Aero-engine part characteristic process parameter design evaluation method and related device

Abstract

The invention discloses an aero-engine part characteristic process parameter design evaluation method and a related device, and relates to the field of aero-engine part process parameter evaluation, and the method comprises the steps: carrying out the retrieval of similar cases according to a machining characteristic type, a machining material and a machining characteristic type inputted by a user, and obtaining the similar cases; rewriting cutting parameters of the similar cases to obtain rewritten cases; according to the parameter optimization model, optimizing cutting parameters by using a particle swarm algorithm to obtain an optimization case; and performing case evaluation on the rewritten cases and the optimized cases to obtain scores of the cases. According to the method, the technological parameters meeting the user input requirements can be rapidly determined from the existing technological parameter cases and the cases optimized by applying the optimization algorithm, the design period of the aero-engine part feature machining technological scheme can be shortened, and the technological parameter selection efficiency can be improved.

Description

Technical Field

[0001] The present application relates to the field of process parameter evaluation of aircraft engine parts, and in particular to a method for designing and evaluating characteristic process parameters of aircraft engine parts and related devices. Background Art

[0002] Aircraft engines are typical difficult-to-machine components in the aerospace field, which include a variety of typical difficult-to-machine parts, such as blades, blisks, casings, etc. Typical difficult-to-machine parts of aircraft engines also include a variety of typical difficult-to-machine features, such as blade surfaces, stiffeners, flow channels, etc. Due to the lack of systematic sorting, summarization and evaluation of relevant processing data knowledge, and the incomplete enabling tools such as data knowledge reuse reasoning and rapid programming, the procedures ultimately used for feature processing vary from person to person, resulting in a lack of stability and consistency in the processing of typical parts, which has brought challenges to the stability of mass production model manufacturing and the progress of research and development models.

[0003] At present, in terms of the knowledge modeling method and system design of hierarchical process data in aero-engine manufacturing, there is no effective data processing method from materials to features and parts at different levels, which is insufficient to support the high-quality manufacturing of engines. Specifically, the basic data and expert knowledge data of typical processing features of aero-engines are huge, diverse in structure, and complex in association rules. In order to effectively organize and manage these data, it is necessary to carry out research on related technologies, establish a scientific data knowledge expression model, and then complete the design of the expert system for processing typical features of difficult-to-process parts of aero-engines. Taking the invention "CNC machining process quality evaluation method, device, equipment and medium" (application number 202210695676.1) as an example, the optimization subject of this evaluation method is parts, and it does not start from the detailed features of parts. Moreover, the evaluation subject is the quality of CNC machining process, not the more detailed machining process parameters, and it is not possible to optimize the process parameters in a targeted manner at the feature level; taking the invention "A method for evaluating the greenness of cold cutting process of mechanical parts" (application number 201610905656.7) as an example, this invention only targets single cold cutting processing, takes green processing as the goal, and evaluates and optimizes the process route.

[0004] For the process parameter design of typical part features, if traditional feature process parameter planning is adopted, it must rely on the experience of process developers, and the processing quality is difficult to guarantee. It is easy to have a variety of processing defects, resulting in poor product consistency, low processing quality and other problems, such as Figure 1 shown.

[0005] In order to achieve efficient and precise machining of typical features of difficult-to-machine parts of aircraft engines, optimize the manufacturing of complex parts, shorten the process design cycle, reduce the process design cost, and thus achieve the goal of comprehensively improving process design capabilities, the present invention provides a method and related device for designing and evaluating feature process parameters of aircraft engine parts. Summary of the invention

[0006] The purpose of this application is to provide a method and related device for design and evaluation of characteristic process parameters of aircraft engine parts, which can quickly determine the process parameters that meet user input requirements from existing process parameter cases and cases optimized by applying optimization algorithms, shorten the design cycle of characteristic process parameters of aircraft engine parts, and improve the efficiency of process parameter design.

[0007] To achieve the above objectives, this application provides the following solutions:

[0008] In a first aspect, the present application provides a method for designing and evaluating characteristic process parameters of aircraft engine parts, comprising:

[0009] According to the processing feature type and processing material or the processing feature type, processing material and processing target input by the user, similar cases are retrieved from the historical actual processing cases stored in the database and the rewritten cases stored in the case library to obtain similar cases; the processing feature type refers to the type of each component part of the aircraft engine; the historical actual processing case and the rewritten case include the processing feature type, processing material, machine tool information, tool information, working condition information, cutting parameters and processing results;

[0010] Rewriting the cutting parameters of the similar case to obtain a rewritten case;

[0011] According to the parameter optimization model, the particle swarm algorithm is applied to optimize the cutting parameters to obtain the cutting parameter optimization results; the parameter optimization model is determined according to the processing feature type and the processing material or the processing feature type, the processing material and the processing target; the parameter optimization model includes a surface processing quality model and a processing efficiency model; the expressions of the surface processing quality model and the processing efficiency model include cutting parameters;

[0012] A case evaluation is performed on the rewriting case and the optimization case, and case scores of the rewriting case and the optimization case are obtained to display the rewriting case and the optimization case and the corresponding case scores to the user; the optimization case includes the cutting parameter optimization result, the processing feature type, the processing material and the processing result; when the user's input includes a processing target, the processing result is determined according to the processing target; when the user's input does not include a processing target, the processing result is determined according to the default processing target parameters in the parameter optimization model; the default processing target parameters in the parameter optimization model include tool wear weight, cutting force weight and surface roughness weight.

