting schemes are obtained, to realize rapidly and efficiently
Produce the high-level requirement for adapting to the personalized finished product and replenishment of process in market and technique variation.
To achieve the above object, the technical scheme adopted by the invention is that:
A kind of complicated technology module partition method of mass customization, comprising the following steps:
S1: modular character attribute is established by modularization mapping mechanism, and the relevance between technical module is established by attribute, will be closed
Connection property is described as replaceable correlation and disturbance degree correlation；
S2: the interrelated degree between technical module is related to replaceability characteristic, disturbance degree correlation, with Fuzzy Correlation square
The mathematical model of battle array R is indicated: R=ωfF+ωpP；
S3: utilizing Fuzzy Cluster Analysis method, carries out conversion solution to Fuzzy Correlation matrix model, obtains transitive closure matrix；
S31: determining the correlation between each process, determines the interrelated degree between each technical module；
S32: data normalization processing: it is different according to native format, expression format, grade quantizing mode, using translation, very poor change
Swap-in row data standardization, will be in each data compression to [0,1] of Fuzzy Correlation matrix；
S33: establishing fuzzy similarity matrix R': Fuzzy Correlation matrix R with fuzzy similarity matrix R` is equivalent relation, with direct Europe
Furthest Neighbor: r is obtained in several(i,j)=1-c × d (xi,xj), wherein c is the parameter in any constituency, so that 0≤r(i,j)≤ 1, d (xi,
xj) indicate xiWith xjThe distance between:
S34: transitive closure matrix is soughtAccording to theorem, Fuzzy Correlation matrix R is acquired into fuzzy equivalent matrix with quadratic method
That is transitive closure matrixAlso, make
S35: Level Matrix R is soughtλ=λ(i,j): where λ indicates Level Matrix coefficient as metric, to the fuzzy relation between module
Matrix carries out cutting, and the element of λ is more than or equal in Fuzzy Correlation matrix R, and numerical value takes 1, and the numerical value less than λ element takes 0,
The element aggregation that numerical value is 1 in same row or column is same module, and remaining element then individually becomes module, and λ value is bigger, then
Module divides more detailed；By choosing different λ values, different technical module clustering results can be obtained；
S36: by matlab tool and Fuzzy Cluster Analysis method is used, can successively calculate standard according to original incidence matrix
Change matrix, fuzzy similarity matrix, transitive closure matrix, finally Fuzzy Correlation matrix R is cut using λ-Level Matrix, is formed
Module dendrogram:
S4: forming whole module dendrogram according to different subregions threshold series, by choosing different λ values, obtains different
Module splitting scheme.
Further, in the step S2, the method for building up of Fuzzy Correlation matrix, specifically includes the following steps:
A1: using the attribute codomain of output parameter as the relevance judged between each manufacturing procedure, relevance is described as can
Correlation and disturbance degree correlation are replaced, is indicated Fuzzy Correlation matrix R:r with mathematical modeli,j=ωffi,j+ωppi,j；
A2: Fuzzy Correlation matrix R is also referred to as original matrix R, and R meets reflexivity: 0≤r (i, j)≤1, r (i, i)=r (j, j)=
1；And R meets symmetry: r (i, j)=r (j, i), then the mathematical model of Fuzzy Correlation matrix R indicates in step A1 are as follows:
Further, in the step A1, ri,jThe coefficient of total correlation between representation module i and module j, fi,jRepresentation module
Related coefficient between i and module j, pi,jRelated coefficient between representation module j and module i, ωf、ωpCorresponding to characterization
Weight coefficient；ωf、ωp∈ [0,1], and meet: ωf+ωp=1.
Further, in the step A1, the output parameter be grain size, yield strength, elongation percentage, residual stress,
Corrosion resistance, conductivity, hardness, tensile strength.
Further, in the step S32, data normalization handles the standardization formula applied to are as follows:
Wherein, i=1,2 ..., m, x '(i,j)Represent initial data；x'(i,j)man、x'(i,j)minRespectively indicate initial data most
Big value and minimum value；x(i,j)For the data obtained after standardization.
