Body-based intelligent reasoning method for box plane machining process parameters

Abstract

The invention belongs to the technical field of box part process decision and computer aided process design (CAPP), and relates to an intelligent reasoning method for box plane machining process parameters based on a body. The method specifically comprises the following steps that 1, a box plane machining technological parameter generation body is constructed; step 2, establishing an SWRL inference rule generated by box plane processing technological parameters; 3, analyzing box plane machining process parameters and establishing an assertion formula set APS; 4, box plane machining feature information is extracted, and an assertion formula set ACS and an assertion formula set ACF are constructed; and 5, constructing an instantiated ontology knowledge base generated by the box body plane machining process parameters, and reasoning the instantiated ontology knowledge base by utilizing a reasoning engine according to the SWRL reasoning rule base to generate the box body plane machining process parameters.

Description

Technical Field

[0001] The present invention belongs to the technical field of process decision-making for box parts and computer-aided process planning (CAPP), and relates to an intelligent reasoning method for process parameters of box plane machining based on ontology. Background Art

[0002] Intelligent and digital manufacturing applies advanced manufacturing technology and digital technology to the entire life cycle of products to realize product design and process planning. Process planning for machining is to determine a reasonable process route, process parameters, and equipment selection according to the machining requirements of specific products. Nowadays, in the machining of mechanical products, process planning is basically completed manually by process planners or semi-automatically with the help of different tools. Due to the inconsistent experience of different process planners, there are problems such as messy process data and low intelligence level. Especially, the method of selecting machining process parameters is decided by process planners, resulting in low production efficiency and standardization level. At the same time, these process experiences are also difficult to transmit and share in the computer.

[0003] To address the problems of low efficiency and uncertainty in manual decision-making of machining process parameters and the difficulty of sharing and transmitting relevant process knowledge between heterogeneous CAPP systems, the intelligence of machining process parameter decision-making is provided, and ontology technology is introduced into the decision-making of process parameters for box plane process routes. First, an ontology for generating process parameters of box plane machining is constructed using the Ontology Web Language (OWL). Then, inference rules related to the generation of process parameters for box plane machining are constructed through the Semantic Web Rule Language (SWRL). Finally, according to the constructed SWRL inference rules, an inference engine is used to automatically generate process parameter information for box plane machining. The realization of automatic generation of process parameters for box plane machining is conducive to the sharing and reuse of process experience knowledge related to box plane machining process parameters, and improves the intelligence of process parameter decision-making. Summary of the Invention

[0004] The technical problem to be solved by the present invention is the low intelligence level in the decision-making of existing process parameters and process routes for box plane machining. The present invention proposes an intelligent reasoning method for process parameters of box plane machining based on ontology. To solve the above problems, the present invention is realized through the following technical solutions, including the following steps: Step 1: According to the domain knowledge of the decision-making process of box plane machining process parameters, construct an ontology knowledge base system for box plane machining process parameters. The attribute relationships between classes in the knowledge base provide a consistent description of the domain knowledge and experience of the decision-making process of box plane machining process parameters. Step 2: Based on the ontology knowledge base constructed in Step 1, construct a SWRL rule base for intelligent reasoning of the process parameters for machining the box body plane according to the domain knowledge of the decision-making process of the process parameters for machining the box body plane; Step 3: For the decision-making instance of the process parameters for machining the box body plane, establish a part drawing model and extract the part structure to obtain several surface information. Represent the constraint relationship between the box body and its machining surface of the instance through OWL and establish an assertion formula set A PS ; Step 4: According to the part drawing model established in Step 3, extract the design feature information related to the process parameters for machining the box body plane, and establish an assertion formula set A CS and the assertion formula set A CF to represent the subordinate relationship between the part and these machining feature information; Step 5: According to the assertion formula set A CS , the assertion formula set A PF and the part drawing model established in Step 3, instantiate the relevant concept and attribute information respectively, construct the classes and subclasses, individuals, and the relationships between individuals in the generation instance of the process parameters for machining the box body plane, and obtain the instantiated ontology knowledge base for generating the process parameters for machining the box body plane; use the established SWRL rule base for intelligent reasoning of the process parameters for machining the box body plane, and use the Drools inference engine to reason about the instantiated ontology knowledge base obtained in Step 5 to generate the process parameters for machining the instance box body plane.

