CN103729525B - A kind of hobbing method for processing - Google Patents
A kind of hobbing method for processing Download PDFInfo
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- CN103729525B CN103729525B CN201410038194.4A CN201410038194A CN103729525B CN 103729525 B CN103729525 B CN 103729525B CN 201410038194 A CN201410038194 A CN 201410038194A CN 103729525 B CN103729525 B CN 103729525B
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Abstract
The invention discloses a kind of hobbing method for processing, it is characterised in that during gear hobbing process, following the steps below the Optimal Decision-making of gear hobbing process technological parameter, specifically including step is: (1) realizes the structure of gear hobbing process technique ontology library；(2) expression in gear hobbing process technological parameter decision objective space is realized；(3) the complex optimum decision-making of gear hobbing process technological parameter is realized.The invention have the advantage that and can realize sharing and reusing of this domain knowledge with the gear hobbing process technology field ontology library of database purchase, by case-based reasoning and analytic hierarchy process (AHP) R. concomitans, gear hobbing process technological parameter can be considered as a system, can solve the problem that again new hobbing processes parameter optimization decision problem.
Description
Technical field
The present invention relates to gear machining technology, during especially relating to a kind of gear hobbing process, technological parameter is optimized
The processing method of decision-making.
Background technology
The rationally selection of gear hobbing process technological parameter and Optimal Decision-making are for improving the gear hobbing process quality of production and processing
Efficiency is significant.It is empirical method or test method(s) that the most widely used technological parameter drafts method: raw for high-volume
Producing, technologist's often processing experience according to various workshop manuals and oneself accumulation completes the formulation of technological parameter；For little
Multi-item production in batches, need to be determined in advance feasible technological parameter according to the process experiences of technologist, and combine certain number of times
Trial cut obtain and can meet the technological parameter of processing request.But existing technological parameter decision method exists following not enough: 1.
Lack the Unified Expression of unified gear hobbing process process knowledge；2. gear hobbing process process cycle is long；The most do not consider processing
Quality, process time, processing cost, resource consumption, the factor such as environmental effect.
The existing processing method to gear hobbing process process parameter optimizing decision problem specifically includes that (1) is based on specialist system
Various optimized algorithms；(1) Case-Based Reasoning.Various optimized algorithms based on specialist system process gear hobbing process technological parameter
During Optimal Decision-making problem, it is automatically performed process parameter optimizing by genetic algorithm, artificial neural network scheduling algorithm, but due to algorithm
There is unpredictability, cause the result of decision unstable and the most reusable, lack unified Process Knowledge Representation.Case-based reasoning side
Method from history processing instance angle, by retrieving, reuse, revising, the step such as reservation solve new problem, but with example phase
Like degree as sole indicator, lack the processing effect considering craft embodiment so that the process program of Optimum Matching example is used
Technological parameter is applied to its processing effect when decision-making technological problems may be undesirable.
Summary of the invention
For the deficiencies in the prior art, the technical problem to be solved is, how to provide one can shorten rolling
Tooth process time, improving the hobbing method for processing of gear hobbing process effect, it is capable of the Optimal Decision-making of working process parameter, real
The Unified Expression of existing gear hobbing process process knowledge, and analytic hierarchy process (AHP) and case-based reasoning being combined, adds man-hour reaching shortening
Between, improve the purpose of processing effect.
In order to solve above-mentioned technical problem, the present invention have employed following technical scheme:
A kind of hobbing method for processing, it is characterised in that during gear hobbing process, follows the steps below gear hobbing process technique ginseng
The Optimal Decision-making of number, concretely comprises the following steps:
(1) structure of gear hobbing process technique ontology library is realized；First, by abstract for gear hobbing process process knowledge for gear hobbing process
Technology field body, according to similarity feature, is grouped knowledge in gear hobbing process technology field, respectively concept, relation,
Attribute, rule and example five groups；Secondly, it is grouped according to gear hobbing process technology field knowledge, conceptual knowledge is pressed top down method
It is integrated into gear hobbing process technology field conception ontology tree；Again, according to the gear hobbing process technology field conception ontology tree formed
Abstract model, utilize ontology theory, analyze the contact between gear hobbing process process concept knowledge, abstract for relation knowledge, to generally
Read and relation knowledge carries out attributes extraction and Rule Extraction；Finally, store with the form of data base, complete gear hobbing process technique
Knowledge representation, forms ontology library, and it comprises conceptual base, relation storehouse, attribute library, rule base and case library；I.e. according to BNF form
(Backus normal form (BNF)), according to the conceptual knowledge in gear hobbing process technology field conception ontology tree and the relation extracted before, attribute,
The knowledge such as rule, form conceptual base, relation storehouse, attribute library, rule base respectively；By whole gear hobbing process Process Knowledge Representation process
Instantiation, forms case library；
(2) expression in gear hobbing process technological parameter decision objective space is realized；First analytic hierarchy process (AHP) is used to build gear hobbing
Working process parameter decision model, is divided into three levels by gear hobbing process technological parameter decision objective space: destination layer, evaluate layer
And solution layer；Wherein belonging to the attribute of hobboing cutter basic parameter in the attribute of solution layer, by the way of man-machine interaction, policymaker depends on
According to the relevant knowledge of modulus equal rule search ontology library, obtain solution layer decision scheme matrix A；In attribute in solution layer not
Belong to the attribute of hobboing cutter basic parameter, utilize Case-Based Reasoning the similarity in decision objective space with history processing instance to be entered
Row calculates, and to select the example mated with solution layer numerical attribute, obtains solution layer decision scheme matrix B；(decision matrix A,
B often many row, it is assumed that the line number of A be the line number of g, B be k, then the assembled scheme of solution layer decision-making just has g × k；)
(3) the complex optimum decision-making of gear hobbing process technological parameter is realized；First to gear hobbing process technological parameter decision model
Set up judgment matrix, i.e. compare two-by-two between all for this level key elements for certain key element of last layer；Then, layer is carried out
Secondary single sequence, according to judgment matrix, by calculating relative Link Importance to each key element of this level relative to certain key element of last layer
Carry out importance sorting；Then, consistency check is carried out, it is judged that the credibility of each scheme relative Link Importance；Finally, layer is carried out
Secondary total sequence, can obtain each key element on current layer for last layer generally speaking comprehensive from the beginning of destination layer top-downly
Importance degree；What importance degree was the highest is i.e. gear hobbing process process parameter optimizing decision scheme.
