A blanking parameter self-adaptive control method and system for air wave machine assembly

Abstract

The application provides a blanking parameter adaptive control method and system for air wave machine assembly, and relates to the technical field of automatic control. The method comprises the following steps: obtaining air wave machine assembly structure and size requirements of each component, calculating assembly criticality of each component, generating blanking state data set, forming blanking control parameters, dynamically adjusting blanking speed, tool feed and compensation amount, calculating actual blanking deviation, adjusting blanking control parameters according to the actual blanking deviation, dynamically adjusting blanking speed and tool feed, and matching assembly process requirements and optimizing production efficiency. The application solves the problems of lack of collaborative control in the blanking process, difficulty in inhibiting error accumulation and insufficient precision of key components in the prior art, realizes dynamic regulation and closed-loop optimization of blanking parameters based on assembly criticality and machining state, thereby ensuring the precision of key dimensions while improving overall assembly consistency, and significantly improving assembly precision, pneumatic performance stability and production efficiency.

Description

Technical Field

[0001] This application relates to the field of automation control technology, and in particular to an adaptive control method and system for feeding parameters of a gas wave machine assembly. Background Technology

[0002] As the application of air wave machines in automated production and precision pneumatic control continues to deepen, their assembly accuracy has a direct impact on the stability of airflow performance and the reliability of equipment operation. Air wave machines are typically composed of multiple functional components, and these components form a critical dimensional chain through complex assembly relationships. Dimensional deviations between different components have a transmission and superposition effect during the assembly process, thus affecting the overall aerodynamic performance. Therefore, higher requirements are placed on the accuracy and consistency of material cutting.

[0003] Existing material cutting control methods mostly employ fixed-size control, relying primarily on experience to set cutting dimensions and compensation amounts. They lack comprehensive analysis of assembly criticality, equipment operating status, tool wear, and environmental factors. Furthermore, in multi-component collaborative cutting processes, existing technologies fail to systematically constrain and optimize assembly sequence and airflow channel spatial positioning, making dynamic adjustment and collaborative control during processing difficult. This results in cutting errors being difficult to correct in a timely manner, consequently affecting assembly accuracy consistency and aerodynamic performance stability.

[0004] In summary, existing technologies suffer from insufficient control over cutting accuracy, lack of dynamic process adjustment, and lack of coordinated optimization of assembly sequence and compensation allocation during multi-component assembly and cutting. There is an urgent need to provide an adaptive control method for cutting parameters based on assembly criticality and processing status information to achieve closed-loop adjustment and coordinated optimization of the multi-component cutting process, thereby improving the consistency of assembly accuracy and the stability of aerodynamic performance. Summary of the Invention

[0005] The purpose of this application is to provide an adaptive control method and system for blanking parameters in the assembly of air wave machines, in order to solve the problems in the prior art where blanking control relies only on fixed dimensions and empirical parameters, and does not comprehensively consider the criticality of component assembly, processing equipment status, tool wear and environmental factors, resulting in insufficient assembly accuracy of multiple components, airflow performance fluctuations and reduced production efficiency.

[0006] In view of the above problems, this application provides an adaptive control method and system for feeding parameters of a gas wave machine assembly.

[0007] In a first aspect, this application provides an adaptive control method for blanking parameters in airwave machine assembly. The method is implemented through an adaptive control system for blanking parameters in airwave machine assembly. The method includes: acquiring the airwave machine assembly structure and the dimensional requirements of each component; constructing component assembly relationships and a critical dimension chain; and calculating the assembly criticality of each component based on the critical dimension chain; collecting raw material dimensional information, processing equipment operating status information, tool status information, and environmental parameters; and uniformly processing the collected data to generate a blanking status dataset; based on the assembly criticality and the blanking status dataset, combined with the assembly sequence and airflow channel position distribution relationship, generating target dimension control parameters and compensation control parameters for each component; inputting the target dimension control parameters and compensation control parameters into a control execution unit, and implementing them during execution. The system acquires processing status information in real time, and dynamically adjusts the target size control parameters and compensation control parameters based on the processing status information to achieve precision control of the blanking process; it acquires actual size detection data of each component, and compares and analyzes the actual size detection data with the target size control parameters to generate blanking deviation data; it corrects the target size control parameters and compensation control parameters based on the blanking deviation data, and feeds the correction results back to steps S3 and S4 to form closed-loop control; it optimizes the control execution sequence and compensation control parameters collaboratively based on the assembly sequence and the spatial position relationship of each component in the airflow channel to ensure consistency of key dimensions and aerodynamic performance; and it adaptively adjusts the control execution parameters according to the processing cycle and real-time processing status to achieve processing cycle matching and efficiency optimization.