[0005] Step 1 includes: Step 1.1: According to the relevant machining process experience of the decision-making of the process parameters for machining the box body plane, construct the constraint relationship between classes and the inherent attributes of the classes based on ontology for the process parameters for machining the box body plane, where the attributes of the classes include two types: object attributes and data attributes; Step 1.2: Use the OWL2 language to construct a terminological axiom set TBOX for the process parameters for machining the plane based on ontology, and represent and embody TBOX in the form of the child nodes of the parent class, the inherent attributes of the class, and semantic knowledge, so as to form an ontology knowledge base system for the process parameters for machining the box body plane.

[0006] The specific content of Step 2 is: Step 2.1: Construct an ontology model for generating the process parameters for machining the box body plane.

[0007] Step 2.2: Construct the reasoning rules for the decision-making of the process parameters for machining the box body plane.

[0008] Compared with the prior art, the present invention has the following characteristics: 1. The ontology knowledge base and the reasoning rule base for the decision-making and reasoning of the process parameters for machining the box body plane can be shared and reused, ensuring the portability and scalability of the process knowledge.

[0009] 2. Utilize the domain knowledge of the process parameters for box body plane machining and the corresponding constraint relationships to make the hierarchical relationship of the intelligent reasoning of the process parameters for box body plane machining based on ontology clear and better interpreted by the computer.

[0010] 3. Constructed a knowledge base description framework and an intelligent reasoning rule base for the intelligent reasoning of the process parameters for box body plane machining based on ontology, providing a feasible method for the intelligent reasoning of the process parameters for box body plane machining based on ontology. Description of the Drawings

[0011] Figure 1 It is the intelligent reasoning flowchart of the process parameters for box body plane machining.

[0012] Figure 2 It is the hierarchical relationship of the decision-making classes of the process parameters for box body plane machining.

[0013] Figure 3 It is the hierarchical relationship of the decision-making attributes of the process parameters for box body plane machining.

[0014] Figure 4 It is the instance diagram of the box body part.

[0015] Figure 5 It is the inference result diagram of the process parameters. Detailed Implementation Modes

[0016] The following takes the box body part as an example for a brief description, as shown in the attached Figure 4 figures. The intelligent reasoning process of the process parameters for box body plane machining based on ontology is as shown in the attached Figure 1 figures, and specifically includes the following steps: Step 1: Consult the relevant process knowledge in the field of decision-making design of the process parameters for box body plane machining. According to the document materials, expert experience knowledge and reusable ontologies in this field, obtain the domain knowledge of the decision-making design of the process parameters for box body plane machining. According to the obtained domain knowledge of the decision-making design of the process parameters for box body plane machining, construct the classes for generating the process parameters for box body plane machining and their hierarchical relationships; first, define the important classes in the domain, as well as the class attributes and the relationships between classes. Classes are the core of ontology, used to describe the concepts in the domain. Based on these explicitly defined classes and relationships, implicit knowledge can be obtained through a certain reasoning mechanism.

[0017] Some ontology classes and the hierarchical relationships between classes are as shown in the attached Figure 2 figures, where owl:thing is an abstract class in the ontology editing tool Protégé and is the parent class of all classes; its subclasses Parts, FlatDF, PartDF, ProcessProgram, and PSParameter represent box body parts, machined surface features, part features, machining plans, and process parameters respectively.

[0018] The subclasses of Parts include MachinedFlat; the subclasses of FlatDF include geometric tolerance constraint (FPTolerance), accuracy class (AClass), nominal size (PSize), positioning datum (PDatum), surface roughness (AClass); the subclasses of PartDF include heat treatment (HTreat), part material (PMaterial), casting method (CMethod); the subclasses of ProcessProgram include flat machining plan (FlatMM), machining method (FlatMP); the subclasses of PSParameter include process allowance (PAllowance), total machining allowance (TMAllowance), process dimension (PBDimension).

[0019] In the field of decision-making for process parameters of box body plane machining, attributes are an important part for the establishment of the ontology knowledge base. Concepts are difficult to fully express all the information content of the concept, so it is necessary to define the specific attributes of the concept; when constructing the ontology of ontology languages and tools, object-type attributes and data-type attributes are mainly defined; OWL is used to represent the ontology model and establish the attribute relationships between concepts.

