Method and system for optimizing complex molded surface machining parameters

Abstract

The invention aims to provide a method and a system for optimizing complex molded surface machining parameters. The method comprises the following steps: a, carrying out data acquisition on a to-be-machined object; b, performing data processing on the data set; c, establishing a data optimization algorithm model; d, optimizing the data optimization algorithm model by adopting an artificial intelligence algorithm, performing optimization prediction on manufacturing parameters of the to-be-processed object, and identifying key parameters influencing processing; e, further iteration and loop optimization are carried out according to the optimized data optimization algorithm model, and key parameters after deep optimization are obtained; and f, the product is machined according to the key parameters obtained after deep optimization and the boundary conditions, whether the complex molded surface of the machined part meets the requirement or not is detected, and if the requirement is met, the optimized complex molded surface machining parameters are output. The method for optimizing the machining parameters of the complex molded surface has universality and can improve the precision and machining efficiency of the complex molded surface.

Description

Technical Field

[0001] The invention relates to the technical field of mechanical processing and manufacturing, and in particular to a method and system for optimizing complex surface processing parameters. Background Art

[0002] With the continuous improvement of the complexity and precision of part surfaces, manufacturing technology is developing towards high efficiency, low cost, high precision and leanness, forcing traditional manufacturing technology to continuously transform and upgrade, from automation and digitization to intelligence. Intelligent manufacturing has become the main development direction of parts manufacturing technology in the future. Intelligent manufacturing of complex surfaces is directly constrained by factors such as equipment performance, material properties, and processing conditions, which restricts the improvement of complex surface processing accuracy and processing efficiency. With the development of big data processing and optimization technology, it can provide a new way to improve the quality and efficiency of complex surfaces.

[0003] At present, the processing of complex surfaces mainly relies on expert experience and prior knowledge, which has great limitations and is difficult to achieve automation and high-precision requirements. At the same time, the processing data collection and data management of complex surfaces are difficult, there are many processing parameters, it is difficult to select processing parameter features, there is little data accumulation, and it is difficult to determine processing parameters for small samples.

[0004] As digital design represented by computer-aided design and digital manufacturing represented by CNC machining equipment gradually become the mainstream of current product research and development, how to efficiently and high-fidelity convert digital design models into digital manufacturing models has also become one of the difficulties restricting current digital manufacturing. Summary of the invention

[0005] The object of the present invention is to provide a method for optimizing complex surface machining parameters, which is universal and can improve the precision and machining efficiency of complex surfaces.

[0006] A method for optimizing complex surface machining parameters to achieve the above-mentioned purpose comprises the following steps:

[0007] a. Collect data on the object to be processed and obtain a data set;

[0008] b. performing data processing on the data set;

[0009] c. Extracting features from the processed data set, dividing the data set according to the results of the feature extraction, and establishing a data optimization algorithm model;

[0010] d. Use artificial intelligence algorithms to optimize the data optimization algorithm model, optimize and predict the manufacturing parameters of the object to be processed, and identify key parameters that affect processing;

[0011] e. Further iterate and cyclically optimize the optimized data optimization algorithm model to obtain key parameters after deep optimization;

[0012] f. Process the product according to the key parameters and boundary conditions after the deep optimization, and check whether the complex surface of the processed part meets the requirements. If not, repeat the above steps a to e until the requirements are met; if the requirements are met, output the optimized complex surface processing parameters.

[0013] In one or more embodiments, in step a, the data collection includes historical processing data and simulation data.

[0014] In one or more embodiments, in step a, the data set includes a part material data set, a part structure feature data set, a processing equipment data set, a processing technology data set, a process parameter data set, and a detection requirement data set.

[0015] In one or more embodiments, in step b, the data processing includes data cleaning, data organization, and structured storage.

[0016] In one or more embodiments, the data cleaning includes: identifying missing data, invalid data, inconsistent and duplicate data, and confirming that the collected data is correct and reasonable.