[0008] Secondly, this application also provides an adaptive control system for blanking parameters of a wave machine assembly, used to execute an adaptive control method for blanking parameters of a wave machine assembly as described in the first aspect. The system includes: an assembly structure modeling module, used to acquire the assembly structure of the wave machine and the dimensional requirements of each component, construct component assembly relationships and critical dimension chains, and calculate the assembly criticality of each component based on the critical dimension chains; a status data acquisition and processing module, used to acquire raw material size information, processing equipment operating status information, tool status information, and environmental parameters, and uniformly process the acquired data to generate a blanking status dataset; a control parameter generation module, used to generate target size control parameters and compensation control parameters for each component based on the assembly criticality and the blanking status dataset, combined with the assembly sequence and airflow channel position distribution relationship; and an execution control adjustment module, used to input the target size control parameters and compensation control parameters into a control execution unit, and adjust the control parameters during execution. The system acquires processing status information in real time during the process and dynamically adjusts the target size control parameters and compensation control parameters based on the processing status information to achieve precision control of the blanking process. A size detection and analysis module acquires actual size detection data for each component and compares and analyzes the actual size detection data with the target size control parameters to generate blanking deviation data. A deviation feedback correction module corrects the target size control parameters and compensation control parameters based on the blanking deviation data and feeds the correction results back to steps S3 and S4 to form a closed-loop control. A sequence compensation optimization module optimizes the control execution sequence and compensation control parameters based on the assembly sequence and the spatial relationship of each component in the airflow channel to ensure consistency of key dimensions and aerodynamic performance. A cycle time adaptive adjustment module adaptively adjusts the control execution parameters according to the processing cycle time and real-time processing status to achieve cycle time matching and efficiency optimization in the processing process.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages: By constructing an assembly structure model of the airwave machine, the assembly relationships and critical dimension chains between components are clarified, and the assembly criticality of each component is quantitatively calculated. This enables effective identification of critical components and dimensions, thereby prioritizing the machining accuracy of critical components during multi-component processing and improving overall assembly accuracy and consistency. Based on this, by collecting and uniformly processing data on raw material dimensions, processing equipment status, tool status, and environmental parameters, a blanking status dataset is formed. This provides a comprehensive characterization of multiple factors affecting blanking accuracy, offering reliable data support for blanking parameter optimization. Furthermore, by combining assembly criticality, the blanking status dataset, assembly sequence, and the spatial relationship of airflow channels, target size control parameters and compensation control parameters are generated. This enables refined control of the multi-component blanking process, effectively reducing dimensional deviations caused by equipment status fluctuations, tool wear, and environmental changes. During blanking execution, by acquiring processing status information in real time, the blanking speed, tool feed rate, and compensation amount are dynamically adjusted to ensure that the actual processed dimensions continuously approach the target dimensions, thereby improving the consistency of multi-component assembly and the stability of aerodynamic performance. By detecting the actual dimensions after blanking and comparing them with the target dimensions, blanking deviation data is generated. This deviation data is then fed back to the control parameter generation and execution process, achieving closed-loop optimization control of the blanking process and improving the stability and consistency of multi-batch production. Simultaneously, based on the assembly sequence and the spatial relationship of each component in the airflow channel, the control execution sequence and compensation allocation are collaboratively optimized to achieve priority control of key components and overall optimization under aerodynamic performance constraints, balancing assembly accuracy and aerodynamic performance requirements. Furthermore, the control execution parameters are adaptively adjusted according to the processing cycle and real-time processing status, improving processing efficiency and reducing manual intervention while ensuring assembly accuracy. In summary, this application constructs a closed-loop control mechanism for multi-component assembly blanking through quantitative analysis of assembly key metrics, multi-source state data-driven operation, adaptive adjustment of control parameters, and sequential collaborative optimization. This mechanism achieves a synergistic improvement in blanking accuracy, assembly consistency, aerodynamic performance, and production efficiency, overcoming the problems of reliance on experience-based control, difficulty in integrating multiple factors, and difficulty in guaranteeing the accuracy of key components in existing technologies. It has significant engineering application value.

[0010] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0012] Figure 1 This is a flowchart illustrating an adaptive control method for feeding parameters in the assembly of a wave machine according to this application. Figure 2 This is a schematic diagram of the structure of an adaptive control system for feeding parameters of a wave machine assembly according to this application.

[0013] Explanation of reference numerals in the attached figures: Assembly criticality analysis module 11, status data acquisition and processing module 12, control parameter generation module 13, execution control adjustment module 14, dimension detection and analysis module 15, deviation feedback correction module 16, sequence compensation optimization module 17, and cycle time adaptive adjustment module 18. Detailed Implementation

[0014] This application provides an adaptive control method and system for blanking parameters in the assembly of air-wave machines. It solves the problems in the prior art where the lack of comprehensive analysis of assembly criticality, multi-source processing status, and airflow channel position makes it impossible to achieve dynamic and coordinated control of the blanking process of multiple components, resulting in unstable blanking accuracy, poor assembly consistency, and aerodynamic performance fluctuations. The method achieves adaptive generation of control parameters based on assembly criticality and blanking status data, and dynamically adjusts them in combination with real-time status feedback during processing. This constructs a closed-loop control mechanism for the blanking of multiple components, significantly improving blanking accuracy, assembly consistency, and aerodynamic performance stability.

[0015] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0016] Example 1 Please see the appendix Figure 1 This application provides an adaptive control method for blanking parameters in the assembly of a wave machine. The method is applied to an adaptive control system for blanking parameters in the assembly of a wave machine, and specifically includes the following steps: S1: Obtain the assembly structure and dimensional requirements of each component of the air wave machine, construct the assembly relationship and critical dimension chain of the components, and calculate the assembly criticality of each component based on the critical dimension chain.

[0017] Furthermore, step S1 of this application also includes: S11. Obtain the design drawings of the airflow machine and perform structural analysis on the design drawings to extract the geometric dimension parameters, shape feature parameters, and assembly positioning feature parameters of each component, forming a component basic feature dataset; S12. Based on the component basic feature dataset, establish an assembly relationship matrix between components. The assembly relationship matrix is ​​used to characterize the connection sequence, assembly constraint relationships, and potential interference relationships of each component; S13. Based on the assembly relationship matrix and the component basic feature dataset, identify the key components and corresponding key dimensions involved in the airflow channel structure and assembly fit, construct a key dimension chain, and assign the key dimensions according to their position in the assembly... The order of actions and constraints in the assembly process are prioritized. The critical dimension chain includes pneumatic channel dimensions, connection surface gaps, and assembly positioning feature dimensions. S14: Based on the critical dimension chain, combined with assembly gap parameters, pneumatic channel layout features, and connection method parameters, the influence of each component in the critical dimension chain is quantitatively calculated to obtain the assembly criticality of each component. S15: Based on material thermal expansion characteristic parameters and processing environment temperature and humidity parameters, environmental impact correction is applied to each dimension in the critical dimension chain to generate a corrected critical dimension chain, and the assembly criticality is updated based on the corrected critical dimension chain.

[0018] Furthermore, step S14 of this application also includes: S14-1 Calculate dimensional sensitivity based on the geometric tolerance limit of each dimension in the critical dimension chain. The formula is: ;in, For the overall assembly accuracy F, the component dimensions are... The sensitivity coefficient is obtained through perturbation analysis using the assembly relationship matrix. The actual dimensional error range of the component under the current processing conditions is obtained statistically from the blanking status dataset. The design tolerance range for the components is determined by the design drawings; S14-2, based on the aerodynamic channel structure, CFD simulation analysis of single-factor disturbances is performed on each component to calculate the aerodynamic influence coefficient of each component on airflow performance. The calculation formula is: ;in, To change only the first component while keeping the structural parameters of other components unchanged. i The change in airflow pressure caused by the dimensional parameters of each component. For the corresponding number jThe airflow pressure change of each component under the same simulation conditions, where N is the total number of components; S14-3, based on the assembly sequence and assembly relationship matrix, analyze the error propagation relationship between components, and calculate the assembly sequence weight of each component. The calculation formula is as follows: ;in, For the first i The dimensional deviation of the first component is transmitted to the first... j The cumulative error generated by each component; S14-4, based on size sensitivity Aerodynamic influence coefficient and assembly sequence weight Calculate the assembly criticality of each component. The formula is: ； in, For the first i The criticality of assembling each component These are the weighting coefficients for size sensitivity, aerodynamic effects, and assembly sequence, respectively.