Further, in the step S34, transitive closure matrix is soughtSpecifically includes the following steps:
A1: setting R0For R；
A2: assuming that RiIt has learnt as a result, starting to compare RiWithCorresponding element；IfThen RiIt isTransmitting close
Packet matrix can stop calculating at this time；If be unsatisfactory forIt then continues to execute in next step；
A3: it is calculated by algorithmAnd result is set as Ri+1, execute step A2；
A4: repeating the above steps, and until finally finding out transitive closure matrix, makes
The beneficial effects of the present invention are:
A kind of complicated technology module partition method of mass customization, logic is close, and control is accurate, efficiently, by with work
Characteristic relation between the corresponding process of part proposes to face the method that model is divided in process layer, by constructing technique mould
Fuzzy Correlation matrix between block carries out conversion solution to it using Fuzzy Cluster Analysis method, obtains transitive closure matrix, root
Module dendrogram is formed according to different subregions threshold series, by the different λ value of selection, obtains different module splitting schemes, with
Phase realizes the high-level requirement of the personalized finished product and replenishment of process for producing adaptation market and technique variation rapidly and efficiently；
The present invention is compared with previous product family's module partition method, present invention is primarily based on to the mature product of enterprise and
Analysis to the processing technology of market demand product, in conjunction with technique information is extracted, technique Creative Design and technical module,
Modular process design platform is constructed, by carrying out modularization planning to the Product Process of seriation, with module configuration technology
The similarity mode of module is carried out in conjunction with order demand, and the change for carrying out respective degrees is finally required technique by deformation design module
Shape design, the function of Lai Shixian different product demand.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with
It obtains other drawings based on these drawings.
Fig. 1 is the functional block diagram of modular process domain mapping mechanism of the invention；
Fig. 2 is aluminium in embodiment/Copper plate and rod production process flow chart；
Fig. 3 is the technique process module dendrogram of embodiment.
Specific embodiment
Specific embodiment is given below, to technical solution of the present invention make further it is clear, complete, explain in detail.This
Embodiment is most preferred embodiment based on the technical solution of the present invention, but protection scope of the present invention is not limited to following implementation
Example.
A kind of complicated technology module partition method of mass customization, comprising the following steps:
S1: modular character attribute is established by modularization mapping mechanism, and the relevance between technical module is established by attribute, will be closed
Connection property is described as replaceable correlation and disturbance degree correlation；
S2: the interrelated degree between technical module is related to replaceability characteristic, disturbance degree correlation, with Fuzzy Correlation square
The mathematical model of battle array R is indicated: R=ωfF+ωpP；
S3: utilizing Fuzzy Cluster Analysis method, carries out conversion solution to Fuzzy Correlation matrix model, obtains transitive closure matrix；
S31: determining the correlation between each process, determines the interrelated degree between each technical module；
S32: data normalization processing: it is different according to native format, expression format, grade quantizing mode, using translation, very poor change
Swap-in row data standardization, will be in each data compression to [0,1] of Fuzzy Correlation matrix；
S33: establishing fuzzy similarity matrix R': Fuzzy Correlation matrix R with fuzzy similarity matrix R` is equivalent relation, with direct Europe
Furthest Neighbor: r is obtained in several(i,j)=1-c × d (xi,xj), wherein c is the parameter in any constituency, so that 0≤r(i,j)≤ 1, d (xi,
xj) indicate xiWith xjThe distance between:
S34: transitive closure matrix is soughtAccording to theorem, Fuzzy Correlation matrix R is acquired into fuzzy equivalent matrix with quadratic method
That is transitive closure matrixAlso, make
S35: Level Matrix R is soughtλ=λ(i,j): where λ indicates Level Matrix coefficient as metric, to the fuzzy relation between module
Matrix carries out cutting, and the element of λ is more than or equal in Fuzzy Correlation matrix R, and numerical value takes 1, and the numerical value less than λ element takes 0,
The element aggregation that numerical value is 1 in same row or column is same module, and remaining element then individually becomes module, and λ value is bigger, then
Module divides more detailed；By choosing different λ values, different technical module clustering results can be obtained；
S36: by matlab tool and Fuzzy Cluster Analysis method is used, can successively calculate standard according to original incidence matrix
Change matrix, fuzzy similarity matrix, transitive closure matrix, finally Fuzzy Correlation matrix R is cut using λ-Level Matrix, is formed
Module dendrogram:
S4: forming whole module dendrogram according to different subregions threshold series, by choosing different λ values, obtains different
Module splitting scheme.