[0013] In a second aspect, the present application provides a device for designing and evaluating characteristic process parameters of aircraft engine parts, comprising:

[0014] Database, used to store historical actual processing cases;

[0015] A case library for storing rewritten cases; the historical actual processing cases and the rewritten cases include processing feature types, processing materials, machine tool information, tool information, working condition information, cutting parameters and processing results;

[0016] A similar case retrieval module is used to retrieve similar cases from historical actual processing cases stored in the database and rewritten cases stored in the case library according to the processing feature type and processing material or the processing feature type, processing material and processing target input by the user, so as to obtain similar cases; the processing feature type refers to the type of each component of the aircraft engine;

[0017] A similar case rewriting module, used for rewriting cutting parameters of the similar case to obtain a rewritten case;

[0018] An optimization module is used to optimize cutting parameters by applying a particle swarm algorithm according to a parameter optimization model to obtain a cutting parameter optimization result; the parameter optimization model is determined according to the processing feature type and the processing material or the processing feature type, the processing material and the processing target; the parameter optimization model includes a surface processing quality model and a processing efficiency model; the expressions of the surface processing quality model and the processing efficiency model include cutting parameters;

[0019] A case evaluation module is used to perform case evaluation on the rewriting case and the optimization case, and obtain the case scores of the rewriting case and the optimization case to display the rewriting case and the optimization case and the corresponding case scores to the user; the optimization case includes the cutting parameter optimization result, the processing feature type and the processing material and the processing result; when the user's input includes the processing target, the processing result is determined according to the processing target; when the user's input does not include the processing target, the processing result is determined according to the default processing target parameters in the parameter optimization model; the default processing target parameters in the parameter optimization model include tool wear weight, cutting force weight and surface roughness weight.

[0020] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned method for designing and evaluating characteristic process parameters of aircraft engine parts.

[0021] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for designing and evaluating characteristic process parameters of aircraft engine parts.

[0022] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0023] The present application provides a method and related device for designing and evaluating characteristic process parameters of aircraft engine parts. On the one hand, similar cases that meet the user input requirements (processing feature type, processing material and processing target) are retrieved from existing cases in the database and case library, and process parameters (i.e. cutting parameters) are rewritten on the basis of similar cases to meet the user input requirements; on the other hand, based on the user's input requirements, the process parameters are optimized by using an optimization algorithm to obtain an optimized case that meets the user's input requirements. After that, the rewritten cases and the optimized cases are further evaluated to obtain the scores of each case. Subsequently, the user can refer to the required process parameters for manufacturing related part features according to the scores of each case. Obviously, the present invention can quickly determine the process parameters that meet the user input requirements and process parameters from the existing process parameter cases and the cases optimized by the optimization algorithm, which can shorten the design cycle of characteristic process parameters of aircraft engine parts and improve the efficiency of process parameter design. In addition, the optimization target of the present invention can be diversified, divided into two aspects: processing efficiency and surface processing quality, which can achieve the shortening of the process design cycle and complete the optimization target requirements in many aspects. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0025] Figure 1 This is a schematic diagram of flutter during milling of the blade body profile of an aircraft engine blade;

[0026] Figure 2 This is an application environment diagram of a method for designing and evaluating characteristic process parameters of aircraft engine parts in one embodiment of the present application;

[0027] Figure 3 A schematic diagram of a flow chart of a method for designing and evaluating characteristic process parameters of aircraft engine parts provided in one embodiment of the present application;

[0028] Figure 4 A schematic diagram of a technical route for a method for designing and evaluating characteristic process parameters of aircraft engine parts provided in one embodiment of the present application;

[0029] Figure 5 A database ER diagram provided for an embodiment of the present application;

[0030] Figure 6 A schematic diagram of a case search process provided in an embodiment of the present application;

[0031] Figure 7 A schematic diagram of a case optimization process provided in an embodiment of the present application;

[0032] Figure 8 A schematic diagram of setting weights for a parameter optimization model provided in one embodiment of the present application;

[0033] Fig. 9 A schematic diagram of particle swarm optimization provided in an embodiment of the present application;

[0034] Fig.10 A schematic diagram of functional modules of a device for designing and evaluating characteristic process parameters of aircraft engine parts provided by another embodiment of the present application;

[0035] Fig.11 A diagram of a database front-end interface provided for another embodiment of the present application;

