Method and system for optimizing manufacturing process of wind turbine gear

Abstract

The application provides a manufacturing process optimization method and system for wind turbine gears, and relates to the technical field of data processing. A plurality of gears with the same performance and a plurality of gear machining raw materials are obtained by synchronizing K sets of gear performance information to a performance deviation analysis module. A plurality of gear specification information is obtained by dividing the K sets of gear parameter information into groups according to the plurality of gears with the same performance. A plurality of plate milling strategies are obtained by optimizing milling according to the plurality of gear specification information and a plurality of standard material specification information to perform batch milling manufacturing of K functional gears. The technical problem of causing waste of machining raw materials and high production cost is solved by the prior art, which usually adopts the batch machining mode of the same design parameter gears on the blank to process gears. The technical effect of reducing the waste of machining raw materials and reducing the production cost is achieved based on the blank multi-specification parameter gear adaptive machining of the wind turbine box gear composition.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a manufacturing process optimization method and system for wind turbine gears. BACKGROUND

[0002] Under the traditional processing method, gear manufacturing enterprises usually purchase a large number of gear blanks of the same specification, and then cut them into the required size and shape during the processing. Such a method improves production efficiency to a certain extent, but there is obvious waste of raw materials, because a large number of blanks may not be fully utilized after processing is completed, thereby causing waste of resources and unnecessary additional costs, which not only affects the sustainable economy of the enterprise, but also has a negative impact on the environment.

[0003] In summary, the prior art usually adopts a batch processing method of the same design parameter gear on the blank for wind turbine gear processing, which causes waste of processing raw materials and results in high production cost. SUMMARY

[0004] The present application provides a manufacturing process optimization method and system for wind turbine gears, which is used to solve the technical problem of the prior art that the same design parameter gear is usually processed on the blank in a batch processing method for wind turbine gears, which causes waste of processing raw materials and results in high production cost.

[0005] In view of the above problems, the present application provides a manufacturing process optimization method and system for wind turbine gears.

[0006] In a first aspect, the present application provides a manufacturing process optimization method for wind turbine gears, the method comprising: interactively obtaining target design information of a wind turbine gearbox, wherein the target design information comprises K sets of gear parameter information and K sets of gear performance information, the K sets of gear parameter information and the K sets of gear performance information are based on K functional gear correlation mapping, K is a positive integer; pre-constructing a performance deviation analysis module; synchronizing the K sets of gear performance information to the performance deviation analysis module for performance deviation analysis, to divide the K functional gears into multiple groups of gears with the same performance, and obtain multiple groups of gears with the same performance; interactively obtaining multiple standard material specification information of the multiple gear processing raw materials; dividing the K sets of gear parameter information into groups according to the multiple groups of gears with the same performance, and obtaining multiple gear specification information; optimizing milling according to the multiple gear specification information and the multiple standard material specification information, and obtaining multiple plate milling strategies; and performing batch milling manufacturing of the K functional gears using the multiple plate milling strategies.

[0007] In a second aspect of the present application, a manufacturing process optimization system for wind turbine gears is provided, which comprises: a design information interaction unit configured to interactively obtain target design information of a wind turbine gear box, wherein the target design information comprises K sets of gear parameter information and K sets of gear performance information, the K sets of gear parameter information and the K sets of gear performance information are based on K functional gear correlation mappings, K is a positive integer; an analysis module construction unit configured to pre-construct a performance deviation analysis module; a performance deviation analysis unit configured to synchronize the K sets of gear performance information to the performance deviation analysis module for performance deviation analysis, to divide the K functional gears into groups according to processing raw materials, and to obtain multiple groups of gears with the same performance; a processing raw material calling unit configured to call processing raw materials according to the multiple groups of gears with the same performance, and to obtain multiple types of gear processing raw materials; a specification information calling unit configured to interactively obtain multiple types of standard material specification information of the multiple types of gear processing raw materials; a gear specification calling unit configured to divide the K sets of gear parameter information into groups according to the multiple groups of gears with the same performance, and to obtain multiple groups of gear specification information; a milling strategy optimization unit configured to optimize milling according to the multiple groups of gear specification information and the multiple types of standard material specification information, and to obtain multiple groups of plate milling strategies; and a batch milling execution unit configured to perform batch milling process of the K functional gears by using the multiple groups of plate milling strategies.

[0008] The one or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0009] The method provided by the embodiments of the present application interactively obtains target design information of a wind turbine gear box, wherein the target design information comprises K sets of gear parameter information and K sets of gear performance information, the K sets of gear parameter information and the K sets of gear performance information are based on K functional gear correlation mappings, K is a positive integer; a performance deviation analysis module is pre-constructed; the K sets of gear performance information are synchronized to the performance deviation analysis module for performance deviation analysis, to divide the K functional gears into groups according to processing raw materials, and to obtain multiple groups of gears with the same performance; processing raw materials are called according to the multiple groups of gears with the same performance, and multiple types of gear processing raw materials are obtained; multiple types of standard material specification information of the multiple types of gear processing raw materials are interactively obtained; the K sets of gear parameter information are divided into groups according to the multiple groups of gears with the same performance, and multiple groups of gear specification information are obtained; optimization milling is performed according to the multiple groups of gear specification information and the multiple types of standard material specification information, and multiple groups of plate milling strategies are obtained; and the K functional gears are manufactured by batch milling by using the multiple groups of plate milling strategies. The technical effect of adapting multiple-specification parameters of a blank to gears based on the composition of a wind turbine gear box is achieved, the waste of processing raw materials is reduced, and the production cost is reduced. BRIEF DESCRIPTION OF DRAWINGS

[0010] Figure 1A flowchart of a manufacturing process optimization method of a wind turbine gear provided in the present application is provided.