[0019] Specifically, the process involves acquiring the design drawings of the airwave machine and performing structural analysis on these drawings. During analysis, the geometric dimensions, shape features, and assembly positioning features of each component are extracted by reading the dimensions and structural information from the engineering drawings or 3D design files. These parameters include, for example, the positions of mating holes, the dimensions of connecting surfaces, and the reference positioning surfaces. This process forms a basic feature dataset for each component. This method allows for the unified organization of structural information for each component, facilitating subsequent processing. For instance, for the main air chamber, guide vane, and connecting flange of the airwave machine, the length, width, thickness, and hole distribution of each component are read from the CAD model, and the assembly positioning features of each component are identified to establish a precise assembly relationship matrix. This step provides basic dimensional information and assembly constraints for the cutting of multiple components, ensuring that the cutting data matches the design requirements and improving subsequent assembly accuracy. Subsequently, step S12 is executed, where the connection relationships between the components are analyzed based on the basic feature dataset, forming an assembly relationship matrix between the components. This assembly relationship matrix represents the connection sequence, assembly constraints, and potential interference relationships between the components. For example, the assembly sequence can be determined based on the correspondence of the connection interfaces, the constraint conditions can be determined based on the fit tolerance range, and the existence of interference risk can be judged in combination with the spatial position. Through the above processing, the assembly relationship of multiple components becomes clearer. Further, step S13 is executed, based on the assembly relationship matrix and the component basic feature dataset, relevant components involved in the formation of airflow channels and assembly fit are screened, and their corresponding key dimensions are extracted to construct a key dimension chain. Further, according to the transmission path and constraint order of each key dimension in the assembly process, they are prioritized. The key dimension chain includes aerodynamic channel dimensions, connection surface gaps, and assembly positioning feature dimensions, etc. By establishing the key dimension chain, the transmission relationship of dimensional deviations in the assembly process can be reflected. For example, for the channel width, flange face gap, and positioning pin hole position of the airflow guide plate of the wave machine, the sensitivity of its impact on the overall aerodynamic resistance and velocity distribution is determined through numerical simulation or airflow performance analysis, and the key dimensions are sorted, giving priority to the dimensions with the greatest impact on aerodynamic performance for precision control. Through this step, the processing accuracy of key components can be guaranteed in the blanking stage, thereby improving the overall aerodynamic performance stability. In step S14, based on the critical dimension chain, and in conjunction with assembly clearance parameters, pneumatic channel layout characteristics, and connection method parameters, the degree of influence of each component in the critical dimension chain is quantified to obtain the assembly criticality of each component. Specifically, each component can be classified or assigned a value according to the position of its corresponding critical dimension in the dimension chain, the number of constraints it participates in, and the degree of its influence on the continuity of the airflow channel, thereby obtaining the assembly criticality ranking result. Through this step, critical components and non-critical components can be distinguished.Subsequently, step S15 considers the impact of the processing environment on dimensions, and corrects the relevant dimensions in the critical dimension chain based on the material's thermal expansion characteristics and environmental temperature and humidity parameters. For example, when the environmental temperature deviates from the standard temperature, the dimensions can be corrected accordingly based on the material's thermal expansion characteristics; when humidity changes significantly, the dimensions of components susceptible to environmental influences are compensated and adjusted. After completing the dimension correction, the critical dimension chain is updated, and the assembly criticality is corrected accordingly to better suit the actual processing conditions. For example, during the assembly of the airflow channel of the wave machine, the guide plate and connecting node components directly affect the continuity of the airflow channel, and their corresponding dimensions are in critical positions in the critical dimension chain. Using the above method, such components can be identified as high-criticality components, thereby prioritizing their processing accuracy during subsequent material preparation. Through the above steps, this embodiment achieves a systematic sorting and quantification of the assembly relationships, critical dimension chains, and assembly criticality of multiple components of the wave machine. Without relying on complex modeling, it provides a reliable basis for determining subsequent material preparation control parameters, thereby improving the rationality of accuracy allocation and the overall aerodynamic performance stability during the multi-component assembly process. In summary, by acquiring and analyzing the geometric, assembly, and aerodynamic characteristics of each component of the waveguide, and by quantitatively calculating the criticality of assembly, a scientific data foundation is provided for subsequent precise material cutting and closed-loop optimization of multi-component assembly. This significantly improves material cutting accuracy, assembly consistency, and the overall performance stability of the waveguide. Furthermore, dimensional sensitivity is calculated based on the geometric tolerance limits of each dimension in the critical dimension chain. The formula is: ;in, For the overall assembly accuracy F, the component dimensions are... The sensitivity coefficient is obtained through perturbation analysis using the assembly relationship matrix. The actual dimensional error range of the component under the current processing conditions is obtained statistically from the blanking status dataset. The design tolerance range for the components is determined by the design drawings; S14-2, based on the aerodynamic channel structure, CFD simulation analysis of single-factor disturbances is performed on each component to calculate the aerodynamic influence coefficient of each component on airflow performance. The calculation formula is: ;in, To change only the first component while keeping the structural parameters of other components unchanged. i The change in airflow pressure caused by the dimensional parameters of each component. For the corresponding number j The airflow pressure change of each component under the same simulation conditions, where N is the total number of components; S14-3, based on the assembly sequence and assembly relationship matrix, analyze the error propagation relationship between components, and calculate the assembly sequence weight of each component. The calculation formula is as follows: ;in, For the firsti The dimensional deviation of the first component is transmitted to the first... j The cumulative error generated by each component; the weighting coefficient is determined based on the variance contribution rate of each factor in the overall assembly accuracy, using the following formula: ； ； ； in, These are the weighting coefficients for size sensitivity, aerodynamic effects, and assembly sequence. Based on size sensitivity Aerodynamic influence coefficient and assembly sequence weight Calculate the assembly criticality of each component. The formula is: ； in, For the first i The criticality of assembling each component.