[0036] Fig.12 A diagram of the case retrieval and rewriting front-end interface provided for another embodiment of the present application. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0038] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0039] The method for designing and evaluating characteristic process parameters of aircraft engine parts provided in the embodiment of the present application can be applied to Figure 2In the application environment shown. Among them, the terminal communicates with the expert system through the network. The data server set can store the data that the expert system needs to process. The data server set can be set up separately, integrated on the expert system, or placed on the cloud or other expert systems. The terminal can send the processing feature type and processing material or processing feature type, processing material and processing target input by the user to the expert system. After the expert system receives the processing feature type and processing material or processing feature type, processing material and processing target input by the user, the expert system retrieves similar cases from the historical actual processing cases stored in the database and the rewritten cases stored in the case library based on the processing feature type and processing material or processing feature type, processing material and processing target input by the user, and obtains similar cases; rewrites the cutting parameters of the similar cases to obtain rewritten cases; according to the parameter optimization model, the particle swarm algorithm is applied to optimize the cutting parameters to obtain the cutting parameter optimization results; the rewritten cases and the optimized cases are evaluated, and the case scores of the rewritten cases and the optimized cases are obtained to display the rewritten cases and the optimized cases and the corresponding case scores to the user. The expert system may feed back the obtained rewriting cases and optimization cases and corresponding scores to the terminal.

[0040] The terminal may be, but is not limited to, various desktop computers, laptops, smart phones, tablet computers, and portable IoT devices.

[0041] In an exemplary embodiment, Figure 3 and Figure 4 As shown, a method for designing and evaluating characteristic process parameters of aircraft engine parts is provided. The method is executed by a computer device, and specifically can be executed by a computer device such as an expert system. In the embodiment of the present application, the method is applied to Figure 2 The expert system in is used as an example to illustrate, including the following steps 101 to 104.

[0042] Step 101, based on the processing feature type and processing material or the processing feature type, processing material and processing target input by the user, similar cases are retrieved from the historical actual processing cases stored in the database and the rewritten cases stored in the case library to obtain similar cases; the processing feature type refers to the type of each component part of the aircraft engine; the historical actual processing case and the rewritten case include the processing feature type, processing material, machine tool information, tool information, working condition information, cutting parameters and processing results.

[0043] The information stored in the database can be in the form of an ER diagram (entity-relationship diagram), such as Figure 5As shown in the figure, the database field information entities include: feature information, process method, machine tool information, tool information, working condition information, cutting parameters and processing results; processing feature information includes processing feature name (type) and processing material; there are 20 types of processing features of typical aircraft engine parts, such as boss, thin-walled ring, blade body surface, etc.; process methods include turning, milling, planing, grinding and drilling; machine tool information includes machine tool brand, machine tool type, spindle speed, spindle maximum power, etc.; tool information includes tool name, tool brand, tool type, tool diameter, tool R angle, etc.; working condition information includes roughing, semi-finishing and finishing; cutting parameters include cutting speed v c , axial cutting depth a p , radial cutting depth a e and feed per tooth f z ; The processing results include surface roughness R a , tool wear VB, cutting force F and machining deformation, etc. Among them, cutting parameters affect the machining results; select appropriate process methods, tools, machine tools and cutting parameters according to machining feature information and working condition information; machine tool information and tool information must match the process method to ensure that the equipment used meets the method requirements; cutting parameters must match the machine tool information to ensure that the machine tool performance meets the machining requirements.

[0044] Step 102, rewriting the cutting parameters of the similar case to obtain a rewritten case.

[0045] Step 103, according to the parameter optimization model, the particle swarm algorithm is applied to optimize the cutting parameters to obtain the cutting parameter optimization results; the parameter optimization model is determined according to the processing feature type and the processing material or the processing feature type, the processing material and the processing target; the parameter optimization model includes a surface processing quality model and a processing efficiency model; the expressions of the surface processing quality model and the processing efficiency model include cutting parameters.

[0046] Step 104, performing case evaluation on the rewriting case and the optimization case, obtaining case scores of the rewriting case and the optimization case to display the rewriting case and the optimization case and the corresponding case scores to the user; the optimization case includes the cutting parameter optimization result, the processing feature type and the processing material and the processing result; when the user's input includes a processing target, the processing result is determined according to the processing target; when the user's input does not include a processing target, the processing result is determined according to the default processing target parameters in the parameter optimization model; the default processing target parameters in the parameter optimization model include tool wear weight, cutting force weight and surface roughness weight.

[0047] Implementing the above steps 101 to 104, the present invention retrieves similar cases that meet the user input requirements (processing feature type, processing material and processing target) from the existing cases in the database and case library, rewrites the process parameters (i.e. cutting parameters) on the basis of similar cases to meet the user input requirements, and retrieves similar cases by building a database and case library, so as to rewrite the cases that are more in line with the user's needs based on the existing cases, thereby improving the efficiency of process parameter design; on the other hand, in order to ensure the number of cases to be evaluated, a process parameter optimization model is further constructed based on the user's input, and the process parameters are optimized using the optimization algorithm to obtain the optimized cases of the process parameters that meet the user input requirements. After that, the rewritten cases and the optimized cases are further evaluated to obtain the scores of each case. Subsequently, the user can refer to the required process parameters according to the scores of each case to manufacture the relevant part features. Obviously, the present invention can quickly determine the process parameters that meet the user input requirements from the existing process parameter cases and the cases optimized by the optimization algorithm, which can shorten the design cycle of the feature process parameters of the aircraft engine parts and improve the efficiency of the process parameter design. In addition, the optimization objectives of the present invention can be selected in a variety of ways, which are divided into two aspects: processing efficiency and surface processing quality. This can achieve the goal of achieving multiple optimization objectives on the basis of shortening the process design cycle.

