CN103729525A - Gear hobbing method - Google Patents
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- CN103729525A CN103729525A CN201410038194.4A CN201410038194A CN103729525A CN 103729525 A CN103729525 A CN 103729525A CN 201410038194 A CN201410038194 A CN 201410038194A CN 103729525 A CN103729525 A CN 103729525A
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Abstract
The invention discloses a gear hobbing method which is characterized in that during hobbing, optimization decision on gear hobbing process parameters comprises the following specific steps: (1) building a gear hobbing process ontology base; (2) expressing a gear hobbing process parameter decision target space; and (3) performing comprehensive optimization decision on the gear hobbing process parameters. The method disclosed by the invention has the advantages that by using the gear hobbing process domain ontology base stored in a database, the method can realize sharing and reuse of the knowledge about the domain of the gear hobbing, and not only can consider the gear hobbing process parameters as a system but also can solve new optimization decision problems of the gear hobbing process parameters by combining case-based reasoning with analytic hierarchy process during use.
Description
Technical field
The present invention relates to gear machining technology, especially relate to the job operation that in a kind of gear hobbing process, technological parameter is optimized decision-making.
Background technology
The choose reasonable of gear hobbing working process parameter and Optimal Decision-making are significant for improving gear hobbing processing quality and working (machining) efficiency.It is empirical method or test method(s) that the technological parameter extensively adopting is at present drafted method: for production in enormous quantities, the normal processing experience according to various workshop manuals and oneself accumulation of technologist completes the formulation of technological parameter; For the many variety production of short run, need determine in advance feasible technological parameter according to technologist's technique experience, and obtain the technological parameter that can meet processing request in conjunction with the trial cut of certain number of times.But existing technological parameter decision-making technique exists following not enough: the Unified Expression that 1. lacks unified gear hobbing processing technology knowledge; 2. the gear hobbing processing technology cycle is long; 3. do not consider crudy, process time, processing cost, resource consumption, the factors such as environmental impact.
The existing disposal route to gear hobbing working process parameter Optimal Decision-making problem mainly comprises: (1) various optimized algorithms based on expert system; (1) Case-Based Reasoning.When the various optimized algorithms based on expert system are processed gear hobbing working process parameter Optimal Decision-making problem, by genetic algorithm, artificial neural network scheduling algorithm, automatically complete process parameter optimizing, but because algorithm has unpredictability, cause the result of decision unstable and not reusable, lack unified Process Knowledge Representation.Case-Based Reasoning is from historical processing instance angle, by retrieving, reuse, revise, the step such as reservation solves new problem, but using case similarity as unique index, lack to consider the processing effect of craft embodiment, making the process program institute adopting process parameter of Optimum Matching example be applied to its processing effect when decision-making technological problems may be undesirable.
Summary of the invention
For the deficiencies in the prior art, technical matters to be solved by this invention is, how be provided a kind of gear hobbing process time of can shortening, improve the hobbing method for processing of gear hobbing processing effect, it can realize the Optimal Decision-making of working process parameter, realizes the Unified Expression of gear hobbing processing technology knowledge, and analytical hierarchy process and case-based reasoning are combined, to reach, shorten process time, improve the object of processing effect.
In order to solve the problems of the technologies described above, in the present invention, adopted following technical scheme:
A hobbing method for processing, is characterized in that, gear hobbing adds man-hour, carries out the Optimal Decision-making of gear hobbing working process parameter according to following steps, and concrete steps are:
(1) realize the structure of gear hobbing processing technology ontology library; First, by gear hobbing processing technology knowledge abstraction, be gear hobbing processing technology domain body, according to similarity feature, knowledge in gear hobbing processing technology field is divided into groups, be respectively five groups of concepts, relation, attribute, rule and instance; Secondly, according to the grouping of gear hobbing processing technology domain knowledge, conceptual knowledge is integrated into gear hobbing processing technology field concept body tree by top down method; Again, the abstract model according to formed gear hobbing processing technology field concept body tree, utilizes ontology theory, analyzes the contact between gear hobbing processing technology conceptual knowledge, and abstract is to be related to knowledge, to concept be related to that knowledge carries out attributes extraction and Rule Extraction; Finally, the form storage with database, completes the knowledge representation of gear hobbing processing technology, forms ontology library, and it comprises conceptual base, is related to storehouse, attribute library, rule base and case library; According to BNF normal form (Backus normal form (BNF)), according to knowledge such as the relation of the conceptual knowledge in gear hobbing processing technology field concept body tree and extraction before, attribute, rules, form respectively conceptual base, be related to storehouse, attribute library, rule base; By whole gear hobbing processing technology knowledge representation process instance, form case library;
(2) realize the expression in gear hobbing working process parameter decision objective space; First use analytical hierarchy process to build gear hobbing working process parameter decision model, gear hobbing working process parameter decision objective space is divided into three levels: destination layer, evaluation layer and solution layer; Wherein in the attribute of solution layer, belong to the attribute of hobboing cutter basic parameter, by the mode of man-machine interaction, decision maker equates the relevant knowledge of rule search ontology library according to modulus, obtain solution layer decision scheme matrix A; In attribute in solution layer, do not belong to the attribute of hobboing cutter basic parameter, utilize Case-Based Reasoning that the similarity of decision objective space and historical processing instance is calculated, to select the example mating with solution layer numerical attribute, obtain solution layer decision scheme matrix B; (decision matrix A, B be many row often, and the line number of supposing A is g, and the line number of B is k, and the assembled scheme of solution layer decision-making just has g * k so; )
(3) realize the complex optimum decision-making of gear hobbing working process parameter; First gear hobbing working process parameter decision model is set up to judgment matrix, certain key element of last layer will be compared between all key elements of this level between two; Then, carry out Mode of Level Simple Sequence, according to judgment matrix, by calculating relative Link Importance, each key element of this level is carried out to importance sorting with respect to certain key element of last layer; Then, carry out consistency check, judge the confidence level of each scheme relative Link Importance; Finally, carry out level and always sort, can be from destination layer, obtain on current layer each key element for last layer comprehensive importance degree generally speaking top-downly; What importance degree was the highest is gear hobbing working process parameter Optimal Decision-making scheme.