[0020] S2: Collect raw material size information, processing equipment operating status information, tool status information and environmental parameters, and process the collected data in a unified manner to generate a material feeding status dataset.

[0021] Specifically, the process involves collecting raw material dimensional information, including the material's length, width, thickness, and surface morphology. This can be achieved by using a laser measuring instrument or a 3D scanning device to inspect the raw material and obtain information on its actual dimensions and surface defects. For example, for metal sheets used in the guide vanes of a waveguide machine, scanning can reveal information such as local deformation and edge irregularities. Collecting raw material dimensional information allows for the identification of material deviations before cutting, reducing processing deviations caused by material errors at the source. The process also includes collecting processing equipment operating status information, including parameters such as equipment temperature, vibration status, spindle speed, and positional accuracy. This can be achieved by deploying temperature sensors, vibration sensors, and displacement detection devices to monitor the cutting equipment's operation in real time. For example, during the operation of a CNC cutting equipment, spindle speed fluctuations and tool holder movement deviations can be obtained. Acquiring equipment status information allows for the assessment of the equipment's current processing capacity and stability, providing a basis for adjusting cutting parameters. Furthermore, the process involves collecting tool status information, including tool usage time, wear level, and changes in tool geometry. A tool management system can record the cumulative tool usage time and combine this information with cutting force detection results to determine the tool wear status. For example, when an abnormal increase in cutting force or a decrease in the quality of the machined surface is detected, it can be determined that the tool is showing signs of wear. By monitoring the tool's condition, timely compensation adjustments or tool replacements can be made during processing, reducing dimensional errors caused by tool wear. In addition, processing environmental parameters, including workshop temperature, humidity, and airflow conditions, are collected. Real-time temperature and humidity data can be obtained through environmental sensors, and the impact of local airflow on processing stability can be analyzed in conjunction with equipment layout. For example, in environments with large temperature variations, materials and equipment may experience thermal expansion; collecting environmental parameters can provide a basis for subsequent dimensional corrections, thereby reducing the impact of environmental factors on processing accuracy. After completing the above data collection, the raw material size information, processing equipment status information, tool status information, and environmental parameters are processed uniformly. Specifically, this may include time alignment, unit unification, and format standardization of data from different sources, and associating and organizing them according to component numbers to form a structured material cutting status dataset. The material cutting status dataset may include actual raw material size information, equipment operating status parameters, tool status parameters, and environmental impact parameters. Through the above processing, a centralized expression of the relevant influencing factors in the material cutting process can be achieved, providing a unified data basis for determining the target size control parameters and compensation control parameters in subsequent steps. For example, in actual production, when it is detected that the thickness of a batch of material is too large and the equipment temperature is too high, size compensation can be considered in advance in the subsequent material cutting control, thereby avoiding dimensional deviations after processing.Step S2 enables comprehensive collection and unified processing of information related to the material cutting process, allowing various influencing factors to be expressed in the same data system. This provides a reliable basis for subsequent material cutting parameter adjustments and closed-loop control, thereby improving material cutting accuracy and the consistency of multi-component assembly, while also enhancing the stability of the processing process.

[0022] S3: Based on the assembly criticality and the material cutting status dataset, and combined with the assembly sequence and the distribution relationship of airflow channels, generate target size control parameters and compensation control parameters for each component.

[0023] Furthermore, step S3 of this application also includes: S31. Extract the assembly criticality and blanking status data of each component, and associate and map the assembly criticality and blanking status data with the assembly sequence and the position of key airflow channels to construct a component control dataset; the component control dataset includes the basic geometric parameters of each component, assembly criticality ranking information, and assembly constraint relationship information; S32. Based on the basic geometric parameters and assembly constraint relationships in the component control dataset, calculate the target size offset of each component, generate the initial target blanking size, and prioritize the correction of key components according to the assembly criticality ranking; S33. Based on the tool wear status parameters, processing equipment thermal deformation parameters, and processing environment temperature and humidity parameters during the blanking process, correct the initial target blanking size to generate the corrected target blanking size; S34. Based on the correspondence between the actual material cutting deviation of each component and the assembly criticality in historical production data, a sliding window statistical analysis method is used to calculate the average deviation and deviation fluctuation range of different criticality intervals, and adjust the material cutting compensation amount allocation of each component accordingly to optimize the dimensional consistency and batch stability of critical components; S35. Based on the assembly sequence and the positional relationship of critical airflow channels, a component error propagation sequence is constructed, and the allocation order of material cutting compensation amount is optimized and adjusted with the minimum cumulative deviation of each component as a constraint; S36. Based on the corrected target material cutting size and the optimized compensation amount allocation order, the final material cutting control parameters are generated. The material cutting control parameters include the target material cutting size of each component, the material cutting compensation amount, and the material cutting execution order, which are used to guide the material cutting equipment to perform processing control.