[0011] Figure 2 A flowchart of obtaining gear specification information in the manufacturing process optimization method of the wind turbine gear provided in the present application is provided.

[0012] Figure 3 A structural diagram of the manufacturing process optimization system of the wind turbine gear provided in the present application is provided.

[0013] The reference signs are explained as follows: a design information interaction unit 1, an analysis module construction unit 2, a performance deviation analysis unit 3, a processing raw material calling unit 4, a specification information calling unit 5, a gear specification calling unit 6, a milling strategy optimization unit 7, and a batch milling execution unit 8. DETAILED DESCRIPTION

[0014] The present application provides a manufacturing process optimization method and system of a wind turbine gear, which is used to solve the technical problem that the same design parameter gear is usually processed in batches on a blank in the prior art, which causes waste of processing raw materials and high production cost. The technical effect of reducing the waste of processing raw materials and reducing the production cost is achieved by adapting the multi-specification parameter gear processing of the blank based on the wind turbine box gear composition.

[0015] In the technical scheme of the present application, the acquisition, storage, use, processing, etc. of data all comply with relevant regulations.

[0016] Hereinafter, the technical scheme in the present application will be described clearly and completely with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. It should be understood that the present application is not limited by the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application. In addition, it should be noted that, for convenience of description, only the parts related to the present application are shown in the drawings, rather than all the parts. EMBODIMENT

[0017] As shown in the drawings, the present application provides a manufacturing process optimization method of a wind turbine gear, which comprises: Figure 1 A100: obtaining target design information of a wind turbine gear box, wherein the target design information comprises K sets of gear parameter information and K sets of gear performance information, the K sets of gear parameter information and the K sets of gear performance information are based on K functional gear correlation mappings, K is a positive integer;

[0018]

[0019] ​Specifically, in this embodiment, the wind turbine gearbox is a variety of wind turbine gearboxes produced by the current manufacturer, and based on this, there are various types of gears in the various types of wind turbine gearboxes.

[0020] Based on this, this embodiment interactively obtains design parameter information of various models of wind turbine gearboxes currently in production, and then breaks down the obtained design parameters according to the individual gear design parameters to obtain the target design information. The target design information includes K sets of gear parameter information and K sets of gear performance information for K functional gears. The gear parameter information includes, but is not limited to, module, number of teeth, and pitch circle diameter. The gear performance information includes i performance parameters corresponding to i performance indicators. For example, the gear performance indicators include performance indicators such as density, cutting performance, thermal stability, fatigue life, and strength.

[0021] The gear performance information can be used as a screening constraint to select gear blanks with suitable performance information and thickness specifications from the existing gear blanks, and then combine the gear parameter information to process the corresponding functional gears.

[0022] A200: Pre-built performance deviation analysis module;

[0023] In one embodiment, the performance deviation analysis module is pre-built, and the method step A200 provided in this application further includes:

[0024] A210: The performance deviation analysis module includes a performance similarity analyzer and a performance deviation inductive analyzer;

[0025] A220: Pre-constructed similarity analysis calculation formula, the similarity analysis calculation formula is as follows:

[0026] ;

[0027] in, This is a similarity index between the two sets of gear performance information. The first standard gear performance information Performance parameter values, For similar gear performance information, the first Performance parameter values, The total number of gear performance indicators is consistent with the standard gear performance information and similar gear performance information.

[0028] A230: Construct the performance similarity analyzer based on the KNN model, and synchronize the similarity analysis calculation formula to the performance similarity analyzer; A240: Preset the performance deviation summarization threshold, and synchronize the performance deviation summarization threshold to the performance deviation summarizer.

[0029] Specifically, in the embodiment, the performance deviation analysis module is configured to quickly and accurately calculate the similarity of the two functional gears under the condition that the gear performance information of the two functional gears is obtained, so as to determine whether the two functional gears can be machined by using the same gear blank.

[0030] The performance deviation analysis module comprises a performance similarity analyzer and a performance deviation inducer. The performance similarity analyzer is configured to calculate the similarity of the two functional gears. The performance deviation inducer is a data comparison engine configured to compare data according to predefined parameters. In the embodiment, the performance deviation inducer is set as the comparison benchmark of the data comparison engine (performance deviation inducer) according to the predefined parameter of the performance deviation induction threshold, so that the performance deviation inducer can be used to determine whether the two functional gears can be machined by using the same gear blank according to the calculation result of the performance similarity analyzer.