Further, in the step S2, the method for building up of Fuzzy Correlation matrix, specifically includes the following steps:
A1: using the attribute codomain of output parameter as the relevance judged between each manufacturing procedure, relevance is described as can
Correlation and disturbance degree correlation are replaced, is indicated Fuzzy Correlation matrix R:r with mathematical modeli,j=ωffi,j+ωppi,j；
A2: Fuzzy Correlation matrix R is also referred to as original matrix R, and R meets reflexivity: 0≤r (i, j)≤1, r (i, i)=r (j, j)=
1；And R meets symmetry: r (i, j)=r (j, i), then the mathematical model of Fuzzy Correlation matrix R indicates in step A1 are as follows:
Further, in the step A1, ri,jThe coefficient of total correlation between representation module i and module j, fi,jRepresentation module
Related coefficient between i and module j, pi,jRelated coefficient between representation module j and module i, ωf、ωpCorresponding to characterization
Weight coefficient；ωf、ωp∈ [0,1], and meet: ωf+ωp=1.
Further, in the step A1, the output parameter be grain size, yield strength, elongation percentage, residual stress,
Corrosion resistance, conductivity, hardness, tensile strength.
Further, in the step S32, data normalization handles the standardization formula applied to are as follows:
Wherein, i=1,2 ..., m, x '(i,j)Represent initial data；x'(i,j)man、x'(i,j)minRespectively indicate initial data most
Big value and minimum value；x(i,j)For the data obtained after standardization.
Further, in the step S34, transitive closure matrix is soughtSpecifically includes the following steps:
A1: setting R0For R；
A2: assuming that RiIt has learnt as a result, starting to compare RiWithCorresponding element；IfThen RiIt isTransmitting close
Packet matrix can stop calculating at this time；If be unsatisfactory forIt then continues to execute in next step；
A3: it is calculated by algorithmAnd result is set as Ri+1, execute step A2；
A4: repeating the above steps, and until finally finding out transitive closure matrix, makes
The specific work process of the present embodiment is as follows:
1, technical module mapping mechanism and technical module are established, as shown in Figure 1, each technical module has one to represent it
The model of body characteristic, and each characteristic model can be indicated with multiple attributes, when the attribute of module has multiple attribute values,
These attribute values can be referred to as variable, the public attribute set that will distinguish example module is defined as class technical module
Variables set P (X), variables set is then to distinguish the difference between same class model, as shown in table 1.According to customer demand to technique
During module is configured, the reason of personalized customization, is the modular model pre-defined inside process modeling library,
Different modules is called independently of each other and with multiple attributes and attribute value according to the different demands of client between these models
It is combined.P (X)={ parti|1≤i≤n} parti={ vali,j| 1≤i≤n, 1≤j≤m }, PartiIndicate inner classes
Between configuration constraint；
1 technical module attribute variable of table and codomain
2, the foundation of modularized design and Fuzzy Correlation matrix model
(1), Fuzzy Correlation matrix R is established
Interdependence between complex product technique process refers to the sides such as feature, performance, the structure between research configuration module