[0024] Specifically, based on the assembly criticality calculation in step S1 and the construction of the material cutting status dataset in step S2, step S3 is further executed. By integrating the assembly criticality and material cutting status datasets, and combining them with the assembly sequence and the distribution of airflow channels, target size control parameters and compensation control parameters for each component are generated, thereby achieving precise control of the multi-component material cutting process. First, the assembly criticality and material cutting status data of each component are extracted, and the assembly criticality and material cutting status data are correlated with the assembly sequence and the position of critical airflow channels to construct a component control dataset. The component control dataset includes the basic geometric parameters of each component, assembly criticality ranking information, assembly constraint relationship information, and airflow channel position identification information. In this way, multi-source data is organized in a unified manner, so that the role relationship of each component in the assembly structure and aerodynamic path is clarified, providing a unified data foundation for subsequent calculations. For example, for the guide vane, air chamber shell, and connecting flange in the air wave machine, their positions in the airflow channel can be correlated with the assembly criticality, so that critical aerodynamic components participate in control in subsequent calculations. Based on the component control dataset, and combining fundamental geometric parameters and assembly constraints, the initial target blanking dimensions of each component are calculated. Specifically, according to the component's position in the critical dimension chain and its dimensional relationships with adjacent components, the design dimensions are offset and adjusted to obtain the initial target blanking dimensions. On this basis, the components are sorted according to their assembly criticality, with higher-criticality components receiving priority for dimensional correction. The allowable tolerances in the critical dimension chain are allocated proportionally to criticality, giving critical components a more stringent dimensional control range, while non-critical components are adjusted with relative relaxation while meeting assembly constraints, thus ensuring that the overall dimensional chain closure meets assembly requirements. First, the design dimensions of each component are extracted from the component control dataset. Assembly clearance requirements Assembly criticality And the dimensional constraint information related to this component in the assembly relationship matrix. Based on the assembly relationship matrix, determine the position of this component in the critical dimension chain and its dimensional coupling relationship with adjacent components, thereby establishing a set of dimensional constraint equations: ,in, For the first The initial target blanking dimensions for each component. This is the amount of correction for assembly clearance. In order to be with the first Each component exists within a set of components that are associated with each other in assembly. The dimension transfer factor is determined by the assembly relation matrix. This provides an estimate of the dimensional deviations for the relevant components, which can be obtained from historical material cutting deviation statistics. Secondly, it considers the assembly criticality... Sort all components to form a criticality sequence. Among these, components with high criticality are given priority for dimensional correction. Based on this, dimensional correction is performed preferentially on critical components, and the total tolerance in the critical dimension chain is allocated according to criticality to obtain the target dimensional correction amount for the critical components: ,in, The total allowable tolerance for the critical dimension chain is obtained by superimposing design tolerances. Further, the above corrections are added to the initial target blanking size to obtain the target blanking size for the critical components after priority correction. For non-critical components, secondary adjustments are made to their target dimensions while satisfying assembly constraints, ensuring the overall dimensional chain meets the closure condition, i.e.: ,in, The overall assembly dimensions are required. In the assembly of the airflow channel of the wave machine, the guide plate and the air chamber shell constitute a critical airflow channel dimension chain. Among them, the assembly of the guide plate is of high criticality, so the target blanking dimension of the guide plate is corrected first to control its dimensional deviation within a smaller range. The tolerance of secondary components such as connecting flanges is appropriately relaxed to ensure the accuracy of the overall airflow channel dimensions. Through the above method, the calculation and priority correction of the target blanking dimension based on the assembly criticality are realized, so that the key components can obtain higher dimensional control accuracy in the blanking stage, effectively reducing the accumulation of assembly errors, improving the consistency of multi-component assembly and the stability of the aerodynamic performance of the wave machine. Based on the blanking state dataset, the initial target blanking dimension is corrected. Specifically, this includes: determining the dimensional deviation trend caused by the tool according to the tool wear state parameters and making corresponding compensation; determining the impact of equipment thermal deformation on the dimensions according to the temperature and operating state parameters of the processing equipment and correcting it; and performing environmental correction of the dimensions according to the temperature and humidity parameters of the processing environment and the material properties. Various correction amounts can be determined based on historical statistical results or equipment calibration data, and the initial target blanking size is comprehensively adjusted to obtain the corrected target blanking size. This step effectively reduces the impact of equipment status changes and environmental fluctuations on blanking accuracy. Specifically, tool status information, processing equipment status information, and environmental parameter information corresponding to each component are extracted from the blanking status dataset. Tool status information includes cumulative tool usage time, cutting force changes, and vibration amplitude, used to characterize the degree of tool wear; processing equipment status information includes temperature and operational stability parameters of key equipment components, used to reflect the thermal deformation trend of the equipment during processing; environmental parameter information includes workshop temperature and humidity, used to reflect the impact of the processing environment on material dimensions. The degree of tool wear is evaluated based on the tool status information, and the dimensional correction amount caused by tool wear is determined according to the pre-established correspondence between tool wear and processing dimensional deviation. This correspondence is obtained through statistical analysis of historical production data, ensuring that different wear levels correspond to different dimensional compensation ranges, thereby maintaining blanking accuracy even under tool wear conditions. Based on the difference between the current temperature and the calibrated temperature of the equipment, and combined with the deformation characteristic data of the equipment under different temperature conditions, the dimensional correction amount caused by thermal deformation of the equipment is determined. The deformation characteristic data is obtained through equipment calibration experiments or historical operating data analysis, thereby effectively compensating for the impact of equipment thermal effects on the blanking dimensions. Based on the temperature and humidity parameters of the processing environment, combined with the thermal expansion characteristics and humidity sensitivity of the materials, the dimensional change caused by environmental factors is determined. For example, for metallic materials, corrections are made based on the effect of temperature changes on their linear expansion characteristics; for humidity-sensitive materials, the dimensional compensation amount is adjusted according to humidity changes. Relevant material parameters can be obtained from material handbooks or experimental calibration.After obtaining the dimensional correction amounts corresponding to tool wear, equipment thermal deformation, and environmental factors, these correction amounts are comprehensively superimposed to uniformly correct the initial target blanking size, resulting in the final corrected target blanking size. During this comprehensive superposition process, the weight of each correction amount can be adjusted based on historical production data to make the correction result more consistent with actual processing conditions. For example, during the blanking process of the airflow machine guide plate, when increased tool wear or increased equipment temperature is detected, the target size is adjusted accordingly to offset potential dimensional deviations during processing. By introducing equipment and environmental factor correction mechanisms, the impact of external disturbances on blanking accuracy can be significantly reduced, improving processing stability. Based on the correspondence between the actual blanking deviation of each component and assembly criticality in historical production data, a sliding window statistical analysis method is used to process continuous batch production data, calculate the average deviation and deviation fluctuation range corresponding to different criticality intervals, and optimize the allocation of blanking compensation amounts for each component accordingly. This method enables critical components to obtain a more stable compensation strategy, thereby improving the consistency of multi-batch production. By combining the assembly sequence and the positional relationship of key airflow channels, the error propagation path of each component during the assembly process is analyzed, an error propagation sequence is constructed, and the allocation order of blanking compensation is optimized and adjusted with the constraint of minimizing the overall cumulative deviation. Specifically, for components at the front end of the airflow channel or the starting position of the error propagation chain, compensation control is prioritized to reduce the cumulative amplification effect of errors in subsequent assembly, thereby improving the overall assembly accuracy. For example, for components at the front end of the airflow channel, their dimensional deviations will have a cumulative impact on subsequent components, so compensation adjustment is prioritized for them to reduce error propagation. Through this step, the cumulative effect of errors during the assembly process can be effectively controlled, improving the overall assembly quality. Finally, based on the corrected target blanking size and the optimized compensation allocation order, the final blanking control parameters are generated. The blanking control parameters include the target blanking size, blanking compensation amount, and blanking execution sequence of each component, and are used to guide the blanking equipment to perform processing control. By forming a complete set of blanking control parameters, unified scheduling and precise control of the blanking process of multiple components can be achieved. For example, during the assembly of the airflow channel in a wave machine, the guide plate and the air chamber shell constitute a critical dimension chain. The assembly of the guide plate is particularly critical. The method described above prioritizes the correction of its target dimensions, and dynamic compensation is implemented based on tool wear and equipment temperature changes. Furthermore, its processing is prioritized in the compensation sequence, thereby effectively controlling airflow channel dimensional errors and ensuring stable aerodynamic performance. By collaboratively processing multi-dimensional information such as assembly criticality, material cutting status data, historical deviation data, and assembly sequence, the precise determination of the target material cutting dimensions and the rational allocation of compensation amounts are achieved. This effectively reduces processing and assembly errors, improves the consistency of multi-component assembly and the stability of the wave machine's aerodynamic performance, and enhances the control precision and stability of the production process.