As optimization, above-mentioned gear hobbing processing technology domain body is defined as follows:
Gear hobbing processing technology domain body is that a kind of detailed characterization of the concept to existing in gear hobbing processing technology field is described, being that gear hobbing processing technology domain body is a kind of description to the concept in gear hobbing processing technology field, relation, attribute, rule and instance five elements, is to realize the basis that domain knowledge is shared and reused; Wherein concept refers to normalized, generally acknowledged term in gear hobbing processing technology field, is the set with same alike result or object of action; It,, except referring to concept in general sense, also comprises task, function and the behavior of gear hobbing processing technology aspect; Relation refers to connection or the association between field concept; Relation is present between a plurality of concepts; This form with concept in the process of generalities of relation exists, and between relation, can form new relation; (relation between concept mainly contains Is-a relation, A-kind-of relation and A-part-of relation).
As optimization, described BNF normal form is the formalized description to described gear hobbing processing technology domain body definition, is the representation of knowledge of domain body, is also the basis that body builds; Its BNF normal form is as follows:
(1) < gear hobbing processing technology domain body >::=(< domain name >, < concept >, < is related to >, < attribute >, < Regulation G reatT.GreaT.GT, < example >);
(2) < concept >::=(< concept >, < concept title >, [< synonym >], [< initialism >], < conceptual description >, [father's class-mark > of this concept of <], [domain name > under <]);
(3) < is related to >::=(< relation >, < is related to title >, < is related to former piece >, < is related to consequent >, < relationship description >);
(4) < attribute >::=(< attribute >, the categorical conception > of < institute, < relation >, < Property Name >, [< synonym >], [< initialism >], the type > of < value, the scope > of < collection),
(5) < Regulation G reatT.GreaT.GT::=(< rule >, the categorical conception > of < institute, < relation >, < rule description >, < confidence level >);
(6) < example >::=(< instance number >, < instance name >, < problem is described >, < instance properties >, < instance properties value >, < example rule >);
(7) < domain name >::=< indications >.
As optimization, adopt top down method to set up the concept classification level of gear hobbing processing technology domain body, from gear hobbing processing technology field, maximum concept starts, and by adding subclass by these concept refinements, then uses field concept body tree representation.
As optimization, the destination layer in described step (2) is gear hobbing working process parameter Optimal Decision-making; The key element of evaluating layer comprises crudy, process time, processing cost, resource consumption and environmental impact; Solution layer is scheme 1, scheme 2,, scheme num, num represents natural number, its attribute comprises hobboing cutter precision, hobboing cutter head number, hobboing cutter helix angle, hobboing cutter rotating speed, feed number of times, axial feed velocity, radial feed speed, workpiece rotational frequency and alter cutter amount.
As optimization, the concrete steps that solution layer decision-making assembled scheme in described step (2) generates comprise,
Step1 decision maker equates regular according to modulus and retrieves in ontology library, selects the g of coupling hobboing cutter, extracts hobboing cutter precision out, and hobboing cutter head number, three parameters of hobboing cutter helix angle, obtain solution layer decision scheme matrix A (g, 3);
Step2 utilizes the method for case-based reasoning, construction meta-model
Wherein, N
0represent that decision objective name in a name space claims, p
i 0, (i=1,2 ..., n) represent not belong in solution layer the attribute of hobboing cutter basic parameter, be matter-element feature, v
i 0, (i=1,2 ..., n) be N
0about p
i 0(i=1,2 ..., value two tuples n), are expressed as v
i 0=<v
il 0, v
ih 0>, (i=1,2 ..., n), v
il 0, v
ih 0represent p
i 0(i=1,2 ..., optimization n) is interval; (n represents natural number)
Step3 utilizes similarity formula:
calculate historical processing instance in r case library and optimize interval similarity about i matter-element feature and decision objective space i; The interval of similarity value is [0,1], is worth larger expression similarity higher;
Step4 adds weight w to each matter-element feature
i, (i=1,2 ..., n), calculate comprehensive similarity
definite threshold κ, selects the higher example of similarity, extracts hobboing cutter rotating speed, feed number of times, axial feed velocity, radial feed speed, workpiece rotational frequency out, alters six parameters of cutter amount, obtains solution layer decision scheme matrix B (k, 6);
Step5, by solution layer decision scheme matrix A (g, 3) and B (k, 6) combination, forms g * k kind solution layer decision-making assembled scheme.