[0048] In an exemplary embodiment of the present application, for the similar case search in step 101, if the user input only includes the processing feature type and the processing material, then according to the processing feature type and the processing material input by the user, a search is performed for similar cases from the historical actual processing cases stored in the database and the rewritten cases stored in the case library to obtain similar cases, specifically including:

[0049] (a1) According to the processing feature type input by the user, cases with the same processing feature type are screened out from the historical actual processing cases stored in the database and the rewritten cases stored in the case library to obtain a plurality of first screened cases.

[0050] (a2) From the first screening cases, select cases having the same or similar material mechanical properties as the processing material input by the user to obtain a plurality of second screening cases.

[0051] (a3) Filter out cases whose material mechanical properties are the same or whose material mechanical properties have a similarity greater than a preset similarity value from the second screening cases to obtain the similar cases.

[0052] In another exemplary embodiment of the present application, for the similar case search in step 101, Figure 6As shown, the user's input includes processing feature type, processing material and processing target. The same feature data is extracted one by one from the database and case library, and the material mechanical performance parameters such as material hardness and material tensile strength, surface roughness and processing efficiency and other processing target parameters are calculated locally. The above attributes (material mechanical performance parameters and processing target parameters) are weighted by the attribute importance scoring matrix completed in advance by the expert, so as to calculate the global similarity. The data with global similarity that meets the threshold requirement (for example, 90%) are selected and sorted by size for output. Therefore, as another optional implementation method, step 101, according to the processing feature type, processing material and processing target input by the user, similar cases are retrieved from the historical actual processing cases stored in the database and the rewritten cases stored in the case library to obtain similar cases, specifically including:

[0053] (b1) According to the processing feature type input by the user, cases with the same processing feature type are screened out from the historical actual processing cases stored in the database and the rewritten cases stored in the case library to obtain a plurality of third screened cases.

[0054] (b2) From the third screening cases, select cases having the same or similar material mechanical properties as the processing material input by the user to obtain a plurality of fourth screening cases.

[0055] (b3) From the third screening cases, cases that are the same as or similar to the processing target input by the user are screened out to obtain a plurality of fifth screening cases.

[0056] (b4) Calculating the global similarity between the processing material and the processing target in the fourth screening case.

[0057] (b5) Calculating the global similarity between the processing material and the processing target in the fifth screening case.

[0058] When calculating the global similarity, the global similarity is obtained by weighted summing the preset weight values ​​of the processing materials and processing targets and their corresponding local similarities.

[0059] (b6) From the fourth screening cases and the fifth screening cases, cases whose global similarity is greater than a preset global similarity are screened out to obtain the similar cases.

[0060] It should be noted that when searching for cases based on the cases in the case library and database, if the same case is retrieved, it can be used directly. If the same case is not found, a search for similar cases is performed.

[0061] In another exemplary embodiment of the present application, for the case rewriting in step 102, the cutting parameters of the similar case are rewritten to obtain the rewritten case, which specifically includes:

[0062] (1) For each similar case, the material similarity between the processed material in the similar case and the processed material input by the user is calculated.

[0063] (2) Determine the similarity of the feed amount per tooth between the similar case and the rewritten case based on the material similarity.

[0064] (3) Determining the cutting speed similarity between the similar case and the rewritten case according to the thermal conductivity ratio between the processing material in the similar case and the processing material input by the user; the cutting speed similarity is equal to the cutting depth similarity between the similar case and the rewritten case.

[0065] (4) Rewriting the cutting parameters in the similar case according to the similarity of the feed per tooth, the similarity of the cutting speed and the similarity of the cutting depth to obtain the rewritten case; the cutting parameters include cutting speed, feed per tooth, axial cutting depth and radial cutting depth.

[0066] Taking the blade surface characteristics TC4 titanium alloy processing data rewritten into TC17 titanium alloy processing data as an example, the relevant performance parameters of the two materials are shown in Tables 1 and 2 below.

[0067] Table 1 Mechanical properties parameters of TC4 titanium alloy materials

[0068]

[0069] Table 2 TC4 milling cutting parameter data

[0070] <![CDATA[Cutting speed v c > <![CDATA[Feed per tooth f z > <![CDATA[Axial depth of cut a p > <![CDATA[Radial depth of cut a e > 44m / min 0.027mm / z 0.5mm 0.5mm

[0071] Therefore, the material similarity m can be obtained by the following formula: m , similarity of feed per tooth m fz , cutting speed similarity m v , cutting depth similarity m a :

[0072]

[0073] m v =m a

[0074] Among them, the cutting speed similarity m v is the ratio of thermal conductivity of the two materials.

[0075] Where m1 is the heat generation ratio, m2 is the tensile strength ratio, m3 is the elastic modulus ratio, m4 is the aluminum content ratio, and k is the material correction factor (this time the value is 1.05); the calculation results are:

[0076] mm ≈0.86, m v ≈0.52. The TC17 rewritten case data (rewritten cutting parameters) can be obtained through parameter similarity calculation as shown in Table 3.