[0025] S4: Input the target size control parameters and compensation control parameters into the control execution unit, and acquire the processing status information in real time during the execution process. Adjust the target size control parameters and compensation control parameters dynamically according to the processing status information to achieve precision control of the blanking process.

[0026] Furthermore, step S4 of this application also includes: S41. Import the blanking control parameters into the blanking equipment control system and complete the initial configuration to determine the initial machining control benchmark and execution path; S42. During the blanking process, collect machining status data in real time through sensors and data acquisition units. The machining status data includes tool vibration information, cutting force information, workpiece surface temperature information, and machining position information, and form a machining status data stream; S43. Based on the machining status data stream, perform real-time quantitative analysis on the current machining deviation to generate a blanking error assessment dataset. The blanking error assessment dataset includes real-time dimensional deviation, cumulative error, and tool wear influence factor; S44. Based on the blanking error assessment dataset, adjust the blanking speed... The degree of machining error, tool feed rate, and blanking compensation are adjusted in a coordinated manner to generate an optimized control parameter set, so as to achieve dynamic correction and stable control of machining error; S45. Based on the optimized control parameter set and machining status data stream, abnormal states in the machining process are judged, and the abnormal types of equipment, materials, and machining fluctuations are identified respectively. Corresponding abnormal warning information is generated, and the abnormal warning information is associated with the corresponding control parameters; S46. After blanking is completed, the machining process data is summarized to generate a machining process record dataset, which includes the control parameter change trajectory of each component, machining error change trajectory, tool wear change information, and abnormal warning records.

[0027] Furthermore, step S43 of this application also includes: S43-1. Based on the processing status data, extract the actual blanking dimensions of each component. And calculate its relationship with the target blanking size. The deviation amount is used to obtain the real-time feeding error. : S43-2, Based on the aforementioned real-time feeding error Compared with the historical average processing error Calculate the cumulative error index : ;in, This represents the real-time error weight, with a value range of... S43-3, According to the aforementioned cumulative error index Combined with the tool usage time Cutting force With vibration amplitude Calculate the influence coefficient of tool wear. This error is then added to the overall material cutting error estimate. : ; Among them, weight Determined by historical production data and statistical analysis, meeting the requirements , These are the tool life threshold, the maximum allowable cutting force of the equipment, and the upper limit of vibration; S43-4, the comprehensive blanking error of each component. This data is associated and encapsulated with the corresponding processing status data to form a material feeding error assessment dataset. 。

[0028] Furthermore, step S44 of this application also includes: S44-1, For each component Based on the comprehensive material cutting error Calculate the initial material cutting compensation amount ;in, The compensation coefficient is determined using the least squares fitting method based on the proportional relationship between the error correction amount and the actual compensation amount of each component in historical production data. It is used to characterize the mapping strength from error to compensation amount. S44-2, Based on assembly sequence information and key airflow channel position constraints, the compensation amount and processing execution parameters of each component are collaboratively reconstructed to generate an optimized blanking compensation dataset. ;in, To improve material feeding speed, For tool feed rate, the sorting rule is based on the priority of key components and airflow channel constraints to ensure the consistency of assembly key dimensions and aerodynamic performance; the collaborative reconstruction process includes: using the assembly criticality sorting result as priority constraint, using the airflow channel continuity constraint as path constraint, and taking the minimum change of compensation amount of each component as the optimization objective, rearranging and matching the execution order and processing parameters of each component.