As optimization, the complex optimum decision-making concrete steps of the gear hobbing working process parameter in described step (3) comprise,
Step1 sets and is compared a layer EVA0 by name, being compared layer key element is ELE0, relatively layer is called EVA1, relatively layer key element is ELE1, EVA0=destination layer, ELE0=gear hobbing working process parameter Optimal Decision-making, EVA1=evaluates layer, ELE1=crudy, process time, processing cost, resource consumption, environmental impact; Judgement symbol ELEFlag=0;
Step2, according to judgement yardstick table, will compare between the ELE1 of EVA1 between two for the ELE0 attribute of EVA0, obtains the judgment matrix of EVA1 to EVA0
(i, j=1,2 ..., N); Wherein, a
ijfor in EVA1 for the fiducial value of i key element to j key element of certain key element in EVA0, a
ji=1/a
ij, (i ≠ j), N is the number of key element in EVA1, is the number of attribute in ELE1;
Step3 is according to judgment matrix H
s, calculate H
sthe product of every a line various element
i, j=1,2 ..., N, then, calculates M
inth power root
then, right
be normalized, obtain each component of the proper vector of judgment matrix
evaluate layer about H
srelative Link Importance be (W
1, W
2..., W
n), last, calculate H
smaximum characteristic root
(AW)
i=H
sthe capable data of i and the sum of products of W respective items; Obtain the hierarchical ranking table of EVA1 to EVA0 key element;
Step4 carries out consistency check according to CR=CI/RI, wherein CI=(λ
max-N)/(N-1), work as H
swhile thering is crash consistency, CI=0; RI is H
smean random index, works as CR<0.1, thinks that judgment matrix has satisfactory consistency, and the relative Link Importance of calculating is also acceptable, otherwise revision judgment matrix, proceeds to Step2;
If Step5 is ELEFlag=0, EVA0=evaluates layer so, ELE0=crudy, and EVA1=solution layer, ELE1=scheme 1, scheme 2,, scheme num, ELEFlag=ELEFlag+1, proceeds to Step2;
If Step6 is ELEFlag=1, so ELE0=process time, ELEFlag=ELEFlag+1, proceeds to Step2;
If Step7 is ELEFlag=2, ELE0=processing cost so, ELEFlag=ELEFlag+1, proceeds to Step2;
If Step8 is ELEFlag=3, ELE0=resource consumption so, ELEFlag=ELEFlag+1, proceeds to Step2;
If Step9 is ELEFlag=4, ELE0=environmental impact so, ELEFlag=ELEFlag+1, proceeds to Step2;
Step10, from destination layer, obtains each key element on current layer for last layer comprehensive importance degree time generally speaking top-downly, and its computing formula is:
j=1,2 ..., num, ap
ito evaluate each key element of layer about the relative Link Importance of destination layer key element, bp
j ibe each key element of solution layer about evaluating the relative Link Importance of certain key element of layer, all try to achieve; Obtaining solution layer to the total sequencing table of the level of destination layer, select the scheme that importance degree is the highest, is the complex optimum result of decision of gear hobbing working process parameter.
In the present invention, with the gear hobbing processing technology field ontology library of database storage, can realize sharing and reusing of this domain knowledge, by case-based reasoning and analytical hierarchy process in conjunction with utilization, gear hobbing working process parameter can be considered as a system, can be solved again new gear hobbing process parameter optimizing decision problem.Roller job operation of the present invention, gear hobbing working process parameter Optimal Decision-making based on a kind of uniqueness and realizing, in this gear hobbing working process parameter Optimal Decision-making, catch the feature of gear hobbing processing technology domain body, conceptual, systematicness, knowledge delamination empirical and Process Character are classified, and analysis relation, extracts attribute and rule, finally form conceptual base, be related to storehouse, attribute library, rule base and case library, be gear hobbing processing technology Domain and ontology knowledge and express; Use analytical hierarchy process to express gear hobbing working process parameter decision objective space, build gear hobbing working process parameter decision model: destination layer, evaluate layer, solution layer, utilize the method for ontology library and case-based reasoning, the assembled scheme of constructing plan layer; Using analytical hierarchy process to calculate decision model, obtain the scheme that importance degree is the highest, is gear hobbing working process parameter Optimal Decision-making scheme; According to optimizing decision scheme, carry out gear hobbing processing, can reach and improve gear hobbing working (machining) efficiency, improve the effect of gear hobbing crudy.
In addition, the present invention also has following technique effect: 1, support knowledge sharing: gear hobbing processing technology be long-term, take experimental knowledge as main field, its key concept can often not change, and this Core Set of Concepts is the basis of realizing knowledge sharing and interoperability.2, support knowledge reuse: gear hobbing processing technology domain body can be safeguarded and expand, and just makes the follow-up work (process parameter optimizing decision-making, numerical control program establishment etc.) towards this field just need not start all over again from the beginning, and has reduced the R&D cycle.3, multi-method combination: case-based reasoning utilizes historical processing instance and experience to solve new problem, the method not only suits the thought process that the mankind deal with problems, and overcome the difficult problem of general intelligence decision system aspect knowledge acquisition, but it take case similarity as unique index, lack systematicness; Analytical hierarchy process adopts the mode of quantitative and qualitative analysis combination, carry out systematic analysis decision, but it can not produce new decision scheme.These two kinds of methods are effectively combined, gear hobbing working process parameter can be considered as a system, can solve again new gear hobbing process parameter optimizing decision problem, thereby solve the less difficult problem of existing technological parameter decision-making technique Consideration.