As optimization, above-mentioned gear hobbing process technology field ontology definition is as follows:
Gear hobbing process technology field body is to a kind of detailed feature of concept present in gear hobbing process technology field
Change and describe, i.e. gear hobbing process technology field body is to the concept in gear hobbing process technology field, relation, attribute, rule and reality
A kind of description of example five elements, is to realize the basis that domain knowledge is shared and reused；Wherein concept refers to that gear hobbing process technique is led
Term normalized, generally acknowledged in territory, is the set with same alike result or object of action；It is except referring in general sense general
Read, also include the task of gear hobbing process process aspect, function and behavior；Relation refers to the connection between field concept or association；Close
System is present between multiple concept；Relation this during conceptualization presented in concept, can structure between relation
The relation of Cheng Xin；(relation between concept mainly has Is-a relation, A-kind-of relation and A-part-of relation).
As optimization, described BNF form is the formalized description to described gear hobbing process technology field ontology definition, is
The representation of knowledge of domain body, is also the basis of ontological construction；Its BNF form is as follows:
(1)<gear hobbing process technology field body>: :=(<domain name>,<concept>,<relation>,<attribute>,<rule>,<
Example >)；
(2)<concept>: :=(<concept number>,<concept name>, [<synonym>], [<initialism>],<conceptual description>, [<
The parent number of this concept>], [<art title>])；
(3)<relation>: :=(<relation number>,<relation name>,<relation former piece>,<relation consequent>,<relationship description>)；
(4)<attribute>: :=(<attribute number>,<said concepts number>,<relation number>,<Property Name>, [<synonym>], [<
Initialism>],<type of value>,<scope of collection>)；
(5)<rule>: :=(<rule number>,<said concepts number>,<relation number>,<rule describes>,<credibility>)；
(6)<example>: :=(<instance number>,<instance name>,<problem description>,<instance properties number>,<instance properties value
>,<example rule number>)；
(7)<domain name>: :=<indications>.
As optimization, use top down method to set up the concept classification level of gear hobbing process technology field body, i.e. from
Concept maximum in gear hobbing process technology field starts, and these concepts is refined by adding subclass, then with field concept originally
Body tree representation.
As optimization, the destination layer in described step (2) is gear hobbing process process parameter optimizing decision-making；Evaluate the key element of layer
Including crudy, process time, processing cost, resource consumption and environmental effect；Solution layer is scheme 1, scheme
2, scheme num, num represents natural number, and its attribute includes that hobboing cutter precision, hob head number, hobboing cutter helical angle, hobboing cutter turn
Speed, feed number of times, axial feed velocity, radial feed speed, workpiece rotational frequency and alter cutter amount.
As optimization, the concrete steps of the solution layer decision combinations schemes generation in described step (2) include,
Step1 policymaker retrieves in ontology library according to the equal rule of modulus, selects the g of coupling hobboing cutter, extraction hobboing cutter
Precision, hob head number, three parameters of hobboing cutter helical angle, obtain solution layer decision scheme matrix A (g, 3)；
Step2 utilizes the method for case-based reasoning, construction meta-model Wherein,
N0Represent that decision objective name in a name space claims, pi 0, (i=1,2 ..., expression solution layer n) is not belonging to the attribute of hobboing cutter basic parameter,
It is matter-element feature, vi 0, (i=1,2 ..., n) it is N0About pi 0(i=1,2 ..., value two tuple n), it is expressed as vi 0=<vil 0,
vih 0>, (i=1,2 ..., n), vil 0、vih 0Represent pi 0(i=1,2 ..., optimization n) is interval；(n represents natural number)
Step3 utilizes similarity formula:Calculate the history in the r case library
Processing instance optimizes interval similarity about the i-th of i-th matter-element feature with decision objective space；The interval of Similarity value
For [0,1], it is worth the biggest expression similarity the highest；
Step4 gives each matter-element feature plus weight wi, (i=1,2 ..., n), calculate comprehensive similarityDetermine threshold value κ, select the example that similarity is higher, extract hobboing cutter rotating speed, feed number of times, axially out
Feed speed, radial feed speed, workpiece rotational frequency, alter six parameters of cutter amount, obtain solution layer decision scheme matrix B (k, 6)；
Solution layer decision scheme matrix A (g, 3) and B (k, 6) are combined by Step5, form g × k kind solution layer decision combinations
Scheme.