[0029] Specifically, after generating the blanking control parameters in step S3, step S4 is further executed. By inputting the target size control parameters and compensation control parameters into the control execution unit and acquiring processing status information in real time during execution, the control parameters are dynamically adjusted to achieve precision control and stable operation of the blanking process. In step S41, the blanking control parameters generated in step S3 are imported into the blanking equipment control system and initialized to determine the initial processing control benchmark and execution path. The blanking control parameters include the target blanking size, initial compensation amount, and blanking execution sequence for each component. By loading these parameters into the CNC blanking equipment, the equipment executes according to the optimized control strategy at the initial processing stage, thereby achieving unified initialization control of the blanking process, reducing manual setting errors, and improving processing consistency. In step S42, during the blanking process, sensors and data acquisition units deployed at key locations on the equipment collect processing status data in real time, forming a continuous processing status data stream. The processing status data includes tool vibration information, cutting force information, workpiece surface temperature information, and processing position information. For example, vibration sensors are used to obtain tool running stability, cutting force sensors reflect load changes, temperature sensors monitor the thermal effects of machining, and position detection devices acquire machining trajectory deviation information. This step enables real-time perception of the blanking process. In step S43, based on the machining status data stream, the current machining deviation is quantitatively analyzed in real time to generate a blanking error assessment dataset. This dataset includes real-time dimensional deviations, error accumulation trends, and tool wear influencing factors. Specifically, by comparing and analyzing the machining status data with the target size control parameters, and combining this with the changing trends during machining, the potential dimensional deviations under the current machining state are estimated, thus forming a multi-dimensional error information set. This step allows for early identification of error trends during machining, reducing the risk of error accumulation. In step S44, based on the blanking error assessment dataset, the blanking speed, tool feed rate, and blanking compensation amount are coordinated and adjusted to generate an optimized control parameter set, enabling dynamic correction of machining errors. Specifically, based on the error deviation direction and trend, machining parameters are adjusted in real time to gradually converge the machining dimensions towards the target value. For example, when a tendency for dimensions to be too large is detected, the feeding speed can be reduced and the feed rate adjusted, while the compensation amount is corrected, thereby achieving dynamic error suppression. This step forms a stable closed-loop control mechanism, improving machining accuracy and process stability. In step S45, based on the optimized control parameter set and machining status data stream, abnormal states during the machining process are determined, and abnormal warning information is generated. Specifically, this includes identifying abnormal equipment operating states, abnormal material states, and abnormal machining fluctuations, and associating the abnormality type with the current control parameters.For example, when an abnormal increase in vibration amplitude or a sudden change in cutting force is detected, an abnormal state of the equipment can be determined, triggering an early warning mechanism to intervene in the current processing. This step enables timely identification and response to abnormal situations, preventing further expansion of processing errors. In step S46, after material preparation, the data from the entire processing process is summarized to generate a processing process record dataset. This dataset includes the trajectory of control parameters for each component, the trajectory of processing error changes, tool wear information, and abnormal early warning records. By recording and organizing this data, the entire material preparation process can be traced, providing a basis for subsequent production optimization and quality analysis. For example, by analyzing the error change trajectory of key components in a batch, the cause of the error can be located and used for subsequent parameter adjustments. Through step S4 and its sub-steps, a complete control flow from processing execution, state perception, error assessment, dynamic adjustment to abnormal handling and data recording is realized, enabling the material preparation process to have real-time response and closed-loop adjustment capabilities, thereby effectively improving the processing accuracy and stability of multiple components and enhancing the controllability and traceability of the production process. Furthermore, based on the processing status data, the actual blanking dimensions of each component are extracted. And calculate its relationship with the target blanking size. The deviation amount is used to obtain the real-time feeding error. Based on the aforementioned real-time feeding error Compared with historical material feeding error data Calculate the cumulative error index ;in, This represents the real-time error weight, with a value range of... The ratio of the sampling frequency of the current processing status data to the update frequency of the historical error data is calculated to reflect the timeliness of the real-time data relative to the historical data. A higher real-time sampling frequency indicates that the current data can more accurately reflect changes in the processing status, thus improving the accuracy of the data. The value of is reduced when the sampling frequency is low or the data update is lagging. The value of [value] is determined to enhance the influence of historical data. Secondly, the stability of the current processing state is considered when determining [the value]. Adaptive correction is implemented. Specifically, by statistically analyzing the fluctuations in tool vibration, cutting force, and temperature, the system improves performance when machining conditions fluctuate significantly. The value of [value] makes the system more reliant on real-time errors for rapid adjustments; when the processing state is relatively stable, [the system] reduces [the error]. The value of is chosen to make error calculation smoother and avoid over-adjustment. Furthermore, based on historical production data... The value range is calibrated. Specifically, this is achieved by analyzing multiple batches of processing data to establish values ​​under different working conditions. The correspondence between the selected values ​​and the cutting accuracy was established, and the value that minimizes the overall cutting deviation was chosen. This serves as a reference range. During actual operation, adjustments are made dynamically within this range based on the real-time sampling frequency and processing status. For example, during the unloading of key components for the air wave machine, when the sensor sampling frequency is high and the tool vibration increases significantly, the system automatically increases... The value of [value] allows error calculation to focus more on real-time data, thus enabling rapid correction of processing parameters; when the equipment is running smoothly and the error changes slowly, [the value] is reduced. The value of this parameter makes the control process more stable. Based on the aforementioned cumulative error index... Combined with the tool usage time Cutting force With vibration amplitude Calculate the influence coefficient of tool wear. This error is then added to the overall material cutting error estimate. : ； Among them, weight Determined by historical production data and statistical analysis, meeting the requirements These are the tool life threshold, the maximum allowable cutting force of the equipment, and the upper limit of vibration; the comprehensive blanking error for each component is also considered. The corresponding processing status data is combined to form a data set for predicting material cutting errors: 。 Furthermore, for each component Based on the comprehensive material cutting error Calculate the initial material cutting compensation amount ;in, The compensation coefficient, determined using the least squares fitting method based on the proportional relationship between the error correction and actual compensation amounts of each component in historical production data, characterizes the mapping strength from error to compensation. Combined with the assembly sequence and the location of key airflow channels, the compensation sequence and material handling execution parameters for each component are adjusted to form an optimized material handling compensation dataset. ;in, To improve material feeding speed, For tool feed rate, the sorting rules are based on the priority of key components and airflow channel constraints to ensure the consistency of critical assembly dimensions and aerodynamic performance.

[0030] S5: Obtain the actual size detection data of each component, and compare and analyze the actual size detection data with the target size control parameters to generate blanking deviation data.

[0031] Specifically, the actual dimensions of each component after blanking are measured. This measurement can be performed using a coordinate measuring machine, laser measuring device, or online vision inspection system to obtain key dimensional data for each component, including length, width, thickness, and dimensions of key assembly parts. For example, for the guide vane and air chamber shell in a wave machine, high-precision measuring equipment can be used to obtain data on the relevant dimensions of the airflow channel and the gaps between connecting surfaces, ensuring that the measurement results accurately reflect the actual processing state of the component. This step provides the actual dimensional information of each component after blanking, providing basic data for subsequent deviation analysis. Subsequently, the measured actual dimensional detection data is compared and analyzed with the target dimensional control parameters determined in step S3 to calculate the actual blanking deviation of each component. Specifically, the key dimensions of each component are compared one by one to determine the degree of deviation relative to the target dimension, forming a corresponding deviation data set. For example, for the width of the airflow channel, a positive deviation is determined when the actual measured value is greater than the target dimension; a negative deviation is determined when the actual measured value is less than the target dimension. This process comprehensively reflects the dimensional deviation distribution of each component during the blanking process. Furthermore, the material cutting deviation data of each component are combined with assembly relationships and critical dimension chains for correlation analysis to identify the potential impact of deviations on the assembly process. Specifically, for components in the critical dimension chain, their dimensional deviations have a significant impact on the overall assembly accuracy and aerodynamic performance and should be given special attention; for non-critical components, a general assessment is conducted under the premise of meeting assembly constraints. Through this step, critical deviations can be identified and classified, providing a basis for subsequent control parameter adjustments. For example, during the assembly of the airwave machine, when the actual dimensional deviation of the guide vane is detected to exceed the preset range, the above comparative analysis can determine the degree of impact of this deviation on the airflow channel size and mark it as critical deviation data for subsequent compensation strategy optimization. Through the above steps, the actual material cutting dimensions of each component are detected and deviations are calculated, transforming the material cutting results from qualitative judgment to quantitative evaluation. This not only accurately reflects the processing accuracy but also provides a reliable basis for the control parameter correction in step S6, thereby improving the accuracy consistency and aerodynamic performance stability of multi-component assembly.