[0031] A pre-constructed similarity analysis calculation formula is as follows:

[0032]

[0033] wherein, is a similarity index of the two groups of gear performance information, is a value of the i th performance parameter of the standard gear performance information, is a value of the i th performance parameter of the similar gear performance information, is a value of the i th performance parameter of the similar gear performance information, is a total number of gear performance indexes, and the gear performance indexes of the standard gear performance information and the similar gear performance information are consistent; The performance similarity analyzer is constructed based on the KNN model, and the similarity analysis calculation formula is synchronized to the performance similarity analyzer.

[0034] A performance deviation induction threshold is preset. When the performance similarity indexes of two or more functional gears are in the same performance deviation induction threshold, it is considered that the two or more functional gears can be machined by using the same specification of gear blank. In the embodiment, the performance deviation induction threshold is synchronized to the performance deviation inducer.

[0035] The performance deviation analysis module provides an effective processing means for scientifically judging the performance design similarity of functional gears and grouping functional gears based on performance design.

[0036] A300: The K groups of gear performance information are synchronized to the performance deviation analysis module for performance deviation analysis, so as to divide the K functional gears into machining raw materials and obtain multiple groups of gears with the same performance.

[0037] ​​

[0038] In an embodiment, the K sets of gear performance information are synchronized to the performance deviation analysis module for performance deviation analysis, to divide the K functional gears into groups according to raw materials, to obtain groups of gears with the same performance, and the method step A300 provided in the present application further comprises:

[0039] A310: randomly selecting based on the K sets of gear performance information to obtain the standard gear performance information;

[0040] A320: constructing K-1 sets of performance deviation data groups based on the K-1 sets of gear performance information and the standard gear performance information, and synchronizing each set of performance deviation data group to the performance similarity analyzer of the performance deviation analysis module for performance deviation analysis to obtain K-1 performance deviation indexes;

[0041] A330: defining the standard performance deviation index of the standard gear performance information as 1, and synchronizing the standard performance deviation index and the K-1 performance deviation indexes to the performance deviation inductor for serialization processing to obtain a performance deviation sequence;

[0042] A340: calling the performance deviation induction threshold to perform data classification on the performance deviation sequence to obtain the groups of gears with the same performance;

[0043] A350: performing induction processing on the K sets of gear performance information according to the groups of gears with the same performance to obtain groups of performance parameter information with the same performance.

[0044] Specifically, in the present embodiment, a set of gear performance information is randomly selected from the K sets of gear performance information as the standard gear performance information, and the similarity between the remaining K-1 sets of gear performance information and the standard gear performance information is judged to classify the K functional gears.

[0045] Specifically, the present embodiment constructs K-1 sets of performance deviation data groups based on the K-1 sets of gear performance information and the standard gear performance information, and each set of performance deviation data group includes the standard gear performance information and a set of gear performance information.

[0046] The K-1 sets of performance deviation data groups are synchronized one by one to the performance similarity analyzer of the performance deviation analysis module for performance deviation analysis to obtain K-1 performance deviation indexes.

[0047] The standard performance deviation index of the standard gear performance information is defined as 1, and the standard performance deviation index and the K-1 performance deviation indexes are synchronized to the performance deviation inductor, and first, serialization processing is completed according to the performance deviation index from large to small, to obtain a performance deviation sequence, in the performance deviation sequence, the closer the performance deviation index to 1, the closer the performance deviation data group corresponding to the functional gear to the performance requirement of the functional gear corresponding to the standard gear performance information to the gear blank.

[0048] The performance deviation inductive threshold is taken as a hierarchical threshold to obtain multi-level performance deviations, and the interval distance between adjacent two levels of performance deviations is the performance deviation inductive threshold.

[0049] Based on the obtained multi-level performance deviations, intuitive data classification of the performance deviation sequence is performed to obtain the multiple groups of same performance gears, and it should be understood that the gear performance information of several functional gears in each group of same performance gears is approximate, and the same performance information of the gear blank can be used for gear processing, that is, the processing of the several functional gears is performed on the same gear blank.

[0050] The embodiment performs mapping inductive processing of the K groups of gear performance information according to the multiple groups of same performance gears to obtain multiple groups of same performance parameter information. The embodiment classifies functional gears based on the similarity of gear performance information, and calls multiple groups of gear performance information of each group of performance similar functional gears, to provide a high availability reference for subsequent gear blank selection calling.

[0051] A400: processing raw material calling is performed according to the multiple groups of same performance gears to obtain multiple gear processing raw materials;

[0052] In one embodiment, processing raw material calling is performed according to the multiple groups of same performance gears to obtain multiple gear processing raw materials, and the method step A400 provided in the application further includes:

[0053] A410: performance index calling is performed based on the standard gear performance information to obtain a gear performance index;

[0054] A420: first group of same performance parameter information is called from the multiple groups of same performance parameter information;

[0055] A430: data splitting and reorganization of the first group of same performance parameter information is performed based on the gear performance index to obtain a group of performance parameters;

[0056] A440: performance extreme value calling is performed on the group of performance parameters to obtain a performance extreme value;

[0057] A450: a pre-constructed processing material information library, wherein a plurality of sample processing material-sample performance index set-sample material specification are stored in the processing material information library;

[0058] A460: traversing the processing material information library with the performance extreme value as a screening constraint to obtain a first gear processing material, wherein each performance index in the sample performance index set of the first gear processing material is better than the performance extreme value;

[0059] A470: by analogy, the plurality of gear processing materials are obtained.