[0017] In one or more embodiments, in step c, the data optimization algorithm model is established according to the correspondence between the characteristics of the divided data set and the processing results.

[0018] In one or more embodiments, step c includes: performing a preliminary evaluation on the data to verify the accuracy of data processing. If the evaluation result does not meet the requirements, re-execute step b; if the evaluation result meets the requirements, proceed to step d.

[0019] In one or more embodiments, the artificial intelligence algorithm is based on a multi-objective particle swarm optimization algorithm.

[0020] In one or more embodiments, identifying key parameters affecting processing includes:

[0021] Processing parameters that affect the test results;

[0022] Processing parameters that cause cross-effects.

[0023] On the other hand, according to some embodiments of the present application, a system for optimizing complex surface machining parameters is provided, which includes:

[0024] A data acquisition module, used for collecting data of the object to be processed;

[0025] A data processing module, used for performing data processing on the data set; and

[0026] Parameter extraction and optimization module, used to establish data optimization algorithm model;

[0027] The system for optimizing complex surface machining parameters obtains optimized complex surface machining parameters through the method for optimizing complex surface machining parameters as described above.

[0028] On the other hand, according to some embodiments of the present application, a readable storage medium is provided, on which computer instructions are stored. When the computer instructions are executed by a processor, the steps of the method for optimizing complex surface machining parameters as described above are implemented.

[0029] The beneficial effects of the present invention are:

[0030] By optimizing the processing parameters of complex surfaces, the automation and high-precision requirements of complex surfaces are realized, the cycle from product design to physical manufacturing is shortened, labor costs are reduced, and dependence on experience is reduced. The data in the complex surface manufacturing process is integrated and analyzed, and the processing quality and efficiency of complex surfaces are improved by using big data and parameter optimization methods. The complex surface manufacturing system based on big data and parameter optimization realizes the intelligentization and universalization of complex surface manufacturing. For the manufacturing of small sample complex surfaces, the optimization prediction of small sample complex surface processing parameters is realized, and the processing efficiency of small sample complex surfaces is improved.

[0031] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Moreover, the same reference numerals are used throughout the drawings to represent the same components. In the drawings:

[0033] Figure 1 A schematic diagram showing the flow chart of some embodiments of the method for optimizing complex surface machining parameters;

[0034] Figure 2 Schematic diagrams showing some embodiments of the system for optimizing complex surface machining parameters according to the present invention. DETAILED DESCRIPTION

[0035] The following embodiments of the technical solution of the present application are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application, and are therefore only used as examples, and cannot be used to limit the scope of protection of the present application.

[0036] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0037] The end of one or more terms in the text is as follows:

[0038] Big data: a data set with large capacity, multiple types, fast access speed and high application value as its main characteristics.

[0039] Parameter optimization: It is a method to achieve the design goal by parameterizing the design goal and using optimization methods to continuously adjust the design variables so that the design results continue to approach the parameterized target value.

[0040] Optimization algorithm: generally refers to optimization theory and algorithm, which refers to the optimization of relevant performance of the algorithm, such as time complexity, space complexity, correctness, robustness, etc.

[0041] Data acquisition: refers to the automatic collection of non-electrical or electrical signals from analog and digital units under test such as sensors and other devices under test, and sending them to the host computer for analysis and processing.

[0042] Data processing: refers to the process of using electronic computers to enter, edit, summarize, calculate, analyze, predict, store and manage large amounts of raw data or information.

[0043] At present, the existing processing of complex surfaces has the following problems:

[0044] 1) There is a digital divide between digital design and digital manufacturing, such as inconsistent models and lack of standardization;

[0045] 2) The processing requirements for complex surfaces are high, there are many parameters that affect the processing accuracy and quality, it is difficult to determine the key processing parameters, the processing efficiency is low, and the intelligence and generalization of complex surfaces are difficult to achieve;

[0046] 3) The processing technology of complex surfaces is complicated, and the processing data is relatively small. It is difficult to optimize parameters through existing processing data to improve the accuracy and processing efficiency of complex surfaces;

[0047] 4) Heavy reliance on the experience of process personnel and product trial and error processing;

[0048] 5) It is difficult to use the collected processing data to improve the accuracy and efficiency of complex surface processing and optimize the processing parameters.