The correlation degree in face.In summary the codomain of attribute variable, with grain size, yield strength, elongation percentage, residual stress, corrosion resistance,
Relevance is described as by the output parameters such as conductivity, hardness, tensile strength as the relevance judged between each manufacturing procedure
Replaceable correlation and disturbance degree correlation, as shown in table 2 and table 3.It is indicated with mathematical model: ri,j=ωffi,j+ωppi,j, wherein ri,jRelated coefficient between representation module i and module j, fi,j、pi,jIt respectively indicates between module i and module j
Related coefficient, ωf、ωpWeight coefficient corresponding to characterization, ωf、ωp∈ [0,1], and meet: ωf+ωp=1, thus
Can between formation process module relationship Fuzzy Correlation matrix R；
The replaceable correlation of table 2
3 disturbance degree correlation of table
By taking aluminium/Copper plate and rod series of products of certain enterprise production as an example, by being investigated on the spot to enterprise, comprehensive production division
Associated production technique provided by personnel, available plate & strip production technique are divided into: founding, hot rolling, cold rolling, heat treatment four
Part.Wherein, -- feeding intake, -- -- stir, skim the concrete technology flow process of founding are as follows: ingredient -- analytical sampling -- adduction gold for fusing
Adjusting component, -- processes such as furnace casting are led in refining -- standing -- for stirring；Hot rolling technology process are as follows: ingot casting -- milling face, milling side -- adds
Hot -- hot rolling (split rolling method) -- aligning, milling face；Cold-rolling process process are as follows: roughing -- annealing -- pickling -- annealing -- finish rolling, most
Latter step heat treatment is finished products, is divided into full annealing and low-temperature annealing, as shown in table 4.Pass through control annealing temperature and guarantor
The warm time obtains the soft state, hard state or half-hard state product of different conditions, size, shape and performance to stabilizing material.Knot
Whole production full-flow process, process are closed, the degree of association between them is analyzed, constructs the interrelated matrix between a process；
Whole process process module of the table 4 based on process route
| Serial number | Process title | Serial number | Process title |
| 1 | It casts bad | 11 | Intermediate annealing |
| 2 | Solution heat treatment | 12 | Pickling |
| 3 | Hot rolling | 13 | Finish rolling |
| 4 | Shearing | 14 | Finished products |
| 5 | The milling face of aligning | 15 | Clean finished product |
| 6 | Homogenizing annealing | 16 | Shear slitting |
| 7 | Roughing | 17 | It checks and accepts |
| 8 | Intermediate annealing | 18 | Packaging |
| 9 | Pickling | 19 | Storage |
| 10 | Pre- finish rolling |
(2), based on the module partition method of Fuzzy Cluster Analysis method
1) data normalization is handled: the native format of data information differs widely, wherein including description type, numeric type, selection type
Etc. other informations expression format not can be used directly since these data levels quantification manners are different in operation, need to its into
Row standardization is convenient for subsequent algorithm operation in each data compression to [0,1] of fuzzy matrix；