[0060] Specifically, in this embodiment, performance index calling is performed based on the standard gear performance information, and a plurality of performance indexes constituting the standard gear performance information are obtained, i.e., a total of G gear performance indexes.

[0061] A first group of same performance parameter information is obtained from the plurality of groups of same performance parameter information, and the first group of same performance parameter information specifically includes G groups of same performance parameter information of G functional gears.

[0062] Based on the G gear performance indexes, data splitting and reorganization are performed on the first group of same performance parameter information to obtain G groups of performance parameters, each group of performance parameters has consistent performance parameter indexes, and each group of performance parameters is G. Each of the G groups of performance parameters is subjected to sequence processing from large to small to call a performance extreme value (maximum value) to obtain G performance extreme values.

[0063] The processing material is also a gear blank, each sample processing material has a corresponding sample performance index set including i performance parameters, and a sample material specification representing the size of the unused gear blank steel plate.

[0064] A plurality of sample processing material-sample performance index set-sample material specifications are obtained, and the obtained data is stored in a pre-constructed processing material information library for storage management.

[0065] Traversing the processing material information library with the G performance extreme values as a screening constraint to obtain a first gear processing material, each performance index in the sample performance index set of the first gear processing material is only slightly worse than the G performance extreme values, and by analogy, the plurality of gear processing materials are obtained.

[0066] ​​​​​​​​The embodiment realizes the technical effects of obtaining a plurality of gear machining raw materials for producing a plurality of groups of gears with the same performance, reducing the number of gear machining raw materials required for gear machining, reducing the production and machining complexity, and reducing the dependence of gear blank selection on manual experience.

[0067] A500: interactively obtaining a plurality of standard material specification information of the plurality of gear machining raw materials;

[0068] Specifically, in the embodiment, the gear machining raw material is also a gear blank, and the standard material specification information is also a complete regular quadrilateral gear blank length and width parameter. In order to facilitate mass production, the gear blank often has standardized length and width parameters. Therefore, the embodiment directly interacts with the gear blank manufacturer or the Internet to obtain a plurality of sample material specifications of a plurality of sample machining raw materials, and then obtains the plurality of standard material specification information through the mapping call of the machining raw material information library after obtaining the plurality of gear machining raw materials.

[0069] A600: According to the plurality of groups of gears with the same performance, the K groups of gear parameter information are divided into groups to obtain a plurality of gear specification information;

[0070] In one embodiment, as shown in Figure 2 According to the plurality of groups of gears with the same performance, the K groups of gear parameter information are divided into groups to obtain a plurality of gear specification information, and the method steps A600 provided by the application further include:

[0071] A610: Based on the K groups of gear parameter information, a production log is called to obtain a K group of historical milling allowance constraint set, wherein each group of historical milling constraint has a historical milling efficiency and a historical milling scrap rate identifier;

[0072] A620: Pre-set milling weight configuration, wherein the milling weight configuration includes milling efficiency weight and scrap rate weight;

[0073] A630: Based on the milling weight configuration, the K group of historical milling allowance constraint set is weighted calculated to obtain a K group of historical milling coefficient set;

[0074] A640: Serializing the K group of historical milling coefficient set and calling the extreme value to obtain K milling allowance extreme value;

[0075] A650: Using the K milling allowance extreme value to map and replace the plurality of groups of gear specification information to obtain a plurality of groups of gear allowance parameters.

[0076] Specifically, in the embodiment, the milling allowance is the diameter parameter of the gear disc steel plate with the most basic initial shape when the gear is cut out from the gear blank at the beginning of the functional gear processing, and the milling allowance is larger than the outer diameter of the actual processed functional gear.

[0077] It should be understood that the larger the milling allowance is, the higher the fault tolerance of the gear processing process is, and the gear processing efficiency and the processing cost also increase.

[0078] Based on this, the embodiment obtains K sets of historical milling allowance constraint sets according to the K sets of gear parameter information for the same design parameter gear production log calling, each set of historical milling constraint includes a plurality of gear historical milling allowances, and it should be understood that the milling allowance in the embodiment is the diameter parameter of the gear disc steel plate with the most basic initial shape when the gear is cut out from the gear blank at the beginning of the functional gear processing, and each gear historical milling allowance has a historical milling efficiency and a historical milling scrap rate identifier, and the milling scrap rate is the production control condition of the gear under different milling allowances.

[0079] A preset milling weight configuration is configured, and the milling weight configuration includes a milling efficiency weight and a scrap rate weight. The embodiment does not limit the specific value of the weight, and the value can be set based on the demand side of the user's production finished product and production efficiency in actual application.