As optimization, the complex optimum decision-making concrete steps of the gear hobbing process technological parameter in described step (3) include,
Step1 sets and is compared the entitled EVA0 of layer, and being compared layer key element is ELE0, compares the entitled EVA1 of layer, compares layer and wants
Element is ELE1, EVA0=destination layer, ELE0=gear hobbing process process parameter optimizing decision-making, and EVA1=evaluates layer, ELE1=crudy,
Process time, processing cost, resource consumption, environmental effect；Judgement symbol ELEFlag=0；
Step2 according to deposit index table, for the ELE0 attribute of EVA0 by the ELE1 of EVA1 between two-by-two than
Relatively, the EVA1 judgment matrix to EVA0 is obtained
(i,j=1,2,…,N)；Wherein, aijFor in EVA1 for the i-th key element for certain key element in EVA0 to jth
The fiducial value of key element, aji=1/aij, (i ≠ j), N is the number of key element in EVA1, is i.e. the number of attribute in ELE1；
Step3 is according to judgment matrix HS, calculate HSThe product of every a line various elementI, j=1,2 ..., N, connect
, calculate MiNth power rootThen, rightIt is normalized, obtains each point of characteristic vector of judgment matrix
AmountThen evaluate layer about HSRelative Link Importance be (W1,W2,…,WN), finally, calculate HSMaximum special
Levy root (AW)i=HSThe sum of products of the i-th row data and W respective items；?
To the EVA1 hierarchical ranking table to EVA0 key element；
Step4 carries out consistency check, wherein CI=(λ according to CR=CI/RImax-N)/(N-1), work as HSHave completely the same
During property, CI=0；RI is HSMean random index, as CR < 0.1, then it is assumed that judgment matrix has satisfactory consistency, calculating relative
Importance degree is also acceptable, otherwise, revises judgment matrix, proceeds to Step2；
If Step5 is ELEFlag=0, then EVA0=evaluates layer, ELE0=crudy, EVA1=solution layer, ELE1=side
Case 1, scheme 2, scheme num, ELEFlag=ELEFlag+1, proceed to Step2；
If Step6 is ELEFlag=1, then ELE0=process time, ELEFlag=ELEFlag+1, proceed to Step2；
If Step7 is ELEFlag=2, then ELE0=processing cost, ELEFlag=ELEFlag+1, proceed to Step2；
If Step8 is ELEFlag=3, then ELE0=resource consumption, ELEFlag=ELEFlag+1, proceed to Step2；
If Step9 is ELEFlag=4, then ELE0=environmental effect, ELEFlag=ELEFlag+1, proceed to Step2；
For last layer time generally speaking Step10, from the beginning of destination layer, obtains each key element on current layer top-downly
Comprehensive importance degree, its computing formula is:J=1,2 ..., num, apiIt is to evaluate each key element of layer about target
The relative Link Importance of layer key element, bpj iIt is the solution layer each key element relative Link Importance about evaluation layer key element, the most tries to achieve；?
To the solution layer total hierarchial sorting table to destination layer, select the scheme that importance degree is the highest, be i.e. combining of gear hobbing process technological parameter
Close Optimal Decision-making result.
The present invention can realize sharing of this domain knowledge with the gear hobbing process technology field ontology library of database purchase
With reuse, by case-based reasoning and analytic hierarchy process (AHP) R. concomitans, gear hobbing process technological parameter can be considered as a system,
Can solve the problem that again new hobbing processes parameter optimization decision problem.The roller processing method of the present invention, rolling based on a kind of uniqueness
Tooth working process parameter Optimal Decision-making and realize, in this gear hobbing process process parameter optimizing decision-making, catch gear hobbing process technique to lead
The feature of territory body, by conceptual, systematicness, the knowledge delamination classification of empirical and Process Character, analyzes relation, extract attribute and
Rule, ultimately forms conceptual base, relation storehouse, attribute library, rule base and case library, is gear hobbing process technology field ontology knowledge
Express；Use analytic hierarchy process (AHP) that gear hobbing process technological parameter decision objective space is expressed, build gear hobbing process technique ginseng
Number decision model: destination layer, evaluation layer, solution layer, the method utilizing ontology library and case-based reasoning, the combination side of constructing plan layer
Case；Use analytic hierarchy process (AHP) that decision model is calculated, obtain the scheme that importance degree is the highest, be i.e. gear hobbing process technological parameter
Optimal Decision-making scheme；Carry out gear hobbing process according to optimal decision scheme, i.e. can reach raising gear hobbing process efficiency, improve gear hobbing and add
The effect of working medium amount.
It addition, the present invention also has following technical effect that 1, support knowledge sharing: gear hobbing process technique be long-term, with
Heuristics is main field, and its key concept will not often change, this Core Set of Concepts be realize knowledge sharing and
The basis of interoperability.2, knowledge reuse is supported: gear hobbing process technology field body can be safeguarded and expand, and allows for towards this
The follow-up work (process parameter optimizing decision-making, numerical control program establishment etc.) in field just need not be started all over again from the beginning, and reduces the R&D cycle.