[0049] In order to solve at least one of the problems existing in the processing of the aforementioned existing complex profiles, a method for optimizing the processing parameters of the complex profiles is provided according to some embodiments of the present application, such as Figure 1 A schematic flow chart of some embodiments of the method for optimizing complex surface machining parameters is shown.

[0050] The method for optimizing complex surface machining parameters includes the following steps:

[0051] Step a. Collect data on the object to be processed to obtain a data set;

[0052] Step b. performing data processing on the data set;

[0053] Step c. extracting features from the processed data set, dividing the data set according to the results of the feature extraction, and establishing a data optimization algorithm model;

[0054] Step d. Use artificial intelligence algorithms to optimize the data optimization algorithm model, optimize and predict the manufacturing parameters of the object to be processed, and identify the key parameters that affect the processing;

[0055] Step e. further iterate and optimize the algorithm model according to the optimized data to obtain the key parameters after deep optimization;

[0056] Step f. Process the product according to the key parameters and boundary conditions after deep optimization, and check whether the complex surface of the processed part meets the requirements. If not, repeat the above steps a to e until the requirements are met; if the requirements are met, output the optimized complex surface processing parameters.

[0057] In some specific embodiments of the method for optimizing complex surface machining parameters, in step a, data collection includes historical machining data and simulation data.

[0058] In some specific embodiments of the method for optimizing complex surface processing parameters, in step a, the data set includes a part material data set, a part structure feature data set, a processing equipment data set, a processing technology data set, a process parameter data set, and a detection requirement data set.

[0059] In some specific embodiments of the method for optimizing complex surface machining parameters, in step b, data processing includes data cleaning, data sorting, and structured storage, so as to achieve multi-level, multi-dimensional, and multi-angle standardized description of massive data, and provide a readable, executable, and understandable data warehouse for artificial intelligence model building.

[0060] In some specific embodiments of the method for optimizing complex surface machining parameters, step b also includes design model parametric extraction. Specifically, this step is to parametrically deconstruct the non-manufacturing parametric design digital model according to the digital manufacturing requirements, and on this basis, perform manufacturing parametric modeling on the processing object design. It includes but is not limited to the steps of product design model structural feature extraction, parametric analysis and reconstruction.

[0061] Furthermore, in some specific embodiments, structural features include but are not limited to: size requirements, tolerance requirements, preparation requirements, material feature requirements and local detail feature (including holes, grooves, chamfers and other features) requirements, detection requirements, etc. These requirement data are collected to form a digital manufacturing model requirement feature data set.

[0062] Furthermore, in some specific embodiments, the part material includes the material grade, composition, preparation method, material processing characteristics and material basic properties, etc., and these data sets constitute the part material data set.

[0063] Furthermore, in some specific embodiments, the processing equipment data include machine tool brand, manufacturer, year, travel, tool (brand, manufacturer, material, model, configuration, usage record, etc.), maximum spindle speed, difference compensation method, positioning accuracy and reset accuracy, etc. These data are collected to form a processing equipment data set.

[0064] Furthermore, in some specific embodiments, product processing technology and process refers to the process used in the process of profile processing, and the process refers to the use of different roughing and finishing steps. The data required to be collected during the processing process include the selection of processing benchmarks before processing, the processing environment temperature and the operator's experience value, and the data collected during roughing, semi-finishing and finishing. Tool data includes data such as tool type, tool number, tool diameter, tool size, tool inclination angle, blade length, etc. The data that need to be collected in different processing processes include processing methods, processing allowances, processing depth, feed rate, material removal rate and processing deformation. These data constitute the processing technology data set and process parameter data set of complex profiles.

[0065] Furthermore, in some specific embodiments, the product structure inspection data includes information such as size, shape tolerance, position tolerance and surface roughness, and these data constitute a complex surface inspection requirement data set.