[0080] Based on the milling weight configuration, the K sets of historical milling allowance constraint sets are weighted and calculated to obtain K sets of historical milling coefficient sets, wherein each set of historical milling coefficient set includes a plurality of historical milling coefficients, and the plurality of historical milling coefficients correspond to the plurality of gear historical milling allowances evaluated in the foregoing, the K sets of historical milling coefficient sets are serialized and extreme value calling is performed to obtain K milling allowance extreme values, and the milling allowance extreme value is the optimal solution of the gear milling diameter balancing the gear processing scrap rate and the production speed.

[0081] The K milling allowance extreme values are used to map and replace the plurality of gear specification information to obtain a plurality of gear allowance parameters, and each gear allowance parameter is a plurality of gear disc diameter information of a plurality of functional gears obtained by milling a circular disc-shaped gear disc using the same gear blank.

[0082] The embodiment realizes the plurality of gear allowances balancing the production efficiency and the production control by performing the gear disc allowance analysis, and provides a reference for the cutting strategy generation of the subsequent gear blank cutting process step.

[0083] A700: According to the plurality of gear specification information and the plurality of standard material specification information, an optimized milling is performed to obtain a plurality of plate milling strategies.

[0084] In one embodiment, the optimization milling is performed according to the plurality of gear specification information and the plurality of standard material specification information, and a plurality of plate milling strategies are obtained. The method step A700 provided in the application further includes:

[0085] A710: a first set of gear allowance parameters and a first standard material specification information are called from the plurality of gear allowance parameters and the plurality of standard material specification information, wherein the first set of gear allowance parameters is mapped to M functional gears, and M is a positive integer less than K;

[0086] A720: based on the first set of gear allowance parameters and the first standard material specification, a two-dimensional modeling is performed to obtain M first gear models and a first standard blank model;

[0087] A730: M gear processing benefits of the M functional gears are obtained interactively;

[0088] A740: the M first gear models are taken as optimization benchmarks, the M gear processing benefits are taken as milling strategy evaluation benchmarks, and optimization milling is performed on the first standard blank model to obtain a first plate milling strategy;

[0089] A750: similarly, the plurality of plate milling strategies are obtained.

[0090] In one embodiment, the M first gear models are taken as optimization benchmarks, the M gear processing benefits are taken as milling strategy evaluation benchmarks, and optimization milling is performed on the first standard blank model to obtain a first plate milling strategy. The method step A740 provided in the application further includes:

[0091] A741: the M first gear models are sequenced according to the M gear processing benefits to obtain a first gear sequence;

[0092] A742: a first benefit gear is called based on the first gear sequence;

[0093] A743: drop fitting filling of the M first gear models and the first benefit gear is performed with the first standard blank model as a constraint to obtain a first filling result;

[0094] A744: a second benefit gear is called based on the first gear sequence;

[0095] A745: the second benefit gear is used to traverse the first filling result to obtain a second filling result;

[0096] A746: similarly, until the Mth benefit gear in the first gear sequence is used to traverse the M-1th filling result to obtain the first plate milling strategy, wherein the first plate milling strategy includes M sets of milling center coordinates.

[0097] Specifically, in the embodiment, since the plate milling strategy method of obtaining the gear blank of any one of the plurality of groups of gears with the same performance has consistency, the embodiment calls a first group of gear allowance parameters and a first standard material specification information from the plurality of groups of gear allowance parameters and the plurality of standard material specification information mapping.

[0098] Taking the first plate milling strategy determined based on the first group of gear allowance parameters and the first standard material specification information as an example, the detailed parameters of the plate milling strategy generation method are described.

[0099] Based on the first group of gear allowance parameters and the first standard material specification, two-dimensional modeling is performed to obtain M first gear models with different gear disc diameters and a first standard blank model.

[0100] The M gear processing benefits of the M functional gears are obtained, and the gear processing benefit is the selling unit price of a single gear.

[0101] The M first gear models are serialized according to the M gear processing benefits to obtain a first gear sequence, and a first benefit gear is obtained based on the first gear sequence, which is the functional gear with the maximum economic value.

[0102] In the constraint size interval of the first standard blank model, the M first gear models are first filled in, and the M first gear models are in unspecified positions in the first standard blank model. Based on software simulation, the first standard blank model space is vertically arranged, and the M first gear models naturally fall to the bottom of the first standard blank model. Then, a gear model corresponding to a first benefit gear in an unspecified number is filled into the first standard blank model. Through falling simulation, the displacement of each gear model in the first standard blank model is continuously compressed until the gear model of the first benefit gear cannot be put in. A first filling result is obtained.

[0103] Using the same falling fitting method, a second benefit gear is obtained based on the first gear sequence. The second benefit gear is used to traverse the first filling result to perform second benefit gear filling of the required remaining plate area, and a second filling result is obtained. In this way, the first plate milling strategy is obtained by using the Mth benefit gear in the first gear sequence to traverse the M-1th filling result. The first plate milling strategy includes M groups of milling center coordinates, and each group of milling center coordinates is a plurality of gear disc center coordinates for a functional gear during gear disc milling. In this way, the plurality of plate milling strategies are obtained.

[0104] The embodiment adopts drop fitting to generate a gear blank surface maximum economic benefit gear disc milling scheme, and achieves the technical effect of improving the utilization rate of the milling scheme for the gear blank.