3, multi-method combines: case-based reasoning utilizes history processing instance and experience to solve new problem, and the method not only suits the mankind and solves
The certainly thinking process of problem, and overcome a general intelligence decision system difficult problem in terms of knowledge acquisition, but it is with example phase
It is sole indicator like degree, lacks systematicness；Analytic hierarchy process (AHP) uses the mode of qualitative and quantitative combination, carries out systematic analysis
Decision-making, but it can not produce new decision scheme.Both approaches is effectively combined, gear hobbing process technique can be joined
Number considers as a system, can solve the problem that again new hobbing processes parameter optimization decision problem, thus solves existing technique
The less difficult problem of Parameter Decision Making method Consideration.
Accompanying drawing explanation
Fig. 1 is the schematic diagram of the structure of gear hobbing process technology field body in the specific embodiment of the invention；
Fig. 2 is the schematic diagram of gear hobbing process technology field 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 combination of gear hobbing process technological parameter decisional model plan layer decision scheme in the specific embodiment of the invention
The schematic diagram of process；
Fig. 5 is the schematic diagram of the complex optimum decision making process of gear hobbing process technological parameter in the specific embodiment of the invention.
Detailed description of the invention
The present invention is described in further detail below in conjunction with the accompanying drawings.
The thinking of the present invention is: utilize ontology theory, analyzes gear hobbing process technology field, is defined this field, shape
Become conceptual base, relation storehouse, attribute library, rule base and case library, complete gear hobbing process technology field ontology knowledge and express；Use layer
Gear hobbing process technological parameter decision objective space is expressed by fractional analysis, structure gear hobbing process technological parameter decision model:
Destination layer, evaluation layer, solution layer, utilize ontology library and case-based reasoning, the assembled scheme of constructing plan layer；Use analytic hierarchy process (AHP)
Decision model is calculated, obtains the scheme that importance degree is the highest, be i.e. gear hobbing process process parameter optimizing decision scheme.
The invention will be further described with case study on implementation below in conjunction with the accompanying drawings:
A kind of hobbing method for processing of the present invention, in this method during gear hobbing process, follows the steps below gear hobbing process
The Optimal Decision-making of technological parameter, as Figure 1-Figure 5, including step in detail below:
(1) structure of gear hobbing process technique ontology library-first, by abstract for gear hobbing process process knowledge for gear hobbing process work
Skill domain body, according to similarity feature, is grouped knowledge in gear hobbing process technology field, respectively concept, relation, genus
Property, rule and example five groups；Secondly, it is grouped according to gear hobbing process technology field knowledge, conceptual knowledge is pressed top down method whole
Synthesis gear hobbing process technology field conception ontology tree；Again, according to the gear hobbing process technology field conception ontology tree formed
Abstract model, utilizes ontology theory, analyzes the contact between gear hobbing process process concept knowledge, abstract for relation knowledge, to concept
Attributes extraction and Rule Extraction is carried out with relation knowledge；Finally, store with the form of data base, complete knowing of gear hobbing process technique
Knowing and express, form ontology library, it comprises conceptual base, relation storehouse, attribute library, rule base and case library: according to BNF form, according to
The knowledge such as conceptual knowledge in gear hobbing process technology field conception ontology tree and the relation extracted before, attribute, rule, respectively shape
Become conceptual base, relation storehouse, attribute library, rule base；By whole gear hobbing process Process Knowledge Representation process instantiation, form example
Storehouse.
(2) expression in gear hobbing process technological parameter decision objective space-utilization analytic hierarchy process (AHP) builds gear hobbing process technique
Parameter Decision Making model, is divided into three levels by gear hobbing process technological parameter decision objective space: destination layer, evaluate layer, solution layer.
Destination layer: gear hobbing process process parameter optimizing decision-making；Evaluation layer: crudy, process time, processing cost, resource consumption, ring
Border affects；Solution layer: hobboing cutter precision, hob head number, hobboing cutter helical angle, hobboing cutter rotating speed, feed number of times, axial feed velocity, footpath
To feed speed, workpiece rotational frequency, alter cutter amount.Attribute in solution layer belongs to hobboing cutter basic parameter, by the side of man-machine interaction
Formula, policymaker, according to the relevant knowledge of modulus equal rule search ontology library, obtains solution layer decision scheme matrix A；Solution layer
In attribute be not belonging to hobboing cutter basic parameter, utilize Case-Based Reasoning by the phase in decision objective space with history processing instance
Calculate like degree, to select the example mated with solution layer numerical attribute, obtain solution layer decision scheme matrix B.Decision-making
Matrix A, B often many row, it is assumed that the line number of A be the line number of g, B be k, then the assembled scheme of solution layer decision-making just have g ×
K.
(3) the complex optimum decision-making of gear hobbing process technological parameter is realized；First to gear hobbing process technological parameter decision model
Set up judgment matrix, i.e. compare two-by-two between all for this level key elements for certain key element of last layer；Then, layer is carried out
Secondary single sequence, according to judgment matrix, by calculating relative Link Importance to each key element of this level relative to certain key element of last layer
Carry out importance sorting；Then, consistency check is carried out, it is judged that the credibility of each scheme relative Link Importance；Finally, layer is carried out
Secondary total sequence, can obtain each key element on current layer for last layer generally speaking comprehensive from the beginning of destination layer top-downly
Importance degree；What importance degree was the highest is i.e. gear hobbing process process parameter optimizing decision scheme.
The present invention is based on ontology theory, and gear hobbing process technology field is set to body, according to above-mentioned steps, builds such as figure
Gear hobbing process technology field body shown in 1.