[0066] Among them, the part material data set, part structure feature data set, processing equipment data set, processing technology data set, process parameter data set, and inspection requirement data set together constitute the processing data set.

[0067] In some embodiments of the method for optimizing complex surface machining parameters, data cleaning includes: identifying missing data, invalid data, inconsistent and duplicate data, and confirming that the collected data is correct and reasonable.

[0068] Furthermore, in some embodiments of the method for optimizing complex surface machining parameters, data processing is also included on the collected machining data set, including data extraction, row filtering, column screening and conversion type, outlier processing, data standardization, normalization, principal component analysis, etc.

[0069] Furthermore, in some embodiments of the method for optimizing complex surface machining parameters, statistical analysis of the analyzed data is also included, including determining the mean, standard deviation, minimum, maximum and median of the data, correlation coefficient test and regression analysis.

[0070] Furthermore, in some embodiments of the method for optimizing complex surface machining parameters, in step c, feature extraction is performed on the statistically analyzed data, and the data set is divided according to the feature extraction results. Specifically, the data set is divided according to the type of feature.

[0071] In some embodiments of the method for optimizing complex surface machining parameters, in step c, a data optimization algorithm model is established based on the corresponding relationship between the characteristics of the divided data set and the machining results.

[0072] In some embodiments of the method for optimizing complex surface machining parameters, after step c, the following steps are included: preliminary evaluation of the data, verification of the accuracy of data processing, if the evaluation result does not meet the requirements, re-execution of step b, if the evaluation result meets the requirements, then continue with step d. This is to reduce the output result error caused by data errors in subsequent steps. Among them, the verification method of the preliminary evaluation is to use the output information such as machining process and machining parameters given by the optimization algorithm model, and actually perform machining in practice, and compare whether the predicted and processed results are consistent, so as to achieve the purpose of verification through this method.

[0073] In some embodiments of the method for optimizing complex surface machining parameters, the artificial intelligence algorithm is based on a multi-objective particle swarm optimization algorithm.

[0074] In some embodiments of the method for optimizing complex surface machining parameters, the key parameters that affect machining include: machining parameters that affect the detection results and machining parameters that cause cross-effects. Specifically, by inputting the machining process and corresponding parameters into the obtained data optimization algorithm model, the predicted machining results can be output, and the key parameters can be obtained by comparing the results.

[0075] In some embodiments of the method for optimizing complex surface machining parameters, the data collected after each new machining is added to the data set, and the process is continuously optimized and iterated to improve the machining efficiency.

[0076] In some embodiments of the method for optimizing complex surface machining parameters, when the machining data is relatively small, data of parts with similar features to the machining surface can be selected, data of materials different from the machining surface can be selected, and data similar to the selected process can be selected. By adopting such a strategy, massive data can be formed for optimizing complex surface machining parameters.

[0077] In some specific embodiments, a complex surface refers to a surface containing curved surfaces with different curvatures, a surface with relatively large structural changes, and a surface with relatively many structural features.

[0078] On the other hand, according to some embodiments of the present application, a system for optimizing complex surface processing parameters is also provided, such as Figure 2 The schematic diagram of some embodiments of the system for optimizing complex surface machining parameters is shown. The system 100 for optimizing complex surface machining parameters includes: a data acquisition module 101, a data processing module 102, and a parameter extraction and optimization module 103. The data acquisition module 101 is used to collect data on the object to be processed, the data processing module 102 is used to process the data set, and the parameter extraction and optimization module 103 is used to establish a data optimization algorithm model. Among them, the system for optimizing complex surface machining parameters obtains the optimized complex surface machining parameters by the method for optimizing complex surface machining parameters as described in one or more of the previous embodiments.

[0079] The computer-readable storage medium provided by the present disclosure stores computer instructions, which, when executed by a processor, can implement the method for optimizing complex surface machining parameters provided by any of the above embodiments, thereby obtaining optimized complex surface machining parameters.