[0020] The attributes of classes are used to describe the relationships between the same and different classes in the field of decision-making for process parameters of box body plane machining; these attributes include object-type attributes (Object-Property) and data-type attributes (Datatype-Property). Object-type attributes are used to express the constraint relationships between classes, while data-type attributes are used to represent the inherent numerical characteristic attributes of classes, generally used to represent the numerical magnitudes and characteristics of specific classes, usually including integer (int) and floating-point (float), and strings (string) as shown in the appendix Figure 3 as follows.

[0021] Step 2: Based on the ontology knowledge base constructed in Step 1, according to the domain knowledge of the design process of box body plane machining process parameters, construct a SWRL rule base for intelligent reasoning of box body plane machining process parameters; representative rules are as follows: Rule1-1: Part(?x) ^ ProductionType(?y) ^ hasPT(?x,Lb) ->isCMof(?x, SmM) Rule1-2: Parts(?x) ^ isPMof(?x,Gcr) ^ isCMof(?x,SmM) -> Value_DTG(?x,10) ^ isMAGof(?x,G) Rule 1-1 describes the relationship between the production type of the box part and the casting method. If the production type of the box part is mass production, the casting method is sand mold machine molding. Rule 1-2 describes the selection of the blank parameters of the box part. If the material of the box part is gray cast iron and the casting method is sand mold machine molding, the dimensional tolerance grade of the blank is CT10, and the machining allowance grade is G.

[0022] Rule2-1: Part(?x) ^ RoughDatum(?y) ^ hasRD(?x,?y) ->hasRD(?x,Sh) Rule2-2: Part(?x) ^ PType(?y) ^ hasPT(?x,Lb) ->hasFD(?x,1f2h) Rules 2-1 and 2-2 describe the selection of the positioning reference during the machining of the box plane. If the production type of the box part is mass production, the rough reference is the spindle hole, and the fine reference is one plane and two holes.

[0023] Rule3-1: Part(?x)^isPMof(?x,Gcr)^isHTof(?x,Na)^MachinedFlat(?y)^Value_Ra(?y,?b)^swrlb:lessThanOrEqual(?b,12.5)^swrlb:greaterThan(?b,3.2)^isITof(?x,11)^hasFPT(?x,PA)^Value_TG(?y,?bswrlb:lessThanOrEqual(?b,9)^swrlb:greaterThan(?b,6)->hasCMP(?y,CMP2)^hasCMM1(?y,Rm)^hasCMM2(?y,SFm) Rule 3-1 describes the selection of the machining plan for the box plane. If the box material is gray cast iron, the heat treatment is natural aging treatment, the surface roughness range of the machined surface is 3.2 - 12.5, the accuracy grade is 11, the geometric constraint is parallelism, and the grade range is 6 - 9, then the machining plan for the machined surface is CMP2, and the machining methods are rough milling and semi-finish milling in sequence.

[0024] Rule4-1: MachinedFlat (?x)^Value_SBD(?x,?v)^swrlb:greaterThan(?v,0)^swrlb:lessThanOrEqual(?v,100)^CasePart(?y)^isMAGof(?y,G)^Value_DTG(?y,10)->Value_TMA(?x,3.5) Rule 4-2: MachinedFlat(?x) ^ Value_SL(?x,?b) ^ swrlb:greaterThan(?b, 0) ^ swrlb:lessThanOrEqual(?b, 300) ^ Value_SL(?x,?c) ^ swrlb:greaterThan(?c, 0) ^ swrlb:lessThanOrEqual(?c, 100) ^ hasCMP(?x, CMP2) -> Value_SFMA(?x, 1.3) Rule 4-3: MachinedFlat (?x) ^ hasCMP(?x, CMP1) ^ Value_TMA(?x,?m) ^ Value_SFMA(?x,?n) ^ swrlb:subtract(?t,?m,?n) -> Value_RMA(?x,?t) Rule 4-1 describes the total machining allowance for the machined flat surface of the box body. If the nominal size of the machined surface is 0 - 100 mm, the tolerance grade of the blank size is 10, and the machining allowance grade is G, then the total machining allowance for the machined surface is 3.5 mm. Rules 4-2 and 4-3 describe the machining allowances for each process of the machined flat surface of the box body. If the size of the machined surface is 100 * 300 mm and the machining plan is CMP1, then the allowance for the semi-finishing milling process is 1.3 mm, and the allowance for the rough milling process is the total machining allowance - the allowance for the semi-finishing milling process.