2) fuzzy similarity matrix R ' is established: by the available fuzzy matrix of above-mentioned property list, fuzzy matrix and fuzzy similarity matrix
For equivalent relation, with direct Euclidean distance method: r(i, j)=1-c × d (xi, xj), wherein c is the parameter in any constituency,
So that 0≤r(i, j)≤ 1, d (xi, xj) indicate xiWith xjThe distance betweenWith above-mentioned module partition method,
Evaluation and test marking is carried out by expert, determines the correlation between each process, as slab and solution heat treatment process are closely accepted
Two procedures, and the time interval acted is smaller, then taking disturbance degree correlation is 0.8, and replaceable correlation is 0.5.According to
It is 0.55 and 0.45 that formula takes weight coefficient respectively.Be computed, the two correlation be 0.665, similarly in the case of solution heat treatment
Disturbance degree correlation and replaceable correlation between hot rolling take 0.4 and 0.1 respectively, then the two overall relevancy is 0.265.According to
The secondary correlation calculated between other each process, as shown in table 5.The mould formed after correlation analysis and relatedness computation
Paste incidence matrix R；
5 master operation module Fuzzy Correlation matrix R of table
3) transitive closure matrix is soughtAccording to resulting Fuzzy Correlation matrix R is demarcated, an only fuzzy similarity matrix R ', such as
Shown in table 6, there is reflexivity and symmetry, but not necessarily have transitivity, i.e., R is not necessarily fuzzy equivalent matrix, it is also necessary to
Fuzzy equivalent matrix R is transformed into itk, according to theorem, transitive closure matrix is sought with quadratic methodIt is exactly required fuzzy equivalence
Matrix Rk, as shown in table 7；
The fuzzy similarity matrix R ' of relationship between 6 process module of table
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | |
| 1 | 1 | 0.806 | 0.622 | 0.522 | 0.570 | 0.575 | 0.470 | 0.497 | 0.428 | 0.456 | 0.521 | 0.448 | 0.508 | 0.536 | 0.463 | 0.471 |
| 2 | 0.806 | 1 | 0.621 | 0.523 | 0.557 | 0.658 | 0.517 | 0.561 | 0.398 | 0.520 | 0.596 | 0.414 | 0.516 | 0.610 | 0.412 | 0.480 |
| 3 | 0.622 | 0.621 | 1 | 0.726 | 0.753 | 0.786 | 0.558 | 0.508 | 0.398 | 0.529 | 0.475 | 0.409 | 0.517 | 0.478 | 0.369 | 0.528 |
| 4 | 0.522 | 0.523 | 0.726 | 1 | 0.869 | 0.635 | 0.527 | 0.406 | 0.383 | 0.517 | 0.400 | 0.372 | 0.535 | 0.426 | 0.410 | 0.552 |
| 5 | 0.570 | 0.557 | 0.753 | 0.869 | 1 | 0.671 | 0.539 | 0.453 | 0.405 | 0.536 | 0.445 | 0.406 | 0.550 | 0.455 | 0.416 | 0.566 |
| 6 | 0.575 | 0.658 | 0.786 | 0.635 | 0.671 | 1 | 0.625 | 0.614 | 0.445 | 0.596 | 0.569 | 0.404 | 0.544 | 0.512 | 0.344 | 0.526 |
| 7 | 0.470 | 0.517 | 0.558 | 0.527 | 0.539 | 0.625 | 1 | 0.740 | 0.657 | 0.788 | 0.661 | 0.559 | 0.558 | 0.491 | 0.422 | 0.560 |
| 8 | 0.497 | 0.561 | 0.508 | 0.406 | 0.453 | 0.614 | 0.740 | 1 | 0.593 | 0.702 | 0.791 | 0.538 | 0.497 | 0.543 | 0.377 | 0.463 |
| 9 | 0.428 | 0.398 | 0.398 | 0.383 | 0.405 | 0.445 | 0.657 | 0.593 | 1 | 0.661 | 0.580 | 0.837 | 0.490 | 0.441 | 0.604 | 0.500 |
| 10 | 0.456 | 0.520 | 0.529 | 0.517 | 0.536 | 0.596 | 0.788 | 0.702 | 0.661 | 1 | 0.657 | 0.591 | 0.606 | 0.513 | 0.470 | 0.615 |
| 11 | 0.521 | 0.596 | 0.475 | 0.400 | 0.445 | 0.569 | 0.661 | 0.791 | 0.580 | 0.657 | 1 | 0.545 | 0.575 | 0.655 | 0.450 | 0.531 |