[0077] Table 3 Cutting parameters in the TC17 rewrite case

[0078] <![CDATA[Cutting speed v c > <![CDATA[Feed per tooth f z > <![CDATA[Axial depth of cut a p > <![CDATA[Radial depth of cut a e > 84.6m / min 0.023mm / z 1mm 1mm

[0079] It should be noted that when there is no data in the database and case library that meets the similarity conditions, case rewriting cannot be performed. In this case, the rule inference engine will be used to perform rule inference work and infer a reasonable range of processing parameters. For example, there will be a rough parameter range for cutting aluminum alloy materials. Its main purpose is to avoid the problem of no data output due to insufficient similarity and to provide users with a reasonable parameter range reference. When similar cases are continuously retrieved, case evaluation will no longer be performed, and the inferred processing parameter range will be displayed to users for reference.

[0080] In another exemplary embodiment of the present application, for the process parameter optimization process in step 103, as shown in FIG. Figure 7 As shown in the figure, experimental data were obtained through four-factor five-level orthogonal experiments (referring to the milling force measurement, surface quality measurement and machining deformation measurement under the changes of four cutting parameters). For each machining feature type, two process parameter optimization models were constructed through the response surface method: surface machining quality model and machining efficiency model. Taking the machining efficiency model and surface machining quality model of the blade profile feature of the impeller part as an example, the formulas of the machining efficiency model and surface machining quality model are as follows:

[0081]

[0082] in,

[0083]

[0084] The formula of the surface processing quality model is:

[0085]

[0086] in,

[0087]

[0088] Where: v c is the cutting speed, f z is the feed per tooth, a eis the radial cutting depth, β1 is the tool wear weight, β2 is the material weight, β3 is the cutting force weight, β4 is the surface roughness weight, g i (x) is the penalty function of each unknown in the blade profile processing efficiency model, t function is the cutting processing time function, d is the tool diameter, A is the milling area, t c is the tool change time, T is the tool life, Ra is the surface roughness, Fy is the y-direction cutting force, c i (x) is the penalty function of each unknown number in the surface quality model of the blade profile, and the F function is the main milling force function of the blade profile. Among them, the material weight β2 is adjusted according to the processing material input by the user. When the user's input includes the processing target, the specific weight values ​​of the parameters related to the processing target (processing result): tool wear weight β1, cutting force weight β3 and surface roughness weight β4 are adjusted according to the processing target. If the user does not enter the processing target, the weight values ​​of the parameters related to the processing target (processing result) are selected from the conventional setting values.

[0089] After determining the expression of the parameter optimization model, different weight coefficients of the surface quality model and the processing efficiency model are calculated according to the multiple processing targets input by the user; Figure 8 As shown in the figure, when the user subjectively assigns the four weights according to the actual situation. Determine the boundary conditions of the parameter optimization model according to the multiple processing goals input by the user; rewrite the parameter optimization model according to the calculated weight coefficient and the input boundary conditions (referring to the penalty function in the above formula); according to the parameter optimization model, use the PSO particle swarm optimization algorithm embedded in the program to optimize the cutting parameters, and finally obtain the optimal solution of the cutting parameters. Fig. 9 Schematic diagram of particle swarm optimization. Fig. 9 Represents an iterative optimization process of the particle swarm algorithm.

[0090] Therefore, in step 103, according to the parameter optimization model, the particle swarm algorithm is applied to optimize the cutting parameters to obtain the cutting parameter optimization result, which specifically includes:

[0091] (1) determining a parameter optimization model according to the processing feature type and the processing material or the processing feature type, the processing material and the processing target; when the user input includes the processing feature type and the processing material, the material weight parameter in the parameter optimization model is determined by the processing material input by the user, and the weight of the default processing target parameter in the parameter optimization model is selected from a preset weight value; when the user input also includes the processing target, the weight of the default processing target parameter in the parameter optimization model is determined by the processing target input by the user;

[0092] (2) The particle fitness is calculated using the parameter optimization model, and a particle swarm algorithm is applied to optimize the cutting parameters to obtain a cutting parameter optimization result; in the particle swarm algorithm, one particle is a numerical combination of a group of cutting parameters.

[0093] In another exemplary embodiment of the present application, the case evaluation in step 104 adopts a grey correlation analysis method:

[0094] (1) Establishment and standardization of evaluation index matrix.

[0095] a. Establish an evaluation index matrix:

[0096] Let U={u1,u2,...,u i , ..., u m} is a set of processing plans, where u i represents the i-th processing plan, and m represents the number of plans in the processing plan set. Let V = {v1, v2, ..., v i , ..., v n} is the evaluation index set, where v i Represents the result corresponding to the i-th evaluation index, and n is the total number of evaluation results, for example, v1: surface roughness Ra; v2: dimensional accuracy D i ; v3: tool back wear VB. v1, v2 represent the processing quality, v3 represents the tool life. The element pair (u i , v j ) form a Cartesian product set, specifying that the element pair (u i , v j ) is f ij , then m×n f ij The finishing evaluation index matrix F = (f ij ) m×n .