In above-mentioned steps 1, the foundation of conception ontology tree is as follows:
Body be the conceptualization to field, concept and relation be the basic building block of body, wherein concept is core.Because closing
System is used to describe the contact between field concept, and itself can also process as concept.Attribute, rule and example are to depend on
In a certain concept, so the structure of body should be concept-centric.The present invention uses top down method to set up gear hobbing process
The concept classification level of technology field body, from the beginning of i.e. maximum from gear hobbing process technology field concept, by adding subclass
These concepts are refined, then with field concept body tree representation, as shown in Figure 2.
In above-mentioned steps 1, relation foundation, attributes extraction, Rule Extraction and formation conceptual base, relation storehouse, attribute library, rule
The process of storehouse and case library is as follows:
Analyze the relation between the concept of gear hobbing process technology field conception ontology tree, concept and relation are carried out attributes extraction
And Rule Extraction.
Below to process 7 class precisions, normal module for 3, the number of teeth is 53, and helical angle is the involute standard helical teeth circle of 180
Elaboration attribute and Rule Extraction process as a example by stud wheel:
First analyzing this example field, part is enumerated concept and is layered: (1) preparation.Check numbering and actual chi
Very little whether require to be consistent with technical process；Check tooth base basal plane labelling, must not be wrongly installed by positioning basal plane.(2) tooth base processing.This
In example, the size and dimension accuracy class in hole, location is IT7, and roughness Ra 1.25 μm, reference diameter is 3 × 53/cos180=
167.18mm, tooth base cylindrical circular runout and datum end face run-out tolerance in the range of 125～400mm are less than 0.022mm；
During tooth base clamping, should be by downward for markd datum level so that it is bearing-surface is fitted, must not the thing such as packing paper or copper sheet；Use before compressing
Amesdial checks that tooth base cylindrical radial run-out and datum end face are beated, and needs to check again for after compression, to produce during preventing pinch
Deformation.(3) Optimized Matching such as hobboing cutter, fixture.In this example, when carrying out roughing, select the hobboing cutter precision of B or C grade, carry out essence
Add man-hour, select the hobboing cutter precision of AA grade；When gear hobbing, as found, the flank of tooth has the phenomenons such as hot spot, plucking, roughness degenerate
Time, it is necessary to checking hob abrasion amount, in this example, thick hob abrasion amount cannot be greater than 0.4mm, and fine hobbing cutter wear extent cannot be greater than
0.2mm；Fine hobbing cutter is both needed to after sharpening check before chip pocket total cumulative pitch error, chip pocket adjacent pitch error, cutter tooth every time
Non-radial property, tooth-face roughness and cutter tooth before with the depth of parallelism etc. of interior axially bored line, and certificate of inspection to be had can use；
When fixture is installed, diverse location to meet different error requirements, in this example, as it is shown on figure 3, the run-out error upper limit at A
For 0.015mm, it is 0.010mm at B, is 0.005mm at C, be 0.015mm at D.(4) process parameter optimizing.According to processed tooth
The situations such as technical parameter, required precision, material and the tooth face hardness of wheel determine cutting data.With during Single-start hob recommend use with
Lower processing specification: a. rolling cut number of times: thick rolling, essence rolling are the most once；B. cutting depth: after thick rolling, transverse tooth thickness 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, the essence rolling 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 the conceptual base of this example
Contact between conceptual knowledge in analytical table 1, abstract for relation knowledge, according to the method in step 1, opening relationships
Storehouse, as shown in table 2.
Table 2 is for the relation storehouse of this example
Attribute and Rule Extraction can be carried out from concept above and relation knowledge, according to the method in step 1, set up
Attribute library and rule base, as shown in table 3-4
Table 3 is for the attribute library of this example
Table 4 is for the rule base of this example
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 the case library of this example
Conceptual base, relation storehouse, attribute library, rule base and case library for this example constitute the gear hobbing process for this example
Technique ontology library.There is ontology library just can inquire about a certain concept easily relevant with which concept, there are which attribute and rule
Then.As hobboing cutter selects this concept, it and the same level (being shown in Table 2: for the relation storehouse of this example) such as fixture, rolling cut technique, with rolling
The Optimized Matching such as cutter, fixture is filiation (being shown in Table 2: for the relation storehouse of this example), it have hobboing cutter material, geometric properties,
The attributes such as precision (are shown in Table 3: for the attribute library of this example), and it must meet some rules, how to select hobboing cutter precision, wear extent
Deng, rule searching 4,5,6,7(be shown in Table 4: for the rule base of this example)；To inquire about the work flow of this example, so that it may check reality
Example storehouse.