| 12 | 0.448 | 0.414 | 0.409 | 0.372 | 0.406 | 0.404 | 0.559 | 0.538 | 0.837 | 0.591 | 0.545 | 1 | 0.507 | 0.491 | 0.680 | 0.540 |
| 13 | 0.508 | 0.516 | 0.517 | 0.535 | 0.550 | 0.544 | 0.558 | 0.497 | 0.490 | 0.606 | 0.575 | 0.507 | 1 | 0.711 | 0.597 | 0.777 |
| 14 | 0.536 | 0.610 | 0.478 | 0.426 | 0.455 | 0.512 | 0.491 | 0.543 | 0.441 | 0.513 | 0.655 | 0.491 | 0.711 | 1 | 0.583 | 0.661 |
| 15 | 0.463 | 0.412 | 0.369 | 0.410 | 0.416 | 0.344 | 0.422 | 0.377 | 0.604 | 0.470 | 0.450 | 0.680 | 0.597 | 0.583 | 1 | 0.663 |
| 16 | 0.471 | 0.480 | 0.528 | 0.552 | 0.566 | 0.526 | 0.560 | 0.463 | 0.500 | 0.615 | 0.531 | 0.540 | 0.777 | 0.661 | 0.663 | 1 |
7 transitive closure matrix of table
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | |
| 1 | 1 | 0.80 | 0.62 | 0.52 | 0.57 | 0.57 | 0.47 | 0.49 | 0.42 | 0.45 | 0.52 | 0.44 | 0.50 | 0.53 | 0.46 | 0.47 |
| 2 | 0.80 | 1 | 0.62 | 0.52 | 0.55 | 0.65 | 0.51 | 0.56 | 0.39 | 0.52 | 0.59 | 0.41 | 0.51 | 0.61 | 0.41 | 0.48 |
| 3 | 0.62 | 0.62 | 1 | 0.72 | 0.75 | 0.78 | 0.55 | 0.50 | 0.39 | 0.52 | 0.47 | 0.40 | 0.51 | 0.47 | 0.36 | 0.52 |
| 4 | 0.52 | 0.52 | 0.72 | 1 | 0.86 | 0.63 | 0.52 | 0.40 | 0.38 | 0.51 | 0.40 | 0.37 | 0.53 | 0.42 | 0.41 | 0.55 |
| 5 | 0.57 | 0.55 | 0.75 | 0.86 | 1 | 0.67 | 0.53 | 0.45 | 0.40 | 0.53 | 0.44 | 0.40 | 0.55 | 0.45 | 0.41 | 0.56 |
| 6 | 0.57 | 0.65 | 0.78 | 0.63 | 0.67 | 1 | 0.62 | 0.61 | 0.44 | 0.59 | 0.56 | 0.40 | 0.54 | 0.51 | 0.34 | 0.52 |
| 7 | 0.47 | 0.51 | 0.55 | 0.52 | 0.53 | 0.62 | 1 | 0.74 | 0.65 | 0.78 | 0.66 | 0.55 | 0.55 | 0.49 | 0.42 | 0.56 |
| 8 | 0.49 | 0.56 | 0.50 | 0.40 | 0.45 | 0.61 | 0.74 | 1 | 0.59 | 0.70 | 0.79 | 0.53 | 0.49 | 0.54 | 0.37 | 0.46 |
| 9 | 0.42 | 0.39 | 0.39 | 0.38 | 0.40 | 0.44 | 0.65 | 0.59 | 1 | 0.66 | 0.58 | 0.83 | 0.49 | 0.44 | 0.60 | 0.50 |
| 10 | 0.45 | 0.52 | 0.52 | 0.51 | 0.53 | 0.59 | 0.78 | 0.70 | 0.66 | 1 | 0.65 | 0.59 | 0.60 | 0.51 | 0.47 | 0.61 |
| 11 | 0.52 | 0.59 | 0.47 | 0.40 | 0.44 | 0.56 | 0.66 | 0.79 | 0.58 | 0.65 | 1 | 0.54 | 0.57 | 0.65 | 0.45 | 0.53 |
| 12 | 0.44 | 0.41 | 0.40 | 0.37 | 0.40 | 0.40 | 0.55 | 0.53 | 0.83 | 0.59 | 0.54 | 1 | 0.50 | 0.49 | 0.68 | 0.54 |
| 13 | 0.50 | 0.51 | 0.51 | 0.53 | 0.55 | 0.54 | 0.55 | 0.49 | 0.49 | 0.60 | 0.57 | 0.50 | 1 | 0.71 | 0.59 | 0.77 |
| 14 | 0.53 | 0.61 | 0.47 | 0.42 | 0.45 | 0.51 | 0.49 | 0.54 | 0.44 | 0.51 | 0.65 | 0.49 | 0.71 | 1 | 0.58 | 0.66 |
| 15 | 0.46 | 0.41 | 0.36 | 0.41 | 0.41 | 0.34 | 0.42 | 0.37 | 0.60 | 0.47 | 0.45 | 0.68 | 0.59 | 0.58 | 1 | 0.66 |
| 16 | 0.47 | 0.48 | 0.52 | 0.55 | 0.56 | 0.52 | 0.56 | 0.46 | 0.50 | 0.61 | 0.53 | 0.54 | 0.77 | 0.66 | 0.66 | 1 |
4) Level Matrix R is soughtλ=λ(i, j): where λ indicates Level Matrix coefficient as metric, to the fuzzy relation square between module
Battle array carries out cutting, and the element of λ is more than or equal in fuzzy relationship matrix r, and numerical value takes 1, and the numerical value less than λ element takes 0,
The element aggregation that numerical value is 1 in same row or column is same module, and remaining element then individually becomes module, and λ value is bigger, then mould
Block divides more detailed；By choosing different λ values, different technical module clustering results can be obtained；By
Matlab tool with Fuzzy Cluster Analysis Algorithm according to original incidence matrix can successively calculate normalized matrix, obscure it is similar
Matrix, transitive closure matrix.Dynamic clustering module can be formed after finally cutting using λ-Level Matrix to R, as shown in Figure 3.