[0097] b. Standardization of evaluation indicators

[0098] Different evaluation indicators usually have different dimensions and orders of magnitude. In order to eliminate the influence and ensure the accuracy and reliability of the results, different methods are used to normalize different types of evaluation indicators.

[0099] (2) Establishment of the solution decision model

[0100] a. Grey correlation degree of the scheme

[0101] According to the grey correlation decision theory, the order of the processing schemes can be judged by comparing the correlation between each evaluation index vector and the evaluation index vector of the relatively optimal processing scheme. The larger the correlation, the closer it is to the optimal. Suppose the relatively optimal processing scheme is u0=(f 01 , f 02 , …, f 0n ), after normalization, we have u0=(1, 1, …, 1), and the processing plan u i Evaluation index v j The evaluation index v of the relative optimal processing solution u0 j The grey correlation degree between is:

[0102]

[0103] Where ξ∈(0,1) is the resolution coefficient, whose value is difficult to quantify and usually depends on experience, and is generally taken as 0.5, i=1,2,…,m; j=1,2,…,n.

[0104] b. Determine the indicator weight

[0105] c. Decision model

[0106] From the above analysis, we can know that the grey correlation matrix of the processing scheme can be expressed as:

[0107]

[0108] The weight vector of n evaluation indicators can be expressed as W = (w1, w2, ..., w n ), based on the grey correlation matrix and weight vector, the processing scheme u i The weighted correlation γ between the relative optimal processing solution u0 i Form the correlation vector γ′.

[0109] γ′=γW=(γ1,γ2,...,γ i ,..,γ m );

[0110] in, Weighted correlation γ i The larger the i The closer it is to the relatively optimal solution u0.

[0111] Therefore, step 104, performing case evaluation on the rewriting case and the optimization case to obtain case scores of the rewriting case and the optimization case, specifically includes:

[0112] (1) A processing evaluation index matrix is ​​constructed according to the rewriting case and the optimization case and their corresponding processing objectives; the elements in the processing evaluation index matrix are element pairs formed by any combination of each scheme to be evaluated and the evaluation index; the scheme to be evaluated refers to the rewriting case and the optimization case; and the evaluation index is determined according to the processing objectives.

[0113] (2) The processing evaluation index matrix is ​​standardized to obtain a preprocessed processing evaluation index matrix.

[0114] (3) The grey correlation between the evaluation index of each scheme to be evaluated and the evaluation index of the relatively optimal processing scheme is calculated according to the processing evaluation index matrix and the evaluation index corresponding to the relatively optimal processing scheme; the grey correlation corresponding to each scheme to be evaluated constitutes the grey correlation matrix of each scheme to be evaluated; the relatively optimal processing scheme is predefined.

[0115] (4) Determine the weight value of each evaluation indicator corresponding to the scheme to be evaluated.

[0116] (5) Calculating the weighted correlation between each of the schemes to be evaluated and the relatively optimal scheme according to the weight value and the grey correlation matrix; the weighted correlation corresponding to each of the schemes to be evaluated is the case score.

[0117] In another exemplary embodiment of the present application, after the cases are evaluated, the cases with the highest scores among the rewritten cases and the optimized cases are further stored in the case library. Therefore, after executing step 104 "evaluating the rewritten cases and the optimized cases to obtain the scores of the rewritten cases and the optimized cases", the aircraft engine part feature process parameter design evaluation method further includes:

[0118] (1) Sort the case scores from large to small.

[0119] (2) Select multiple cases with high case scores and store them in the case library.

[0120] The present application also provides an application scenario, which applies the above-mentioned aircraft engine parts characteristic process parameter design and evaluation method. Specifically: The aircraft engine parts characteristic process parameter design and evaluation method provided in this embodiment is mainly used in the preparation of new aircraft engine process regulations to provide appropriate process parameter references for existing processing procedures. This scenario includes the following links: analyzing part drawings, formulating process routes, determining processing equipment, determining processing allowances, determining process parameters, and compiling and approving. The video label processing method provided in this embodiment belongs to the link of determining process parameters.

[0121] Based on the same inventive concept, the embodiment of the present application also provides a device for implementing the above-mentioned aircraft engine part characteristic process parameter design and evaluation method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more embodiments of the aircraft engine part characteristic process parameter design and evaluation device provided below can refer to the above-mentioned limitations on the aircraft engine part characteristic process parameter design and evaluation method, and will not be repeated here.

[0122] In an exemplary embodiment, Fig.10 As shown, a device for designing and evaluating characteristic process parameters of aircraft engine parts is provided, comprising:

[0123] Database M1 is used to store historical actual processing cases. Fig.11 As shown, the front-end interface of the database is shown, which can realize the functions of adding, deleting, modifying and checking the database data.

[0124] The case library M2 is used to store rewritten cases; the historical actual processing cases and the rewritten cases include processing feature types, processing materials, machine tool information, tool information, working condition information, cutting parameters and processing results.

[0125] The similar case retrieval module M3 is used to retrieve similar cases from the historical actual processing cases stored in the database and the rewritten cases stored in the case library according to the processing feature type and processing material or processing feature type, processing material and processing target input by the user, and obtain similar cases; the processing feature type refers to the type of each component of the aircraft engine. Fig.12 As shown, the front-end interface of similar case retrieval is shown.