In above-mentioned steps 2, after having built gear hobbing process technological parameter decision model, need by retrieval ontology library and reality
The Combination of Methods of example reasoning obtains solution layer decision scheme, and its process as shown in Figure 4, specifically comprises the following steps that
Step1 policymaker retrieves in ontology library according to the equal rule of modulus, selects the g of coupling hobboing cutter, extraction hobboing cutter
Precision, hob head number, three parameters of hobboing cutter helical angle, obtain solution layer decision scheme matrix A (g, 3)；
Step2 utilizes the method for case-based reasoning, construction meta-model Wherein,
N0Represent that decision objective name in a name space claims, pi 0, (i=1,2 ..., expression solution layer n) is not belonging to the attribute of hobboing cutter basic parameter,
It is matter-element feature, vi 0, (i=1,2 ..., n) it is N0About pi 0(i=1,2 ..., value two tuple n), it is expressed as vi 0=<vil 0,
vih 0>, (i=1,2 ..., n), vil 0、vih 0Represent pi 0(i=1,2 ..., optimization n) is interval；
Step3 utilizes similarity formula:Calculate the history in the r case library
Processing instance optimizes interval similarity about the i-th of i-th matter-element feature with decision objective space.The interval of Similarity value
For [0,1], it is worth the biggest expression similarity the highest；
Step4 gives each matter-element feature plus weight wi, (i=1,2 ..., n), calculate comprehensive similarityDetermine threshold value κ, select k the history processing instance that similarity is higher, extract hobboing cutter rotating speed out, walk
Cutter number of times, axial feed velocity, radial feed speed, workpiece rotational frequency, alter six parameters of cutter amount, obtain solution layer decision scheme square
Battle array B (k, 6)；
Solution layer decision scheme matrix A (g, 3) and B (k, 6) are combined by Step5, form g × k kind solution layer decision combinations
Scheme.
In above-mentioned steps 3, the complex optimum decision-making of gear hobbing process technological parameter is as it is shown in figure 5, specifically comprise the following steps that
Step1 sets and is compared the entitled EVA0 of layer, and being compared layer key element is ELE0, compares the entitled EVA1 of layer, compares layer and wants
Element is ELE1, EVA0=destination layer, ELE0=gear hobbing process process parameter optimizing decision-making, and EVA1=evaluates layer, ELE1=crudy,
Process time, processing cost, resource consumption, environmental effect；Judgement symbol ELEFlag=0；
Step2 according to deposit index table (table 6), for the ELE0 attribute of EVA0 by the ELE1 of EVA1 between carry out two
Two compare, and obtain the EVA1 judgment matrix to EVA0
(i,j=1,2,…,N)；Wherein, aijFor in EVA1 for the i-th key element for certain key element in EVA0 to jth
The fiducial value of key element, aji=1/aij, (i ≠ j), N is the number of key element in EVA1, is i.e. the number of attribute in ELE1；
Step3 is according to judgment matrix HS, calculate HSThe product of every a line various elementI, j=1,2 ..., N, connect
, calculate MiNth power rootThen, rightIt is normalized, obtains HSEach component of characteristic vectorThen EVA1 is about HSRelative Link Importance be (W1,W2,…,WN), finally, calculate HSMaximum characteristic rootW=(W1,W2,…,WN)T, (AW)i=HSThe sum of products of the i-th row data and W respective items；Obtain EVA1 pair
The hierarchical ranking table (table 7) of certain key element in EVA0；
Step4 carries out consistency check, wherein CI=(λ according to CR=CI/RImax-N)/(N-1), work as HSHave completely the same
During property, CI=0.RI is HSMean random index, as CR < 0.1, then it is assumed that judgment matrix has satisfactory consistency, calculating relative
Importance degree is also acceptable, otherwise, revises HS, proceed to Step2；
If Step5 is ELEFlag=0, then EVA0=evaluates layer, ELE0=crudy, EVA1=solution layer, ELE1=side
Case 1, scheme 2, scheme num, ELEFlag=ELEFlag+1, proceed to Step2；
If Step6 is ELEFlag=1, then ELE0=process time, ELEFlag=ELEFlag+1, proceed to Step2；
If Step7 is ELEFlag=2, then ELE0=processing cost, ELEFlag=ELEFlag+1, proceed to Step2；
If Step8 is ELEFlag=3, then ELE0=resource consumption, ELEFlag=ELEFlag+1, proceed to Step2；
If Step9 is ELEFlag=4, then ELE0=environmental effect, ELEFlag=ELEFlag+1, proceed to Step2；
For last layer time generally speaking Step10, from the beginning of destination layer, obtains each key element on current layer top-downly
Comprehensive importance degree, its computing formula is:J=1,2 ..., num, apiIt is to evaluate each key element of layer about target
The relative Link Importance of layer key element, bpj iIt is the solution layer each key element relative Link Importance about evaluation layer key element, the most tries to achieve.?
To the solution layer total hierarchial sorting table (table 8) to destination layer, select the scheme that importance degree is the highest, be i.e. gear hobbing process technological parameter
The complex optimum result of decision.