[0080] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software module may reside in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor so that the processor can read and write information from / to the storage medium. In an alternative, a storage medium may be integrated into a processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In an alternative, the processor and the storage medium may reside in a user terminal as discrete components.

[0081] In one or more exemplary embodiments, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented as a computer program product in software, each function may be stored on or transmitted by a computer-readable medium as one or more instructions or codes. Computer-readable media include both computer storage media and communication media, including any medium that facilitates the transfer of a computer program from one place to another. Storage media may be any available medium that can be accessed by a computer. As an example and not limitation, such a computer-readable medium may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, disk storage or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer. Any connection is also properly referred to as a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwaves, the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwaves are included in the definition of the medium. Disk and disc as used herein include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc, wherein disk often reproduces data magnetically, while disc reproduces data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

[0082] As described above, the method for optimizing the processing parameters of complex surfaces recorded in one or more embodiments is based on big data analysis and artificial intelligence models, realizing the seamless connection of product design to digital manufacturing, and breaking down the barriers between design and manufacturing. At the same time, by collecting and analyzing the existing processing data of complex parts, the correlation between processing parameters and processing quality and efficiency is established, and the optimization algorithm based on artificial intelligence is used to realize the application of big data and artificial intelligence technology in complex surfaces. By extracting the key factors affecting the processing accuracy and quality such as the shape characteristics, materials and processing technology of complex parts, using the existing processing data in these aspects, based on massive historical data, through data screening and cleaning, an optimization model and artificial intelligence method are established to optimize the key processing parameters, improve the processing accuracy and efficiency, and for small sample scenarios such as new models and trial models, the optimization strategies and methods based on small data volumes are studied to realize the optimization prediction of the processing parameters of small sample complex surfaces.

[0083] By optimizing the processing parameters of complex surfaces, the automation and high-precision requirements of complex surfaces are realized, the cycle from product design to physical manufacturing is shortened, labor costs are reduced, and dependence on experience is reduced. The data in the complex surface manufacturing process is integrated and analyzed, and the processing quality and efficiency of complex surfaces are improved by using big data and parameter optimization methods. The complex surface manufacturing system based on big data and parameter optimization realizes the intelligentization and universalization of complex surface manufacturing. For the manufacturing of small sample complex surfaces, the optimization prediction of small sample complex surface processing parameters is realized, and the processing efficiency of small sample complex surfaces is improved.

[0084] The following uses a blade as an example to illustrate how to achieve high-quality and efficient manufacturing of the blade by optimizing complex surface processing parameters.

[0085] For the blade design section, the blade root, blade middle and blade tip are considered. According to the parts processing stage, the obtained data sets include blade tenon processing data, blade profile processing data and inspection and detection data.

[0086] For blade root tenon, pre-processing data include:

[0087] Workpiece parameters: including material category and weighing records.

[0088] Machine tool data: including machine tool type, blank workpiece coordinate system, time record, ambient temperature and operator experience.

[0089] The processing data of the blade root tenon is divided into tenon rough processing data and tenon finishing data. The tenon rough processing data and tenon finishing data respectively include: tool type, tool diameter, tool blade length, number of tool blades, cantilever length, rake angle, roll angle, spindle speed, cutting depth, feed per tooth, time record, ambient temperature and operator-related experience.

[0090] For the blade profile, the pre-machining data includes machine tool data, specifically the profile machining coordinate system, weighing records, time records, ambient temperature and operator-related experience.

[0091] The processing data of the blade profile is divided into blade rough processing data, blade semi-finishing data, blade finishing data and blade profile data. The blade rough processing data, blade semi-finishing data and blade finishing data respectively include: tool type, tool diameter, tool blade length, number of tool blades, cantilever length, rake angle, side rake angle, spindle speed, cutting depth, feed per tooth, time record, ambient temperature and operator experience. The blade profile data includes weighing records, geometric tolerances and surface roughness.