Descending threshold value partition sequence: { 10.8477 0.8014 0.7823 0.7709 0.7474 0.7274 0.7167
0.7121 0.6893 0.6819 0.6625 0.649 0.6431 0.6092 0.5865 }, full-flow process process is divided
For different technology categories.As λ > 0.6625, the module number divided is excessive and deviates actual conditions.And when λ=
When 0.6625, all technique process modules can mainly be divided into 4 major class, respectively founding module, hot rolling module, cold rolling module,
Heat treatment module.Cleaning module can be divided individually as auxiliary process module, almost the same with actual conditions.
In conclusion a kind of complicated technology module partition method of mass customization, logic is close, control is accurate,
Efficiently, it by the characteristic relation between process corresponding with workpiece, proposes to face the method that model is divided in process layer, lead to
The Fuzzy Correlation matrix between building technical module is crossed, conversion solution is carried out to it using Fuzzy Cluster Analysis method, is passed
Closure Matrix is passed, module dendrogram is formed according to different subregions threshold series, by choosing different λ values, obtains different moulds
Block splitting scheme, to realize the personalized finished product and replenishment of process for producing adaptation market and technique variation rapidly and efficiently
High-level requirement；
The present invention is compared with previous product family's module partition method, present invention is primarily based on to the mature product of enterprise and
Analysis to the processing technology of market demand product, in conjunction with technique information is extracted, technique Creative Design and technical module,
Modular process design platform is constructed, by carrying out modularization planning to the Product Process of seriation, with module configuration technology
The similarity mode of module is carried out in conjunction with order demand, and the change for carrying out respective degrees is finally required technique by deformation design module
Shape design, the function of Lai Shixian different product demand.
Main feature of the invention, basic principle and advantages of the present invention has been shown and described above.Industry technology
Personnel are it should be appreciated that the present invention is not limited to the above embodiments, and the above embodiments and description only describe this
The principle of invention, without departing from the spirit and scope of the present invention, the present invention can also have various change according to the actual situation
And improvement, these changes and improvements all fall within the protetion scope of the claimed invention.The claimed scope of the invention is by appended
Claims and its equivalent thereof.