The deposit index that table 6 key element compares
| Judged result | aij |
| To HSFor, no less important | 1 |
| To HSFor, more important | 3 |
| To HSFor, hence it is evident that important | 5 |
| To HSFor, important a lot | 7 |
| To HSFor, extremely important | 9 |
| Centre between the adjacent deposit index of above-mentioned two | 2,4,6,8 |
Table 7 hierarchical ranking table
Note: n is natural number, the key element number of EVA1 determine
Table 8 solution layer total hierarchial sorting table to destination layer
Claims (7)
1. a hobbing method for processing, it is characterised in that during gear hobbing process, follows the steps below gear hobbing process technological parameter
Optimal Decision-making, concretely comprise the following steps:
(1) structure of gear hobbing process technique ontology library is realized；First, by abstract for gear hobbing process process knowledge for gear hobbing process technique
Domain body, according to similarity feature, is grouped knowledge in gear hobbing process technology field, respectively concept, relation, genus
Property, rule and example five groups；Secondly, it is grouped according to gear hobbing process technology field knowledge, conceptual knowledge is pressed top down method whole
Synthesis gear hobbing process technology field conception ontology tree；Again, according to the gear hobbing process technology field conception ontology tree formed
Abstract model, utilizes ontology theory, analyzes the contact between gear hobbing process process concept knowledge, abstract for relation knowledge, to concept
Attributes extraction and Rule Extraction is carried out with relation knowledge；Finally, store with the form of data base, complete knowing of gear hobbing process technique
Knowing and express, form ontology library, it comprises conceptual base, relation storehouse, attribute library, rule base and case library；I.e. according to BNF form, root
According to the conceptual knowledge in gear hobbing process technology field conception ontology tree and the relation extracted before, attribute, rule knowledge, shape respectively
Become conceptual base, relation storehouse, attribute library, rule base；By whole gear hobbing process Process Knowledge Representation process instantiation, form example
Storehouse；
(2) expression in gear hobbing process technological parameter decision objective space is realized；First analytic hierarchy process (AHP) is used to build gear hobbing process
Technological parameter decision model, is divided into three levels by gear hobbing process technological parameter decision objective space: destination layer, evaluate layer and side
Pattern layer；Wherein belonging to the attribute of hobboing cutter basic parameter in the attribute of solution layer, by the way of man-machine interaction, policymaker is according to mould
The relevant knowledge of the equal rule search ontology library of number, obtains solution layer decision scheme matrix A；Attribute in solution layer is not belonging to
The attribute of hobboing cutter basic parameter, utilizes Case-Based Reasoning the similarity in decision objective space with history processing instance to be counted
Calculate, to select the example mated with solution layer numerical attribute, obtain solution layer decision scheme matrix B；By solution layer decision-making party
Case matrix A and the combination of solution layer decision scheme matrix B, form solution layer decision combinations scheme；
(3) the complex optimum decision-making of gear hobbing process technological parameter is realized；First gear hobbing process technological parameter decision model is set up
Judgment matrix, i.e. compares for certain key element of last layer between all for this level key elements two-by-two；Then, level list is carried out
Sequence, according to judgment matrix, is carried out relative to certain key element of last layer each key element of this level by calculating relative Link Importance
Importance sorting；Then, consistency check is carried out, it is judged that the credibility of each scheme relative Link Importance；Finally, level is carried out total
Sequence, can obtain each key element on current layer for last layer generally speaking the most important from the beginning of destination layer top-downly
Degree；What importance degree was the highest is i.e. gear hobbing process process parameter optimizing decision scheme.
2. hobbing method for processing as claimed in claim 1, it is characterised in that gear hobbing process technology field ontology definition is as follows:
Gear hobbing process technology field body is to retouch a kind of detailed characterization of concept present in gear hobbing process technology field
State, i.e. gear hobbing process technology field body is to the concept in gear hobbing process technology field, relation, attribute, rule and example five
A kind of description of key element, is to realize the basis that domain knowledge is shared and reused；During wherein concept refers to gear hobbing process technology field
Term normalized, that generally acknowledge, is the set with same alike result or object of action；It is except referring to concept in general sense,
Also include the task of gear hobbing process process aspect, function and behavior；Relation refers to the connection between field concept or association；Relation is deposited
It is between multiple concept；Relation basis presented in concept, can constitute new during conceptualization between relation
Relation.
3. hobbing method for processing as claimed in claim 2, it is characterised in that described BNF form is to described gear hobbing process
The formalized description of technology field ontology definition, is the representation of knowledge of domain body, is also the basis of ontological construction；Its BNF model
Formula is as follows:
(1)<gear hobbing process technology field body>: :=(<domain name>,<concept>,<relation>,<attribute>,<rule>,<real
Example >)；
(2)<concept>: :=(<concept number>,<concept name>, [<synonym>], [<initialism>],<conceptual description>, [<this is general
The parent number read>], [<art title>])；
(3)<relation>: :=(<relation number>,<relation name>,<relation former piece>,<relation consequent>,<relationship description>)；
(4)<attribute>: :=(<attribute number>,<said concepts number>,<relation number>,<Property Name>, [<synonym>], [<breviary
Word>],<type of value>,<scope of collection>)；
(5)<rule>: :=(<rule number>,<said concepts number>,<relation number>,<rule describes>,<credibility>)；
(6)<example>: :=(<instance number>,<instance name>,<problem description>,<instance properties number>,<instance properties value>,<
Example rule number >)；
(7)<domain name>: :=<indications>.
4. hobbing method for processing as claimed in claim 1, it is characterised in that use top down method to set up gear hobbing process work
The concept classification level of skill domain body, from the beginning of i.e. maximum from gear hobbing process technology field concept, will by adding subclass
These concepts refine, then with field concept body tree representation.