[0105] A800: adopt the multi-group plate milling strategy to perform batch milling process of the K functional gears.

[0106] Specifically, in the embodiment, after the gear blank is positioned and clamped, the corresponding gear disc milling center positioning is performed using the corresponding plate milling strategy in the multi-group plate milling strategy, the corresponding gear disc milling allowance is used for milling processing on the gear blank, and the gear discs obtained based on the K functional gears are classified for subsequent batch processing in the fine processing process.

[0107] The embodiment achieves the technical effect of reducing the waste of raw materials and reducing production costs based on the multi-specification parameter gear adaptive processing of the gear blanks constituted by the wind turbine gearbox.

[0108] Embodiment two

[0109] Based on the same inventive concept as the manufacturing process optimization method of the wind turbine gear in the foregoing embodiment, as shown in Figure 3 The application provides a manufacturing process optimization system for wind turbine gears, wherein the system comprises:

[0110] A design information interaction unit 1 is configured to interactively obtain target design information of a wind turbine gear box, wherein the target design information comprises K sets of gear parameter information and K sets of gear performance information, and the K sets of gear parameter information and the K sets of gear performance information are associated and mapped based on K functional gears, and K is a positive integer.

[0111] An analysis module construction unit 2 is configured to pre-construct a performance deviation analysis module.

[0112] A performance deviation analysis unit 3 is configured to synchronize the K sets of gear performance information to the performance deviation analysis module for performance deviation analysis, to divide the K functional gears into processing raw materials, and to obtain multiple groups of gears with the same performance.

[0113] A processing raw material calling unit 4 is configured to call processing raw materials according to the multiple groups of gears with the same performance, to obtain multiple types of gear processing raw materials.

[0114] A specification information calling unit 5 is configured to interactively obtain multiple standard material specification information of the multiple types of gear processing raw materials.

[0115] A gear specification calling unit 6 is configured to divide the K sets of gear parameter information into groups according to the multiple groups of gears with the same performance, to obtain multiple groups of gear specification information.

[0116] The milling strategy optimization unit 7 is used to perform milling optimization based on the multiple sets of gear specification information and the multiple sets of standard material specification information to obtain multiple sets of plate milling strategies.

[0117] Batch milling execution unit 8 is used to perform batch milling process processing of the K functional gears using the multi-group plate milling strategy.

[0118] In one embodiment, the analysis module construction unit 2 further includes:

[0119] The performance deviation analysis module includes a performance similarity analyzer and a performance deviation inductor;

[0120] A pre-constructed similarity analysis calculation formula is provided, as follows:

[0121] ;

[0122] in, This is a similarity index between the two sets of gear performance information. The first standard gear performance information Performance parameter values, For similar gear performance information, the first Performance parameter values, The total number of gear performance indicators is consistent with the standard gear performance information and similar gear performance information.

[0123] The performance similarity analyzer is constructed based on the KNN model, and the similarity analysis calculation formula is synchronized to the performance similarity analyzer.

[0124] A preset performance deviation summarization threshold is established, and the performance deviation summarization threshold is synchronized to the performance deviation summarizer.

[0125] In one embodiment, the performance deviation analysis unit 3 further includes:

[0126] Based on the performance information of the K groups of gears, the standard gear performance information is obtained by random selection.

[0127] Based on the performance information of K-1 gears and the performance information of the standard gears, K-1 sets of performance deviation data are constructed and synchronized one by one to the performance similarity analyzer of the performance deviation analysis module for performance deviation analysis to obtain K-1 performance deviation indices.

[0128] The standard performance deviation index of the standard gear performance information is defined as 1, and the standard performance deviation index and the K-1 performance deviation indices are synchronized to the performance deviation inductor for serialization processing to obtain the performance deviation sequence.

[0129] The performance deviation induction threshold is called to perform data classification on the performance deviation sequence, and a plurality of groups of gears with the same performance are obtained.

[0130] According to the plurality of groups of gears with the same performance, induction processing of the K groups of gear performance information is performed to obtain a plurality of groups of same performance parameter information.

[0131] In one embodiment, the processing raw material calling unit 4 further comprises:

[0132] Based on the standard gear performance information, a performance index is called to obtain an item gear performance index;

[0133] From the plurality of groups of same performance parameter information, a first group of same performance parameter information is called.

[0134] Based on the item gear performance index, data splitting and reorganization are performed on the first group of same performance parameter information to obtain a group of performance parameters;

[0135] Performance extreme values are called for the group of performance parameters to obtain a performance extreme value;

[0136] A pre-constructed processing raw material information library is constructed, and the processing raw material information library stores a plurality of groups of sample processing raw material-sample performance index set-sample material specifications.

[0137] The performance extreme value is used as a screening constraint to traverse the processing raw material information library to obtain a first gear processing raw material, wherein each performance index in the sample performance index set of the first gear processing raw material is better than the performance extreme value;

[0138] In this way, the plurality of gear processing raw materials are obtained.