Accompanying drawing explanation
Fig. 1 is the schematic diagram of the structure of gear hobbing processing technology domain body in the specific embodiment of the invention;
Fig. 2 is the schematic diagram of gear hobbing processing technology domain body tree in the specific embodiment of the invention;
Fig. 3 is the schematic diagram of gear-hobbing clamp installation check in the specific embodiment of the invention;
Fig. 4 is the schematic diagram of the anabolic process of gear hobbing working process parameter decisional model plan layer decision scheme in the specific embodiment of the invention;
Fig. 5 is the schematic diagram of the complex optimum decision process of gear hobbing working process parameter in the specific embodiment of the invention.
Embodiment
Below in conjunction with accompanying drawing, the present invention is described in further detail.
Thinking of the present invention is: utilize ontology theory, analyze gear hobbing processing technology field, this field is defined, form conceptual base, be related to storehouse, attribute library, rule base and case library, complete gear hobbing processing technology Domain and ontology knowledge and express; Use analytical hierarchy process to express gear hobbing working process parameter decision objective space, build gear hobbing working process parameter decision model: destination layer, evaluation layer, solution layer, utilize ontology library and case-based reasoning, the assembled scheme of constructing plan layer; Using analytical hierarchy process to calculate decision model, obtain the scheme that importance degree is the highest, is gear hobbing working process parameter Optimal Decision-making scheme.
Below in conjunction with accompanying drawing and case study on implementation, the invention will be further described:
A kind of hobbing method for processing of the present invention, in this method, gear hobbing adds man-hour, carries out the Optimal Decision-making of gear hobbing working process parameter according to following steps, as Figure 1-Figure 5, comprises following concrete steps:
(1) structure of gear hobbing processing technology ontology library-first, by gear hobbing processing technology knowledge abstraction, it is gear hobbing processing technology domain body, according to similarity feature, knowledge in gear hobbing processing technology field is divided into groups, be respectively five groups of concepts, relation, attribute, rule and instance; Secondly, according to the grouping of gear hobbing processing technology domain knowledge, conceptual knowledge is integrated into gear hobbing processing technology field concept body tree by top down method; Again, the abstract model according to formed gear hobbing processing technology field concept body tree, utilizes ontology theory, analyzes the contact between gear hobbing processing technology conceptual knowledge, and abstract is to be related to knowledge, to concept be related to that knowledge carries out attributes extraction and Rule Extraction; Finally, form storage with database, complete the knowledge representation of gear hobbing processing technology, form ontology library, it comprises conceptual base, be related to storehouse, attribute library, rule base and case library: according to BNF normal form, according to knowledge such as the relation of the conceptual knowledge in gear hobbing processing technology field concept body tree and extraction before, attribute, rules, form respectively conceptual base, be related to storehouse, attribute library, rule base; By whole gear hobbing processing technology knowledge representation process instance, form case library.
(2) expression-utilization analytical hierarchy process in gear hobbing working process parameter decision objective space builds gear hobbing working process parameter decision model, and gear hobbing working process parameter decision objective space is divided into three levels: destination layer, evaluation layer, solution layer.Destination layer: gear hobbing working process parameter Optimal Decision-making; Evaluate layer: crudy, process time, processing cost, resource consumption, environmental impact; Solution layer: hobboing cutter precision, hobboing cutter head number, hobboing cutter helix angle, hobboing cutter rotating speed, feed number of times, axial feed velocity, radial feed speed, workpiece rotational frequency, alter cutter amount.Attribute in solution layer belongs to hobboing cutter basic parameter, and by the mode of man-machine interaction, decision maker equates the relevant knowledge of rule search ontology library according to modulus, obtain solution layer decision scheme matrix A; Attribute in solution layer does not belong to hobboing cutter basic parameter, utilizes Case-Based Reasoning that the similarity of decision objective space and historical processing instance is calculated, and to select the example mating with solution layer numerical attribute, obtains solution layer decision scheme matrix B.Decision matrix A, B be many row often, and the line number of supposing A is g, and the line number of B is k, and the assembled scheme of solution layer decision-making just has g * k so.
(3) realize the complex optimum decision-making of gear hobbing working process parameter; First gear hobbing working process parameter decision model is set up to judgment matrix, certain key element of last layer will be compared between all key elements of this level between two; Then, carry out Mode of Level Simple Sequence, according to judgment matrix, by calculating relative Link Importance, each key element of this level is carried out to importance sorting with respect to certain key element of last layer; Then, carry out consistency check, judge the confidence level of each scheme relative Link Importance; Finally, carry out level and always sort, can be from destination layer, obtain on current layer each key element for last layer comprehensive importance degree generally speaking top-downly; What importance degree was the highest is gear hobbing working process parameter Optimal Decision-making scheme.