[0126] The similar case rewriting module M4 is used to rewrite the cutting parameters of the similar case to obtain a rewritten case. Fig.12 As shown, the front-end interface of the rewritten case is shown.

[0127] The optimization module M5 is used to optimize the cutting parameters according to the parameter optimization model and apply the particle swarm algorithm to obtain the cutting parameter optimization results; the parameter optimization model is determined according to the processing feature type and the processing material or the processing feature type, the processing material and the processing target; the parameter optimization model includes a surface processing quality model and a processing efficiency model; the expressions of the surface processing quality model and the processing efficiency model include cutting parameters.

[0128] The case evaluation module M6 is used to perform case evaluation on the rewritten case and the optimized case to obtain case scores of the rewritten case and the optimized case; the optimized case includes the cutting parameter optimization result, the processing feature type, the processing material and the processing result; when the user's input includes the processing target, the processing result is determined according to the processing target; when the user's input does not include the processing target, the processing result is determined according to the default processing target parameters in the parameter optimization model; the default processing target parameters in the parameter optimization model include tool wear weight, cutting force weight and surface roughness weight.

[0129] In an exemplary embodiment, a computer device is provided, which may be a server or a terminal. The computer device includes a processor, a memory, an input / output interface (I / O for short) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store rewriting cases and optimization cases and the evaluation scores corresponding to each case. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for designing and evaluating characteristic process parameters of aircraft engine parts is implemented.

[0130] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0131] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0132] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing related hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. The database involved in the embodiments provided in this application may include a relational database. The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0133] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for designing and evaluating characteristic process parameters of aircraft engine parts, characterized in that: The characteristic process parameter design and evaluation method of aerospace engine parts comprises: According to the processing feature type and processing material or the processing feature type, processing material and processing target input by the user, similar cases are retrieved from the historical actual processing cases stored in the database and the rewritten cases stored in the case library to obtain similar cases; the processing feature type refers to the type of each component part of the aircraft engine; the historical actual processing case and the rewritten case include the processing feature type, processing material, machine tool information, tool information, working condition information, cutting parameters and processing results; Rewriting the cutting parameters of the similar case to obtain a rewritten case; According to the parameter optimization model, the particle swarm algorithm is applied to optimize the cutting parameters to obtain the cutting parameter optimization results; the parameter optimization model is determined according to the processing feature type and the processing material or the processing feature type, the processing material and the processing target; the parameter optimization model includes a surface processing quality model and a processing efficiency model; the expressions of the surface processing quality model and the processing efficiency model include cutting parameters; A case evaluation is performed on the rewriting case and the optimization case, and case scores of the rewriting case and the optimization case are obtained to display the rewriting case and the optimization case and the corresponding case scores to the user; the optimization case includes the cutting parameter optimization result, the processing feature type, the processing material and the processing result; when the user's input includes a processing target, the processing result is determined according to the processing target; when the user's input does not include a processing target, the processing result is determined according to the default processing target parameters in the parameter optimization model; the default processing target parameters in the parameter optimization model include tool wear weight, cutting force weight and surface roughness weight.

2. The method for designing and evaluating characteristic process parameters of aircraft engine parts according to claim 1, characterized in that: According to the processing feature type and processing material input by the user, similar cases are retrieved from the historical actual processing cases stored in the database and the rewritten cases stored in the case library to obtain similar cases, including: According to the processing feature type input by the user, cases with the same processing feature type are screened out from the historical actual processing cases stored in the database and the rewritten cases stored in the case library to obtain a plurality of first screened cases; Screening out cases having the same or similar material mechanical properties as the processing material input by the user from the first screening cases, and obtaining a plurality of second screening cases; The cases having the same material mechanical properties or the similarity of the material mechanical properties being greater than a preset similarity value are screened out from the second screening cases to obtain the similar cases.

3. The method for designing and evaluating characteristic process parameters of aircraft engine parts according to claim 1, characterized in that: According to the processing feature type, processing material and processing target input by the user, similar cases are retrieved from the historical actual processing cases stored in the database and the rewritten cases stored in the case library to obtain similar cases, including: According to the processing feature type input by the user, cases with the same processing feature type are screened out from the historical actual processing cases stored in the database and the rewritten cases stored in the case library to obtain a plurality of third screened cases; From the third screening cases, cases having the same or similar material mechanical properties as the processing material input by the user are screened out to obtain a plurality of fourth screening cases; From the third screening cases, screening out cases that are the same as or similar to the processing target input by the user, to obtain a plurality of fifth screening cases; Calculating the global similarity between the processing material and the processing target in the fourth screening case; Calculating the global similarity between the processing material and the processing target in the fifth screening case; From the fourth screening cases and the fifth screening cases, cases whose global similarity is greater than a preset global similarity are screened out to obtain the similar cases.