[0139] In one embodiment, the gear specification calling unit 5 further comprises:

[0140] Based on the K groups of gear parameter information, production logs are called to obtain K groups of historical milling allowance constraint sets, wherein each group of historical milling constraints has historical milling efficiency and historical milling scrap rate identifiers;

[0141] A preset milling weight configuration is configured, wherein the milling weight configuration includes a milling efficiency weight and a scrap rate weight;

[0142] Based on the milling weight configuration, weighted calculation is performed on the K groups of historical milling allowance constraint sets to obtain K groups of historical milling coefficient sets;

[0143] serializing the K sets of historical milling coefficient sets and performing extreme value calling to obtain K milling allowance extreme values;

[0144] mapping and replacing the multiple sets of gear specification information using the K milling allowance extreme values to obtain multiple sets of gear allowance parameters.

[0145] In one embodiment, the milling strategy optimization unit 7 further comprises:

[0146] mapping and calling a first set of gear allowance parameters and a first standard material specification information from the multiple sets of gear allowance parameters and the multiple types of standard material specification information, wherein the first set of gear allowance parameters is mapped to M functional gears, and M is a positive integer less than K;

[0147] performing two-dimensional modeling based on the first set of gear allowance parameters and the first standard material specification to obtain M first gear models and a first standard blank model;

[0148] interactively obtaining M gear machining benefits of the M functional gears;

[0149] taking the M first gear models as optimization benchmarks and taking the M gear machining benefits as milling strategy evaluation benchmarks, and performing optimization milling on the first standard blank model to obtain a first plate milling strategy;

[0150] By analogy, the multiple sets of plate milling strategies are obtained.

[0151] In one embodiment, the milling strategy optimization unit 7 further comprises:

[0152] serializing the M first gear models according to the M gear machining benefits to obtain a first gear sequence;

[0153] obtaining a first benefit gear based on the first gear sequence calling;

[0154] performing drop fitting filling of the M first gear models and the first benefit gear with the first standard blank model as a constraint to obtain a first filling result;

[0155] obtaining a second benefit gear based on the first gear sequence calling;

[0156] traversing the first filling result using the second benefit gear to obtain a second filling result;

[0157] By analogy, until the Mth benefit gear of the first gear sequence traverses the M-1th filling result to obtain the first plate milling strategy, wherein the first plate milling strategy comprises M sets of milling center coordinates.

[0158] Any of the above-described methods or steps can be stored as computer instructions or programs in various types of computer memories, recognized by various types of computer processors, and implemented accordingly.

[0159] Based on the above specific embodiments of the present application, any improvements and modifications made by those skilled in the art to the present application without departing from the principles of the present application shall fall within the scope of the patent protection of the present application.

Claims

1. A method for optimizing the manufacturing process of a wind turbine gear, characterized in that, The method comprises: interactively obtaining target design information of a wind turbine gearbox, wherein the target design information comprises K sets of gear parameter information and K sets of gear performance information, the K sets of gear parameter information and the K sets of gear performance information are based on K functional gear correlation mappings, K is a positive integer; pre-building a performance deviation analysis module; synchronizing the K sets of gear performance information to the performance deviation analysis module for performance deviation analysis, to divide the K functional gears into processing raw materials, and to obtain multiple sets of gears with the same performance; calling processing raw materials according to the multiple sets of gears with the same performance, to obtain multiple kinds of gear processing raw materials; interactively obtaining multiple standard material specification information of the multiple kinds of gear processing raw materials; grouping the K sets of gear parameter information according to the multiple sets of gears with the same performance, to obtain multiple sets of gear specification information; optimizing milling according to the multiple sets of gear specification information and the multiple standard material specification information, to obtain multiple sets of plate milling strategies; adopting the multiple sets of plate milling strategies for batch milling process of the K functional gears.

2. The method of claim 1, wherein, The method further comprises: the performance deviation analysis module comprises a performance similarity analyzer and a performance deviation inducer; a similarity analysis calculation formula is pre-built, and the similarity analysis calculation formula is as follows: ; wherein, is a similarity index of the two sets of gear performance information, is a first performance parameter value of the standard gear performance information, is a first performance parameter value of the similar gear performance information, is a total number of gear performance indicators, the gear performance indicators of the standard gear performance information and the similar gear performance information are consistent. the performance similarity analyzer is built based on a KNN model, and the similarity analysis calculation formula is synchronized to the performance similarity analyzer; a performance deviation induction threshold is pre-set, and the performance deviation induction threshold is synchronized to the performance deviation inducer.

3. The method of claim 2, wherein, synchronizing the K sets of gear performance information to the performance deviation analysis module for performance deviation analysis, to divide the K functional gears into processing raw materials, and to obtain multiple sets of gears with the same performance, the method further comprises: randomly selecting based on the K sets of gear performance information, to obtain the standard gear performance information; building K-1 sets of performance deviation data groups based on K-1 sets of gear performance information and the standard gear performance information, and synchronizing them one by one to the performance similarity analyzer of the performance deviation analysis module for performance deviation analysis, to obtain K-1 performance deviation indexes; defining the standard performance deviation index of the standard gear performance information as 1, and synchronizing the standard performance deviation index and the K-1 performance deviation indexes to the performance deviation inducer for serialization processing, to obtain a performance deviation sequence; calling the performance deviation induction threshold for data classification of the performance deviation sequence, to obtain the multiple sets of gears with the same performance; performing induction processing of the K sets of gear performance information according to the multiple sets of gears with the same performance, to obtain multiple sets of gear performance parameter information with the same performance.