The present invention is based on ontology theory, and gear hobbing processing technology field is made as to body, according to above-mentioned steps, builds gear hobbing processing technology domain body as shown in Figure 1.
In above-mentioned steps 1, the foundation of conception ontology tree is as follows:
Body is the generalities to field, and concept and relation are the basic building blocks of body, and wherein concept is core.Because relation is for describing the contact between field concept, itself also can be used as concept and processes.Attribute, rule and instance depend on a certain concept, so the structure of body should be centered by concept.The present invention adopts top down method to set up the concept classification level of gear hobbing processing technology domain body, from gear hobbing processing technology field, maximum concept starts, by adding subclass by these concept refinements, then use field concept body tree representation, as shown in Figure 2.
In above-mentioned steps 1, be related to foundation, attributes extraction, Rule Extraction and formation conceptual base, be related to that the process of storehouse, attribute library, rule base and case library is as follows:
Relation between the concept of analysis gear hobbing processing technology field concept body tree, carries out attributes extraction and Rule Extraction to concept and relation.
Take below process 7 class precisions, normal module is 3, the number of teeth is 53, the involute urve standard helical gears that helix angle is 180 are that example is set forth attribute and Rule Extraction process:
First analyze this example field, part is enumerated concept layering: (1) preliminary work.Check whether numbering and physical size require to be consistent with technological process; Check tooth base basal plane mark, must not location basal plane is wrongly installed.(2) tooth base processing.In this example, the size and dimension accuracy class of pilot hole is IT7, roughness Ra 1.25 μ m, and reference diameter is 3 * 53/cos18
0=167.18mm, the cylindrical diameter run-out of tooth base and datum end face run-out tolerance within the scope of 125～400mm are no more than 0.022mm; During tooth base clamping, should markd reference field is downward, make the laminating of its carrying plane, must not packing paper or the thing such as copper sheet; Before compressing, with clock gauge inspection tooth base cylindrical radial run-out and datum end face, beat, after compression, need again to check, during with preventing pinch, produce distortion.(3) Optimized Matching such as hobboing cutter, fixture.In this example, while carrying out roughing, select the hobboing cutter precision of B or C grade, while carrying out finishing, select the hobboing cutter precision of AA grade; When gear hobbing, while having the phenomenons such as hot spot, plucking, roughness degenerate as the discovery flank of tooth, must check hob abrasion amount, in this example, thick hob abrasion amount must not be greater than 0.4mm, and fine hobbing cutter wear extent must not be greater than 0.2mm; After the each sharpening of fine hobbing cutter, all need to check before non-footpath tropism, tooth-face roughness and the cutter tooth before chip pocket total cumulative pitch error, chip pocket adjacent pitch error, cutter tooth depth of parallelism with interior axially bored line etc., and will have certificate of inspection to use; When fixture is installed, diverse location will meet different error requirements, in this example, as shown in Figure 3, on the run-out error at A place, is limited to 0.015mm, and B place is 0.010mm, and C place is 0.005mm, and D place is 0.015mm.(4) process parameter optimizing.According to situations such as the technical parameter of processed gear, accuracy requirement, material and tooth face hardnesses, determine cutting data.The following processing specification of recommend adoption during with Single-start hob: a. rolling cut number of times: slightly roll, essence roll each once; B. cutting depth: slightly roll rear transverse tooth thickness and must leave 0.50～1.00mm surplus; C. cutting speed: 15～40m/mm; D. the amount of feeding: slightly roll the amount of feeding: 0.5～2.0mm/r, essence is rolled the amount of feeding: 0.6～5.0mm/r.
According to the method in step 1, set up conceptual base, as shown in table 1.
Table 1 is for this routine conceptual base
Contact in analytical table 1 between conceptual knowledge, abstract is to be related to knowledge, according to the method in step 1, opening relationships storehouse, as shown in table 2.
Table 2 is for this routine storehouse that is related to
Can and be related to knowledge from concept above and carry out attribute and Rule Extraction, according to the method in step 1, set up attribute library and rule base, as shown in table 3-4
Table 3 is for this routine attribute library
Table 4 is for this routine rule base
According to the method in step 1, this example is added in case library as an example, as shown in table 5.
Table 5 is for this routine case library
For this routine conceptual base, be related to that storehouse, attribute library, rule base and case library have formed for this routine gear hobbing processing technology ontology library.There is ontology library just can inquire about easily a certain concept relevant with which concept, there is which attribute and rule.As hobboing cutter is selected this concept, the same levels such as it and fixture, rolling cut technique (in Table 2: for this routine storehouse that is related to), with the Optimized Matchings such as hobboing cutter, fixture be set membership (in Table 2: for this routine storehouse that is related to), it has the attributes (in Table 3: for this routine attribute library) such as hobboing cutter material, geometric properties, precision, it must meet some rules, How to choose hobboing cutter precision, wear extent etc., rule searching 4,5,6,7(are in Table 4: for this routine rule base); If inquire about this routine work flow, just can check case library.