[0025] Rule 5-1: MachinedFlat(?x) ^ hasCMP(?x, CMP1) ^ Value_SBD(?x,?m) -> Value_SFMD(?x,?m) Rule 5-2: MachinedFlat(?x) ^ hasCMP(?x, CMP1) ^ Value_SFMA(?x,?a) ^ Value_SFMD(?x,?b) ^ swrlb:add(?m,?a,?b) -> Value_RMD(?x,?m) Rules 5-1 and 5-2 describe the process dimensions of the machined flat surface of the box body. If the machining plan for the machined surface is CMP1, the dimension of the semi-finishing milling process is the basic dimension of the machined surface, and the dimension of the rough milling process is the allowance for the semi-finishing milling process + the dimension of the semi-finishing milling process.

[0026] Rule6-1: MachinedFlat(?x) ^ Value_SBD(?x,?v) ^ swrlb:greaterThan(?v, 30) ^ swrlb:lessThanOrEqual(?v, 120) ^ hasCMP(?x, CMP1) ^ Part(?y) ^ isITof(?y, f) -> Value_RMDT(?x, 0.3) ^ Value_SFMDT(?x, 0.15) Rule 6-1 describes the process dimension tolerance of the box body plane. The nominal dimension of the machined surface is 30 - 120 mm, the tolerance grade of the box body part is 11, the machining process of the machined surface is CMP1, then the dimension tolerance of the rough milling process is 0.3 mm, and the dimension tolerance of the semi-finish milling process is 0.15 mm.

[0027] Step 3: For the decision-making example of the box body plane machining process parameters, establish a part drawing model and extract the part structure, obtain several surface information, represent the constraint relationship between the box body part and its machining surface of the instance through OWL and establish an assertion formula set A PS 。

[0028] Assertion formula set A PS : A PS = { MachinedFlat (SA), MachinedFlat (SB), MachinedFlat (SC), MachinedFlat (SD)} 。

[0029] Step 4: According to the part drawing model established in Step 3, extract the part feature information related to the decision-making of the box body plane machining process parameters, establish the assertion formula set A CS and the assertion formula set A CF represent the subordinate relationship between the part and these machining feature information.

[0030] Assertion formula set A CS : A CS = { Parts(p1), hasPT (Lb), isPMof(Gcr), isHTof(Na)} ; Assertion formula set A CF : A CF = {Value_Of_Ra(SA, 1.6), Value_Of_Ra(SB, 6.3), Value_Of_Ra(SC, 0.8), Value_Of_Ra(SD, 6.3),Value_Of_SBD(SA, 80), Value_Of_SBD(SB, 80), Value_ Of_SBD(SC, 58), Value_Of_SBD(SD, 38), Value_SL(SA, 142), Value_SL(SB, 30), Value_SL(SC, 45), Value_SL(SD, 30), Value_SW(SA, 69), Value_SW(SB, 30), Value_SW(SC, 45), Value_SW(SD, 30), Value_IT(SA, f), Value_IT(SB, f), Value_ IT(SC, f), Value_IT(SD, f), hasFPT(SA,FN), hasFPT(SB,PA), hasFPT (SC,SQ), Value_TG(SA, 7), Value_ TG (SB, 8), Value_ TG (SC, 11)} 。

[0031] Step 5: According to the assertion formula set A established in Step 3 CS 、the assertion formula set A PFFor the part drawing model, relevant concepts and attribute information are instantiated respectively to construct the classes, subclasses, individuals, and relationships between individuals in the generation instance of the box body plane machining process parameters, and the ontology knowledge base of the generated instance of the box body plane machining process parameters is obtained; an SWRL rule base for intelligent reasoning of the box body plane machining process parameters is established, and the Drools inference engine is used to reason about the instantiated ontology knowledge base obtained in step 5 to generate the instance box body plane machining process parameters.

[0032] To ensure the integrity of the constructed decision-making ontology knowledge base of the box body plane machining process parameters, all individuals belonging to a class need to be added to the constructed class. Add individual CP under class Parts, add individuals SA, SB, SC, SD under class MachinedFlat; add individuals As, 1f2h under class PDatum; add individuals Ma, Na under class HTreat; add individuals Sb, Lb under class PMaterial; add individuals FinishMilling, Grinding, RoughMilling, Semi-finishMilling under class FlatMP; add individuals SmH, SmM under class CMethod.