Claims (6)
1. a kind of complicated technology module partition method of mass customization, it is characterised in that: the following steps are included:
S1: modular character attribute is established by modularization mapping mechanism, and the relevance between technical module is established by attribute, will be closed
Connection property is described as replaceable correlation and disturbance degree correlation；
S2: the interrelated degree between technical module is related to replaceability characteristic, disturbance degree correlation, with Fuzzy Correlation square
The mathematical model of battle array R is indicated: R=ωfF+ωpP；
S3: utilizing Fuzzy Cluster Analysis method, carries out conversion solution to Fuzzy Correlation matrix model, obtains transitive closure matrix；
S31: determining the correlation between each process, determines the interrelated degree between each technical module；
S32: data normalization processing: it is different according to native format, expression format, grade quantizing mode, using translation, very poor change
Swap-in row data standardization, will be in each data compression to [0,1] of Fuzzy Correlation matrix；
S33: establishing fuzzy similarity matrix R': Fuzzy Correlation matrix R with fuzzy similarity matrix R` is equivalent relation, with direct Europe
Furthest Neighbor: r is obtained in several(i,j)=1-c × d (xi,xj), wherein c is the parameter in any constituency, so that 0≤r(i,j)≤ 1, d (xi,
xj) indicate xiWith xjThe distance between:
S34: transitive closure matrix is soughtAccording to theorem, Fuzzy Correlation matrix R is acquired into fuzzy equivalent matrix with quadratic method
That is transitive closure matrixAlso, make
S35: Level Matrix R is soughtλ=λ(i,j): where λ indicates Level Matrix coefficient as metric, to the fuzzy relation between module
Matrix carries out cutting, and the element of λ is more than or equal in Fuzzy Correlation matrix R, and numerical value takes 1, and the numerical value less than λ element takes 0,
The element aggregation that numerical value is 1 in same row or column is same module, and remaining element then individually becomes module, and λ value is bigger, then
Module divides more detailed；By choosing different λ values, different technical module clustering results can be obtained；
S36: by matlab tool and Fuzzy Cluster Analysis method is used, can successively calculate standard according to original incidence matrix
Change matrix, fuzzy similarity matrix, transitive closure matrix, finally Fuzzy Correlation matrix R is cut using λ-Level Matrix, is formed
Module dendrogram:
S4: forming whole module dendrogram according to different subregions threshold series, by choosing different λ values, obtains different
Module splitting scheme.
2. a kind of complicated technology module partition method of mass customization according to claim 1, it is characterised in that:
In the step S2, the method for building up of Fuzzy Correlation matrix, specifically includes the following steps:
A1: using the attribute codomain of output parameter as the relevance judged between each manufacturing procedure, relevance is described as can
Correlation and disturbance degree correlation are replaced, is indicated Fuzzy Correlation matrix R:r with mathematical modeli,j=ωffi,j+ωppi,j；
A2: Fuzzy Correlation matrix R is also referred to as original matrix R, and R meets reflexivity: 0≤r (i, j)≤1, r (i, i)=r (j, j)=
1；And R meets symmetry: r (i, j)=r (j, i), then the mathematical model of Fuzzy Correlation matrix R indicates in step A1 are as follows:
3. a kind of complicated technology module partition method of mass customization according to claim 2, it is characterised in that:
In the step A1, ri,jThe coefficient of total correlation between representation module i and module j, fi,jPhase between representation module i and module j
Relationship number, pi,jRelated coefficient between representation module j and module i, ωf、ωpWeight coefficient corresponding to characterization；ωf、
ωp∈ [0,1], and meet: ωf+ωp=1.
4. a kind of complicated technology module partition method of mass customization according to claim 2, it is characterised in that:
In the step A1, the output parameter is grain size, yield strength, elongation percentage, residual stress, corrosion resistance, conductivity, hard
Degree, tensile strength.
5. a kind of complicated technology module partition method of mass customization according to claim 1, it is characterised in that:
In the step S32, data normalization handles the standardization formula applied to are as follows:
Wherein, i=1,2 ..., m, x '(i,j)Represent initial data；x′(i,j)man、x′(i,j)minRespectively indicate initial data most
Big value and minimum value；x(i,j)For the data obtained after standardization.
6. a kind of complicated technology module partition method of mass customization according to claim 1, it is characterised in that:
In the step S34, transitive closure matrix is soughtSpecifically includes the following steps:
A1: setting R0For R；
A2: assuming that RiIt has learnt as a result, starting to compare RiWithCorresponding element；IfThen RiIt isTransitive closure
Matrix can stop calculating at this time；If be unsatisfactory forIt then continues to execute in next step；
A3: it is calculated by algorithmAnd result is set as Ri+1, execute step A2；
A4: repeating the above steps, and until finally finding out transitive closure matrix, makes
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