4. The method for designing and evaluating characteristic process parameters of aircraft engine parts according to claim 1, characterized in that: The cutting parameters of the similar case are rewritten to obtain a rewritten case, which specifically includes: For each similar case, calculating the material similarity between the processed material in the similar case and the processed material input by the user; Determining the similarity of feed per tooth between the similar case and the rewritten case according to the material similarity; Determining the cutting speed similarity between the similar case and the rewritten case according to the thermal conductivity ratio between the processing material in the similar case and the processing material input by the user; the cutting speed similarity is equal to the cutting depth similarity between the similar case and the rewritten case; The cutting parameters in the similar case are rewritten according to the similarity of the feed per tooth, the similarity of the cutting speed and the similarity of the cutting depth to obtain the rewritten case; the cutting parameters include cutting speed, feed per tooth, axial cutting depth and radial cutting depth.

5. The method for designing and evaluating characteristic process parameters of aircraft engine parts according to claim 1, characterized in that: According to the parameter optimization model, the particle swarm algorithm is used to optimize the cutting parameters, and the cutting parameter optimization results are obtained, including: A parameter optimization model is determined according to the processing feature type and the processing material or the processing feature type, the processing material and the processing target; when the user input includes the processing feature type and the processing material, the material weight parameter in the parameter optimization model is determined according to the processing material input by the user, and the weight of the default processing target parameter in the parameter optimization model is selected from a preset weight value; when the user input also includes the processing target, the weight of the default processing target parameter in the parameter optimization model is determined according to the processing target input by the user. The particle fitness is calculated by using the parameter optimization model, and the particle swarm algorithm is applied to optimize the cutting parameters to obtain the cutting parameter optimization result; in the particle swarm algorithm, one particle is a numerical combination of a group of cutting parameters.

6. The method for designing and evaluating characteristic process parameters of aircraft engine parts according to claim 1, characterized in that: Perform case evaluation on the rewritten case and the optimized case to obtain case scores of the rewritten case and the optimized case, specifically including: A processing evaluation index matrix is ​​constructed according to the rewriting case and the optimization case and the corresponding processing targets; the elements in the processing evaluation index matrix are element pairs formed by any combination of each scheme to be evaluated and the evaluation index; the scheme to be evaluated refers to the rewriting case and the optimization case; the evaluation index is determined according to the processing target; Performing standardization processing on the processing evaluation index matrix to obtain a preprocessed processing evaluation index matrix; The grey correlation between the evaluation index of each scheme to be evaluated and the evaluation index of the relatively optimal processing scheme is calculated according to the processing evaluation index matrix and the evaluation index corresponding to the relatively optimal processing scheme; the grey correlation corresponding to each scheme to be evaluated constitutes the grey correlation matrix of each scheme to be evaluated; the relatively optimal processing scheme is predefined; Determine the weight value of each evaluation index corresponding to the scheme to be evaluated; The weighted correlation between each of the schemes to be evaluated and the relatively optimal scheme is calculated according to the weight value and the grey correlation matrix; the weighted correlation corresponding to each of the schemes to be evaluated is the case score.

7. The method for designing and evaluating characteristic process parameters of aircraft engine parts according to claim 1, characterized in that: After executing the step of "performing case evaluation on the rewriting case and the optimization case to obtain case scores of the rewriting case and the optimization case", the aircraft engine part characteristic process parameter design evaluation method further includes: Sort the case scores from largest to smallest; Select multiple cases with higher case scores and store them in the case library.

8. An aircraft engine part characteristic process parameter design and evaluation device, characterized in that: The aircraft engine part characteristic process parameter design and evaluation device comprises: Database, used to store historical actual processing cases; A case library for storing rewritten cases; the historical actual processing cases and the rewritten cases include processing feature types, processing materials, machine tool information, tool information, working condition information, cutting parameters and processing results; A similar case retrieval module is used to retrieve similar cases from historical actual processing cases stored in the database and rewritten cases stored in the case library according to the processing feature type and processing material or the processing feature type, processing material and processing target input by the user, so as to obtain similar cases; the processing feature type refers to the type of each component of the aircraft engine; A similar case rewriting module, used for rewriting cutting parameters of the similar case to obtain a rewritten case; An optimization module is used to optimize cutting parameters by applying a particle swarm algorithm according to a parameter optimization model to obtain a cutting parameter optimization result; the parameter optimization model is determined according to the processing feature type and the processing material or the processing feature type, the processing material and the processing target; the parameter optimization model includes a surface processing quality model and a processing efficiency model; the expressions of the surface processing quality model and the processing efficiency model include cutting parameters; A case evaluation module is used to perform case evaluation on the rewriting case and the optimization case, and obtain the case scores of the rewriting case and the optimization case to display the rewriting case and the optimization case and the corresponding case scores to the user; the optimization case includes the cutting parameter optimization result, the processing feature type and the processing material and the processing result; when the user's input includes the processing target, the processing result is determined according to the processing target; when the user's input does not include the processing target, the processing result is determined according to the default processing target parameters in the parameter optimization model; the default processing target parameters in the parameter optimization model include tool wear weight, cutting force weight and surface roughness weight.

9. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for designing and evaluating characteristic process parameters of aircraft engine parts according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for designing and evaluating characteristic process parameters of aircraft engine parts according to any one of claims 1 to 7 is implemented.

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