In above-mentioned steps 2, built after gear hobbing working process parameter decision model, need to obtain solution layer decision scheme by the Combination of Methods of retrieval ontology library and case-based reasoning, as shown in Figure 4, concrete steps are as follows for its process:
Step1 decision maker equates regular according to modulus and retrieves in ontology library, selects the g of coupling hobboing cutter, extracts hobboing cutter precision out, and hobboing cutter head number, three parameters of hobboing cutter helix angle, obtain solution layer decision scheme matrix A (g, 3);
Step2 utilizes the method for case-based reasoning, construction meta-model
Wherein, N
0represent that decision objective name in a name space claims, p
i 0, (i=1,2 ..., n) represent not belong in solution layer the attribute of hobboing cutter basic parameter, be matter-element feature, v
i 0, (i=1,2 ..., n) be N
0about p
i 0(i=1,2 ..., value two tuples n), are expressed as v
i 0=<v
il 0, v
ih 0>, (i=1,2 ..., n), v
il 0, v
ih 0represent p
i 0(i=1,2 ..., optimization n) is interval;
Step3 utilizes similarity formula:
calculate historical processing instance in r case library and optimize interval similarity about i matter-element feature and decision objective space i.The interval of similarity value is [0,1], is worth larger expression similarity higher;
Step4 adds weight w to each matter-element feature
i, (i=1,2 ..., n), calculate comprehensive similarity
definite threshold κ, selects k the historical processing instance that similarity is higher, extracts hobboing cutter rotating speed, feed number of times, axial feed velocity, radial feed speed, workpiece rotational frequency out, alters six parameters of cutter amount, obtains solution layer decision scheme matrix B (k, 6);
Step5, by solution layer decision scheme matrix A (g, 3) and B (k, 6) combination, forms g * k kind solution layer decision-making assembled scheme.
In above-mentioned steps 3, as shown in Figure 5, concrete steps are as follows in the complex optimum decision-making of gear hobbing working process parameter:
Step1 sets and is compared a layer EVA0 by name, being compared layer key element is ELE0, relatively layer is called EVA1, relatively layer key element is ELE1, EVA0=destination layer, ELE0=gear hobbing working process parameter Optimal Decision-making, EVA1=evaluates layer, ELE1=crudy, process time, processing cost, resource consumption, environmental impact; Judgement symbol ELEFlag=0;
Step2, according to judgement yardstick table (table 6), will compare between the ELE1 of EVA1 between two for the ELE0 attribute of EVA0, obtains the judgment matrix of EVA1 to EVA0
(i, j=1,2 ..., N); Wherein, a
ijfor in EVA1 for the fiducial value of i key element to j key element of certain key element in EVA0, a
ji=1/a
ij, (i ≠ j), N is the number of key element in EVA1, is the number of attribute in ELE1;
Step3 is according to judgment matrix H
s, calculate H
sthe product of every a line various element
i, j=1,2 ..., N, then, calculates M
inth power root
then, right
be normalized, obtain H
seach component of proper vector
eVA1 is about H
srelative Link Importance be (W
1, W
2..., W
n), last, calculate H
smaximum characteristic root
w=(W
1, W
2..., W
n)
t, (AW)
i=H
sthe capable data of i and the sum of products of W respective items; Obtain the hierarchical ranking table (table 7) of EVA1 to certain key element in EVA0;
Step4 carries out consistency check according to CR=CI/RI, wherein CI=(λ
max-N)/(N-1), work as H
swhile thering is crash consistency, CI=0.RI is H
smean random index, works as CR<0.1, thinks that judgment matrix has satisfactory consistency, and the relative Link Importance of calculating is also acceptable, otherwise, revision H
s, proceed to Step2;
If Step5 is ELEFlag=0, EVA0=evaluates layer so, ELE0=crudy, and EVA1=solution layer, ELE1=scheme 1, scheme 2,, scheme num, ELEFlag=ELEFlag+ 1, proceeds to Step2;
If Step6 is ELEFlag=1, so ELE0=process time, ELEFlag=ELEFlag+ 1, proceeds to Step2;
If Step7 is ELEFlag=2, ELE0=processing cost so, ELEFlag=ELEFlag+ 1, proceeds to Step2;
If Step8 is ELEFlag=3, ELE0=resource consumption so, ELEFlag=ELEFlag+ 1, proceeds to Step2;
If Step9 is ELEFlag=4, ELE0=environmental impact so, ELEFlag=ELEFlag+ 1, proceeds to Step2;
Step10, from destination layer, obtains each key element on current layer for last layer comprehensive importance degree time generally speaking top-downly, and its computing formula is:
j=1,2 ..., num, ap
ito evaluate each key element of layer about the relative Link Importance of destination layer key element, bp
j ibe each key element of solution layer about evaluating the relative Link Importance of certain key element of layer, all try to achieve.Obtaining solution layer to the total sequencing table of the level of destination layer (table 8), select the scheme that importance degree is the highest, is the complex optimum result of decision of gear hobbing working process parameter.