5. hobbing method for processing as claimed in claim 1, it is characterised in that the destination layer in described step (2) is that gear hobbing adds
Work process parameter optimizing decision-making；The key element evaluating layer includes crudy, process time, processing cost, resource consumption and ring
Border affects；Solution layer is scheme 1, scheme 2, scheme num, num is natural number, and its attribute includes hobboing cutter precision, hobboing cutter
Head number, hobboing cutter helical 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, it is characterised in that
The concrete steps of the solution layer decision combinations schemes generation in described step (2) include,
Step1 policymaker retrieve according to the equal rule of modulus in ontology library, selects the g of coupling hobboing cutter, extraction hobboing cutter precision,
Hob head number, three parameters of hobboing cutter helical angle, obtain solution layer decision scheme matrix A (g, 3)；
Step2 utilizes the method for case-based reasoning, construction meta-modelWherein, n=
6, N0Represent that decision objective name in a name space claims, pi 0, i=1,2 ..., n represents the genus being not belonging to hobboing cutter basic parameter in solution layer
Property, it is matter-element feature, vi 0, i=1,2 ..., n is N0About pi 0, i=1,2 ..., value two tuple of n, it is expressed as vi 0=＜
vil 0,vih 0＞, i=1,2 ..., n, vil 0、vih 0Represent pi 0, i=1,2 ..., the optimization of n is interval；
Step3 utilizes similarity formula:Calculate the history processing in the r case library
Example optimizes interval similarity about the i-th of i-th matter-element feature with decision objective space；The interval of Similarity value is
[0,1], is worth the biggest expression similarity the highest；
Step4 gives each matter-element feature plus weight wi, i=1,2 ..., n, calculate comprehensive similarityDetermine threshold value κ, select k the history processing instance that similarity is higher, extract hobboing cutter rotating speed out, walk
Cutter number of times, axial feed velocity, radial feed speed, workpiece rotational frequency, alter six parameters of cutter amount, obtain solution layer decision scheme square
Battle array B (k, 6)；
Solution layer decision scheme matrix A (g, 3) and B (k, 6) are combined by Step5, form g × k kind solution layer decision combinations scheme.
7. hobbing method for processing as claimed in claim 1, it is characterised in that
The complex optimum decision-making concrete steps of the gear hobbing process technological parameter in described step (3) include,
Step1 sets and is compared the entitled EVA0 of layer, and being compared layer key element is ELE0, compares the entitled EVA1 of layer, compares layer key element and is
ELE1, EVA0=destination layer, ELE0=gear hobbing process process parameter optimizing decision-making, EVA1=evaluates layer, ELE1=crudy,
Process time, processing cost, resource consumption, environmental effect；Judgement symbol ELEFlag=0；
Step2 according to deposit index table, for the ELE0 attribute of EVA0 by the ELE1 of EVA1 between compare two-by-two,
To the EVA1 judgment matrix to EVA0
I, j=1,2 ..., N；Wherein, aijFor in EVA1 for the i-th key element for certain key element in EVA0 to jth key element
Fiducial value, aji=1/aij, i ≠ j, N are the number of key element in EVA1, are i.e. the numbers of attribute in ELE1；
Step3 is according to judgment matrix HS, calculate HSThe product of every a line various elementThen,
Calculate MiNth power rootThen, rightIt is normalized, obtains each component of the characteristic vector of judgment matrixThen evaluate layer about HSRelative Link Importance be (W1,W2,…,WN), finally, calculate HSMaximum characteristic rootW=(W1,W2,…,WN)T, (AW)i=HSThe sum of products of the i-th row data and W respective items；Obtain EVA1 pair
The hierarchical ranking table of certain key element in EVA0；
Step4 carries out consistency check, wherein CI=(λ according to CR=CI/RImax-N)/(N-1), work as HSThere is crash consistency
Time, CI=0；RI is HSMean random index, as CR ＜ 0.1, then it is assumed that judgment matrix has satisfactory consistency, calculating relative
Importance degree is also acceptable, otherwise, revises judgment matrix, proceeds to Step2；
If Step5 is ELEFlag=0, then EVA0=evaluates layer, ELE0=crudy, EVA1=solution layer, ELE1=side
Case 1, scheme 2, scheme num, ELEFlag=ELEFlag+1, proceed to Step2；
If Step6 is ELEFlag=1, then ELE0=process time, ELEFlag=ELEFlag+1, proceed to Step2；
If Step7 is ELEFlag=2, then ELE0=processing cost, ELEFlag=ELEFlag+1, proceed to Step2；
If Step8 is ELEFlag=3, then ELE0=resource consumption, ELEFlag=ELEFlag+1, proceed to Step2；
If Step9 is ELEFlag=4, then ELE0=environmental effect, ELEFlag=ELEFlag+1, proceed to Step2；
Step10, from the beginning of destination layer, obtains each key element on current layer for last layer time generally speaking comprehensive top-downly
Importance degree, its computing formula is:apiIt is to evaluate each key element of layer about destination layer
The relative Link Importance of key element, bpj iIt is the solution layer each key element relative Link Importance about evaluation layer key element, the most tries to achieve；Obtain
The solution layer total hierarchial sorting table to destination layer, selects the scheme that importance degree is the highest, is i.e. the comprehensive of gear hobbing process technological parameter
Optimal Decision-making result.
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| CN104881530B (en) * | 2015-05-18 | 2017-10-20 | 重庆大学 | A kind of dry cutting processing method of gear hobbing based on Optimizing Process Parameters |
| CN104809313B (en) * | 2015-05-18 | 2017-10-13 | 重庆大学 | A kind of dry cutting processing method of gear hobbing based on the polynary Parameters Analysis optimization of small sample |
| CN107480317A (en) * | 2017-06-09 | 2017-12-15 | 合肥工业大学 | A kind of method for improving gear hobbing process precision |
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