4. The method of claim 3, wherein, calling processing raw materials according to the multiple sets of gears with the same performance, to obtain multiple kinds of gear processing raw materials, the method further comprises: based on the standard gear performance information, a performance index call is made to obtain a performance index for the gear calling a first set of gear performance parameter information from the multiple sets of gear performance parameter information with the same performance; based on the The first group of performance parameter information is data split and reorganized by the gear performance index to obtain group performance parameters; The performance extreme value call is performed on the group performance parameter to obtain a performance extreme value. a performance extreme value. pre-building a processing raw material information library, which stores multiple sets of sample processing raw materials-sample performance index sets-sample material specifications; As stated Using performance extreme values ​​as screening constraints, the raw material information database is traversed to obtain the first gear processing raw material, wherein all performance indicators in the sample performance index set of the first gear processing raw material are superior to those of the original material. One performance extreme value; by analogy, the multiple kinds of gear processing raw materials are obtained.

5. The method of claim 1, wherein, The K sets of gear parameter information are classified into groups according to the multiple sets of gear pairs with the same performance, to obtain multiple sets of gear specification information, and the method further comprises: Based on the K sets of gear parameter information, a production log is called to obtain a K set of historical milling allowance constraints, wherein each set of historical milling constraints has a historical milling efficiency and a historical milling scrap rate identifier; A preset milling weight configuration is configured, wherein the milling weight configuration includes a milling efficiency weight and a scrap rate weight; Based on the milling weight configuration, the K set of historical milling allowance constraints is weighted and calculated to obtain a K set of historical milling coefficient sets; The K set of historical milling coefficient sets is serialized and an extreme value is called to obtain K milling allowance extreme values; The K milling allowance extreme values are used to map and replace the multiple sets of gear specification information to obtain multiple sets of gear allowance parameters.

6. The method of claim 5, wherein, According to the multiple sets of gear specification information and the multiple standard material specification information, an optimized milling is performed to obtain multiple sets of plate milling strategies, and the method further comprises: A first set of gear allowance parameters and a first standard material specification information are mapped and called from the multiple sets of gear allowance parameters and the multiple standard material specification information, wherein the first set of gear allowance parameters is mapped to M functional gears, and M is a positive integer less than K; Based on the first set of gear allowance parameters and the first standard material specification, a two-dimensional modeling is performed to obtain M first gear models and a first standard blank model; M gear processing benefits of the M functional gears are interactively obtained; The M first gear models are taken as an optimization reference, the M gear processing benefits are taken as a milling strategy evaluation reference, and an optimized milling is performed on the first standard blank model to obtain a first plate milling strategy; By analogy, the multiple sets of plate milling strategies are obtained.

7. The method of claim 6, wherein, The M first gear models are taken as an optimization reference, the M gear processing benefits are taken as a milling strategy evaluation reference, and an optimized milling is performed on the first standard blank model to obtain a first plate milling strategy, and the method further comprises: The M first gear models are serialized according to the M gear processing benefits to obtain a first gear sequence; A first benefit gear is called based on the first gear sequence; The M first gear models and the first benefit gear are dropped and fitted based on the first standard blank model to obtain a first filling result; A second benefit gear is called based on the first gear sequence; The second benefit gear is used to traverse the first filling result to obtain a second filling result; By analogy, until the Mth benefit gear of the first gear sequence traverses the M-1th filling result to obtain the first plate milling strategy, wherein the first plate milling strategy includes M sets of milling center coordinates.

8. A system for optimizing the manufacturing process of a wind turbine gear, characterized in that, The system comprises: A design information interaction unit is configured to interactively obtain target design information of a wind turbine gearbox, wherein the target design information includes K sets of gear parameter information and K sets of gear performance information, and the K sets of gear parameter information and the K sets of gear performance information are associated and mapped based on K functional gears, and K is a positive integer; An analysis module construction unit is configured to pre-construct a performance deviation analysis module; The performance deviation analysis unit is configured to synchronize the K sets of gear performance information to the performance deviation analysis module for performance deviation analysis, so as to divide the K functional gears into groups according to machining raw materials, and obtain multiple groups of gears with the same performance; The machining raw material calling unit is configured to call machining raw materials according to the multiple groups of gears with the same performance, and obtain multiple kinds of gear machining raw materials; The specification information calling unit is configured to interactively obtain multiple kinds of standard material specification information of the multiple kinds of gear machining raw materials; The gear specification calling unit is configured to divide the K sets of gear parameter information into groups according to the multiple groups of gears with the same performance, and obtain multiple groups of gear specification information; The milling strategy optimization unit is configured to optimize milling according to the multiple groups of gear specification information and the multiple kinds of standard material specification information, and obtain multiple groups of plate milling strategies; The batch milling execution unit is configured to perform batch milling process of the K functional gears by using the multiple groups of plate milling strategies.

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