The judgement yardstick of table 6 key element comparison
| Judged result | a ij |
| To H S, no less important | 1 |
| To H S, more important | 3 |
| To H S, obviously important | 5 |
| To H S, important a lot | 7 |
| To H S, extremely important | 9 |
| Centre between above-mentioned two |
2,4,6,8 |
Table 7 hierarchical ranking table
Note: n is natural number, by the key element number of EVA1, is determined
Table 8 solution layer is to the total sequencing table of the level of destination layer
Claims (7)
1. a hobbing method for processing, is characterized in that, gear hobbing adds man-hour, carries out the Optimal Decision-making of gear hobbing working process parameter according to following steps, and concrete steps are:
(1) realize the structure of gear hobbing processing technology ontology library; First, by gear hobbing processing technology knowledge abstraction, be gear hobbing processing technology domain body, according to similarity feature, knowledge in gear hobbing processing technology field is divided into groups, be respectively five groups of concepts, relation, attribute, rule and instance; Secondly, according to the grouping of gear hobbing processing technology domain knowledge, conceptual knowledge is integrated into gear hobbing processing technology field concept body tree by top down method; Again, the abstract model according to formed gear hobbing processing technology field concept body tree, utilizes ontology theory, analyzes the contact between gear hobbing processing technology conceptual knowledge, and abstract is to be related to knowledge, to concept be related to that knowledge carries out attributes extraction and Rule Extraction; Finally, the form storage with database, completes the knowledge representation of gear hobbing processing technology, forms ontology library, and it comprises conceptual base, is related to storehouse, attribute library, rule base and case library; According to BNF normal form (Backus normal form (BNF)), according to knowledge such as the relation of the conceptual knowledge in gear hobbing processing technology field concept body tree and extraction before, attribute, rules, form respectively conceptual base, be related to storehouse, attribute library, rule base; By whole gear hobbing processing technology knowledge representation process instance, form case library;
(2) realize the expression in gear hobbing working process parameter decision objective space; First use analytical hierarchy process to build gear hobbing working process parameter decision model, gear hobbing working process parameter decision objective space is divided into three levels: destination layer, evaluation layer and solution layer; Wherein in the attribute of solution layer, belong to the attribute of hobboing cutter basic parameter, by the mode of man-machine interaction, decision maker equates the relevant knowledge of rule search ontology library according to modulus, obtain solution layer decision scheme matrix A; In attribute in solution layer, do not belong to the attribute of hobboing cutter basic parameter, utilize Case-Based Reasoning that the similarity of decision objective space and historical processing instance is calculated, to select the example mating with solution layer numerical attribute, obtain solution layer decision scheme matrix B;
(3) realize the complex optimum decision-making of gear hobbing working process parameter; First gear hobbing working process parameter decision model is set up to judgment matrix, certain key element of last layer will be compared between all key elements of this level between two; Then, carry out Mode of Level Simple Sequence, according to judgment matrix, by calculating relative Link Importance, each key element of this level is carried out to importance sorting with respect to certain key element of last layer; Then, carry out consistency check, judge the confidence level of each scheme relative Link Importance; Finally, carry out level and always sort, can be from destination layer, obtain on current layer each key element for last layer comprehensive importance degree generally speaking top-downly; What importance degree was the highest is gear hobbing working process parameter Optimal Decision-making scheme.
2. hobbing method for processing as claimed in claim 1, is characterized in that, gear hobbing processing technology domain body is defined as follows:
Gear hobbing processing technology domain body is that a kind of detailed characterization of the concept to existing in gear hobbing processing technology field is described, being that gear hobbing processing technology domain body is a kind of description to the concept in gear hobbing processing technology field, relation, attribute, rule and instance five elements, is to realize the basis that domain knowledge is shared and reused; Wherein concept refers to normalized, generally acknowledged term in gear hobbing processing technology field, is the set with same alike result or object of action; It,, except referring to concept in general sense, also comprises task, function and the behavior of gear hobbing processing technology aspect; Relation refers to connection or the association between field concept; Relation is present between a plurality of concepts; This form with concept in the process of generalities of relation exists, and between relation, can form new relation.
3. hobbing method for processing as claimed in claim 2, is characterized in that, described BNF normal form is the formalized description to described gear hobbing processing technology domain body definition, is the representation of knowledge of domain body, is also the basis that body builds; Its BNF normal form is as follows:
(1) < gear hobbing processing technology domain body >::=(< domain name >, < concept >, < is related to >, < attribute >, < Regulation G reatT.GreaT.GT, < example >);
(2) < concept >::=(< concept >, < concept title >, [< synonym >], [< initialism >], < conceptual description >, [father's class-mark > of this concept of <], [domain name > under <]);
(3) < is related to >::=(< relation >, < is related to title >, < is related to former piece >, < is related to consequent >, < relationship description >);
(4) < attribute >::=(< attribute >, the categorical conception > of < institute, < relation >, < Property Name >, [< synonym >], [< initialism >], the type > of < value, the scope > of < collection),
(5) < Regulation G reatT.GreaT.GT::=(< rule >, the categorical conception > of < institute, < relation >, < rule description >, < confidence level >);
(6) < example >::=(< instance number >, < instance name >, < problem is described >, < instance properties >, < instance properties value >, < example rule >);
(7) < domain name >::=< indications >.
4. hobbing method for processing as claimed in claim 1, it is characterized in that, adopt top down method to set up the concept classification level of gear hobbing processing technology domain body, from gear hobbing processing technology field, maximum concept starts, by adding subclass by these concept refinements, then use field concept body tree representation.