[0033] According to the SWRL rule base for intelligent reasoning of the box body plane machining process parameters established in step 2, the Drools inference engine is used to reason about the instantiated ontology knowledge base obtained in step 5. The reasoning process is implemented in the software protégé, and the obtained instance box body plane machining process parameter reasoning results are added to the instantiated ontology knowledge base. The process parameter reasoning results are as Figure 5 shown.

[0034] The present invention uses OWL to define the ontology and inference rules for intelligent reasoning of the box body plane machining process parameters. Various characteristic information of the box body part and its machining plane are described by dimensions, precision, materials, tolerance grades, etc., and the design intention can be retrieved from the design characteristic information. The rule semantic reasoning mechanism is used to reason about the decision-making ontology knowledge base of the box body plane machining process parameters to identify the design intention, thereby improving the sharing and reuse of the box body plane machining process parameter decision model.

[0035] The above content is a further detailed description of the present invention in combination with specific engineering examples. It cannot be determined that the specific implementation manner of the present invention is limited to this. For those of ordinary skill in the technical field to which the present invention belongs, without departing from the concept of the present invention, several simple deductions or substitutions can still be made, which should all be regarded as belonging to the protection scope of the present invention.

Claims

1. An intelligent reasoning method for box plane machining process parameters based on ontology, characterized in that: It consists of the following steps: Step 1: Based on the domain knowledge of the decision-making process of box plane machining process parameters, a knowledge base system of box plane machining process parameters ontology is constructed. The attribute relationship between classes in the knowledge base provides a consistent description of the domain knowledge experience of the decision-making process of box plane machining process parameters; Step 2: Based on the ontology knowledge base constructed in step 1, and according to the domain knowledge of the decision-making process of the box plane machining process parameters, a SWRL rule base for intelligent reasoning of the box plane machining process parameters is constructed; Step 3: For the case of box plane machining process parameter decision, establish a part drawing model and extract the part structure to obtain the machining surface information. Use OWL to represent the constraint relationship between the box part and its machining surface and establish the assertion formula set A. PS ; Step 4: Based on the part drawing model established in step 3, extract the design feature information related to the box plane processing parameters and establish the assertion formula set A CS and assertion formula set A CF Indicates the subordinate relationship between parts and these processing feature information; Step 5: According to the assertion formula set A established in step 3 CS , assertion formula set A PF and part drawing model, instantiate the relevant concepts and attribute information respectively, construct the classes and subclasses, individuals and the relationships between individuals in the box plane machining process parameters generation instance, and obtain the instantiated ontology knowledge base for the box plane machining process parameters generation; establish the SWRL rule base for intelligent reasoning of the box plane machining process parameters, use the Drools inference engine to reason on the instantiated ontology knowledge base obtained in step 5, and generate the instance box plane machining process parameters.

2. According to the ontology-based intelligent reasoning method for box plane processing parameters of claim 1, it is characterized in that: In step 1, the top-level classes in the knowledge base of the box plane machining process parameters generated based on the ontology include: box parts, machining planes, part features, machining plans and process parameters.

3. According to the ontology-based intelligent reasoning method for box plane processing parameters of claim 1, it is characterized in that Step 1 specifically includes: Step 1.1: Based on the relevant mechanical processing experience of the box plane processing parameter decision, the constraint relationship between the classes of the box plane processing parameter based on the ontology and the inherent attributes of the class are constructed, where the class attributes include object attributes and data attributes; Step 1.2: Use OWL2 language to construct the box plane processing parameter terminology axiom set TBOX based on ontology, and express TBOX in the form of child nodes of the parent class, the inherent attributes of the class and based on semantic knowledge, thus forming a box plane processing parameter ontology knowledge base system.

4. According to the ontology-based intelligent reasoning method for box plane processing parameters of claim 1, it is characterized in that: Step 3 further includes the following process: using the reasoning ability of the instantiated ontology knowledge base itself to infer implicit relationships and verifying the consistency of the instantiated ontology knowledge base.

5. According to the ontology-based intelligent reasoning method for box plane machining process parameters of claim 1, it is characterized in that: When the ontology-based intelligent reasoning method of box plane machining process parameters is applied to the engineering example, the engineering case and the reasoning result can be added to the instantiated ontology knowledge base, which can realize the self-update of the knowledge base.

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