[0092] Blade inspection data includes:

[0093] Surface roughness specifically includes: looseness in FF area, loose area of ​​the area, loose defect area, penetrating poor glue, poor glue with a diameter greater than 15mm, rich and poor glue in FF area, and rich and poor glue affected area.

[0094] Geometric tolerances include: chord length tolerance, leading edge length tolerance, torsion angle tolerance, thickness tolerance, cross-sectional position tolerance, tenon profile tolerance and weighing records.

[0095] Test data, including: instrument model, instrument accuracy, time records, ambient temperature and operator experience.

[0096] After identifying the key data of the blade, the following key hyper parameters are optimized: tool type, tool diameter, tool edge length, number of tool edges, cantilever length, rake angle, side rake angle, spindle speed, cutting depth, feed per tooth, machining time, temperature control and operator experience.

[0097] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and they should all be included in the scope of the claims and specification of the present application. In particular, as long as there is no structural conflict, the various technical features mentioned in the various embodiments can be combined in any way. The present application is not limited to the specific embodiments disclosed herein, but includes all technical solutions that fall within the scope of the claims.

Claims

1. A method for optimizing complex surface machining parameters, characterized in that: The steps include: a. Collect data on the object to be processed and obtain a data set; b. performing data processing on the data set; c. Extracting features from the processed data set, dividing the data set according to the results of the feature extraction, and establishing a data optimization algorithm model; d. Use artificial intelligence algorithms to optimize the data optimization algorithm model, optimize and predict the manufacturing parameters of the object to be processed, and identify key parameters that affect processing; e. Further iterate and cyclically optimize the optimized data optimization algorithm model to obtain key parameters after deep optimization; f. Processing the product according to the key parameters and boundary conditions after the deep optimization, and detecting whether the complex surface of the processed part meets the requirements. If not, repeating the above steps a to e until the requirements are met; If the requirements are met, the optimized complex surface processing parameters are output.

2. The method for optimizing complex surface machining parameters according to claim 1, characterized in that: In step a, the data collection includes historical processing data and simulation data.

3. The method for optimizing complex surface machining parameters according to claim 2, characterized in that: In step a, the data set includes a part material data set, a part structure feature data set, a processing equipment data set, a processing technology data set, a process parameter data set, and a detection requirement data set.

4. The method for optimizing complex surface machining parameters according to claim 1, characterized in that: In step b, the data processing includes data cleaning, data organization, and structured storage.

5. The method for optimizing complex surface machining parameters according to claim 4, characterized in that: The data cleaning includes: identifying missing data, invalid data, inconsistent and duplicate data, and confirming that the collected data is correct and reasonable.

6. The method for optimizing complex surface machining parameters according to claim 1, characterized in that: In step c, the data optimization algorithm model is established according to the corresponding relationship between the characteristics of the divided data set and the processing results.

7. The method for optimizing complex surface machining parameters according to claim 1, characterized in that: Step c includes: preliminary evaluation of the data, verification of the accuracy of data processing, if the evaluation result does not meet the requirements, re-execute step b, if the evaluation result meets the requirements, continue to step d.

8. The method for optimizing complex surface machining parameters according to claim 1, characterized in that: The artificial intelligence algorithm is based on a multi-objective particle swarm optimization algorithm.

9. The method for optimizing complex surface machining parameters according to claim 1, characterized in that: The key parameters that affect the process were identified as: Processing parameters that affect the test results; Processing parameters that cause cross-effects.

10. A system for optimizing complex surface processing parameters, characterized in that: include: A data acquisition module, used for collecting data of the object to be processed; A data processing module, used for performing data processing on the data set; as well as Parameter extraction and optimization module, used to establish data optimization algorithm model; The system for optimizing complex surface machining parameters obtains optimized complex surface machining parameters by the method for optimizing complex surface machining parameters as described in any one of claims 1 to 9.

11. A readable storage medium, characterized in that: Computer instructions are stored thereon, and when the computer instructions are executed by a processor, the steps of the method for optimizing complex surface machining parameters as claimed in any one of claims 1 to 9 are implemented.

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