5. hobbing method for processing as claimed in claim 1, is characterized in that, the destination layer in described step (2) is gear hobbing working process parameter Optimal Decision-making; The key element of evaluating layer comprises crudy, process time, processing cost, resource consumption and environmental impact; Solution layer is scheme 1, scheme 2,, scheme num, num is natural number, its attribute comprises hobboing cutter precision, hobboing cutter head number, hobboing cutter helix angle, hobboing cutter rotating speed, feed number of times, axial feed velocity, radial feed speed, workpiece rotational frequency and alter cutter amount.
6. hobbing method for processing as claimed in claim 5, is characterized in that,
The concrete steps that solution layer decision-making assembled scheme in described step (2) generates comprise,
Step1 decision maker equates regular according to modulus and retrieves in ontology library, selects the g of coupling hobboing cutter, extracts hobboing cutter precision out, and hobboing cutter head number, three parameters of hobboing cutter helix angle, obtain solution layer decision scheme matrix A (g, 3);
Step2 utilizes the method for case-based reasoning, construction meta-model
wherein, n=6, N
0represent that decision objective name in a name space claims, p
i 0, (i=1,2 ..., n) represent not belong in solution layer the attribute of hobboing cutter basic parameter, be matter-element feature, v
i 0, (i=1,2 ..., n) be N
0about p
i 0(i=1,2 ..., value two tuples n), are expressed as v
i 0=<v
il 0, v
ih 0>, (i=1,2 ..., n), v
il 0, v
ih 0represent p
i 0(i=1,2 ..., optimization n) is interval;
Step3 utilizes similarity formula:
calculate historical processing instance in r case library and optimize interval similarity about i matter-element feature and decision objective space i; The interval of similarity value is [0,1], is worth larger expression similarity higher;
Step4 adds weight w to each matter-element feature
i, (i=1,2 ..., n), calculate comprehensive similarity
definite threshold κ, selects k the historical processing instance that similarity is higher, extracts hobboing cutter rotating speed, feed number of times, axial feed velocity, radial feed speed, workpiece rotational frequency out, alters six parameters of cutter amount, obtains solution layer decision scheme matrix B (k, 6);
Step5, by solution layer decision scheme matrix A (g, 3) and B (k, 6) combination, forms g * k kind solution layer decision-making assembled scheme.
7. described hobbing method for processing as claimed in claim 1, is characterized in that,
The complex optimum decision-making concrete steps of the gear hobbing working process parameter in described step (3) comprise,
Step1 sets and is compared a layer EVA0 by name, being compared layer key element is ELE0, relatively layer is called EVA1, relatively layer key element is ELE1, EVA0=destination layer, ELE0=gear hobbing working process parameter Optimal Decision-making, EVA1=evaluates layer, ELE1=crudy, process time, processing cost, resource consumption, environmental impact; Judgement symbol ELEFlag=0;
Step2, according to judgement yardstick table, will compare between the ELE1 of EVA1 between two for the ELE0 attribute of EVA0, obtains the judgment matrix of EVA1 to EVA0
(i, j=1,2 ..., N); Wherein, a
ijfor in EVA1 for the fiducial value of i key element to j key element of certain key element in EVA0, a
ji=1/a
ij, (i ≠ j), N is the number of key element in EVA1, is the number of attribute in ELE1;
Step3 is according to judgment matrix H
s, calculate H
sthe product of every a line various element
i, j=1,2 ..., N, then, calculates M
inth power root
then, right
be normalized, obtain each component of the proper vector of judgment matrix
evaluate layer about H
srelative Link Importance be (W
1, W
2..., W
n), last, calculate H
smaximum characteristic root
w=(W
1, W
2..., W
n)
t, (AW)
i=H
sthe capable data of i and the sum of products of W respective items; Obtain the hierarchical ranking table of EVA1 to certain key element in EVA0;
Step4 carries out consistency check according to CR=CI/RI, wherein CI=(λ
max-N)/(N-1), work as H
swhile thering is crash consistency, CI=0; RI is H
smean random index, works as CR<0.1, thinks that judgment matrix has satisfactory consistency, and the relative Link Importance of calculating is also acceptable, otherwise revision judgment matrix, proceeds to Step2;
If Step5 is ELEFlag=0, EVA0=evaluates layer so, ELE0=crudy, and EVA1=solution layer, ELE1=scheme 1, scheme 2,, scheme num, ELEFlag=ELEFlag+1, proceeds to Step2;
If Step6 is ELEFlag=1, so ELE0=process time, ELEFlag=ELEFlag+1, proceeds to Step2;
If Step7 is ELEFlag=2, ELE0=processing cost so, ELEFlag=ELEFlag+1, proceeds to Step2;
If Step8 is ELEFlag=3, ELE0=resource consumption so, ELEFlag=ELEFlag+1, proceeds to Step2;
If Step9 is ELEFlag=4, ELE0=environmental impact so, ELEFlag=ELEFlag+1, proceeds to Step2;
Step10, from destination layer, obtains each key element on current layer for last layer comprehensive importance degree time generally speaking top-downly, and its computing formula is:
j=1,2 ..., num, ap
ito evaluate each key element of layer about the relative Link Importance of destination layer key element, bp
j ibe each key element of solution layer about evaluating the relative Link Importance of certain key element of layer, all try to achieve; Obtaining solution layer to the total sequencing table of the level of destination layer, select the scheme that importance degree is the highest, is the complex optimum result of decision of gear hobbing working process parameter.
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