[0032] S6: Based on the material feeding deviation data, the target size control parameters and compensation control parameters are corrected, and the correction results are fed back to steps S3 and S4 to form a closed-loop control.

[0033] Specifically, the actual cutting deviations of each component obtained in step S5 are first organized and structured to form a deviation dataset. This dataset includes component identification, critical dimension type, actual deviation value, and assembly association information, and is correlated with the assembly criticality and critical dimension chain obtained in step S1. By mapping the deviation data to the critical dimension chain structure, the position of each component's deviation in the assembly system and its influence path are clarified, providing a foundation for subsequent analysis. Secondly, based on the correspondence between the deviation dataset and the critical dimension chain, the propagation path of the deviation during the assembly process is analyzed. Specifically, based on the positional relationship of the components in the critical dimension chain, the direction of deviation transmission and the scope of influence are determined: the deviation of a component at the front of the dimension chain can be transmitted to multiple subsequent components and produce a cumulative effect; the deviation of a component at the end of the dimension chain mainly affects local assembly gaps. This method can identify deviation sources with a large impact on the overall assembly accuracy. Further, the deviations of each component are weighted and sorted in conjunction with the assembly criticality. Specifically, the assembly criticality is used as the priority evaluation criterion to sort the deviation data, so that components with high assembly criticality and large deviations are preferentially identified as critical deviation components. Simultaneously, considering the spatial position of components within the airflow channel, aerodynamic performance impact analysis is performed on deviations. For components located in the main airflow path, guide structures, or critical cross-sections, deviations exceeding preset thresholds are identified as critical deviations significantly impacting aerodynamic performance. This comprehensive assessment generates a list of critical deviation components to guide subsequent parameter adjustments. After identifying critical deviations, the material handling control parameters are adaptively corrected based on the deviation data and assembly criticality. Specifically, for components with high assembly criticality and large deviations, their compensation control parameters are adjusted first, and the target size control range is simultaneously tightened to improve machining accuracy. For components with low assembly criticality, their compensation amounts are coordinated and adjusted while satisfying the overall dimensional chain closure constraint, thus achieving a unified approach to critical component optimization and overall assembly balance control. Furthermore, during parameter correction, related components are adjusted in conjunction with the assembly sequence and airflow channel positional relationships. When a critical component's dimensional correction might affect subsequent assembly, compensation amounts are simultaneously fine-tuned for components with assembly coupling relationships to avoid amplifying overall errors due to local optimization, achieving collaborative optimization control among multiple components. Furthermore, during multi-batch production, deviation data from consecutive batches are compared and analyzed to identify systematic and random errors. Specifically, when the deviation direction of the same component is consistent and the amplitude of change is stable in multiple consecutive batches, it is determined to be a systematic error, and its compensation amount is gradually adjusted according to preset rules; when the deviation exhibits irregular fluctuations, it is determined to be a random error, which is only recorded without significant adjustments to maintain the stability of control parameters.This method allows the control parameters to gradually converge to a stable range during multiple batches of operation. After completing the above parameter correction, the updated target size control parameters and compensation control parameters are fed back to step S3 for target size calculation and compensation allocation in subsequent batches. Simultaneously, they are fed back to step S4 for dynamic control adjustments during real-time processing, thus forming a closed-loop control path covering "parameter generation—execution control—deviation feedback—parameter correction." For example, during the assembly of the airflow guide channel in a wave machine, when a dimensional deviation in the same direction is detected in consecutive batches of the guide plate, the system determines it as a systematic error and gradually increases its compensation. Simultaneously, considering the guide plate's critical position in the main airflow channel, the compensation of its adjacent air chamber shells is synchronously fine-tuned to maintain consistent overall airflow channel dimensions, thereby ensuring stable aerodynamic performance. By implementing step S6, structured analysis and adaptive correction of the material cutting deviation of multiple components are realized, enabling the material cutting control parameters to be continuously optimized and gradually converged in multiple batch production processes. This effectively reduces systematic errors and error accumulation, improves the assembly accuracy, consistency and aerodynamic performance stability of multiple components, and enhances the adaptive adjustment capability and production reliability of the material cutting process.

[0034] S7: Based on the assembly sequence and the spatial relationship of each component in the airflow channel, the control execution sequence and compensation control parameters are optimized in a coordinated manner to ensure the consistency of key dimensions and aerodynamic performance.

[0035] Furthermore, step S7 of this application also includes: S71. Based on the assembly sequence and airflow channel layout information of each component of the airwave machine, a criticality ranking analysis is performed on the set of components involved in the assembly, and a priority list of critical components is constructed; wherein, the priority of critical components is determined by the assembly criticality. Influence coefficient of airflow channel position Normalized weighted calculation yields: ;in, The structural weight coefficient is determined based on the variance ratio of dimensional sensitivity and aerodynamic influence coefficient in the critical dimension chain; S72, based on the priority list of key components and the component assembly relationship matrix, the execution order of multi-component material cutting is optimized and adjusted to generate an optimized material cutting order dataset; wherein, the optimization process takes the priority execution of key components as a constraint, the satisfaction of assembly interference relationship as a boundary condition, and the minimum number of adjacent component assembly order switching as the optimization objective, and rearranges the component execution order to generate the final optimized material cutting compensation dataset; S73, based on the optimized material cutting order dataset, the material cutting compensation amount of each component is collaboratively allocated to generate the final optimized material cutting compensation dataset. The final compensation amount of each component The calculation formula is: ;in, This is the initial compensation amount. This is the influence coefficient of the airflow channel position. and The weighting coefficients are obtained through statistical regression analysis of historical production data and are used to achieve priority compensation for key components and overall consistency of aerodynamic performance.

[0036] Specifically, based on the assembly sequence and airflow channel layout information of each component of the airwave machine, a criticality ranking analysis is performed on the set of components involved in the assembly to construct a priority list of key components. Specifically, the assembly criticality of each component obtained in step S1 is extracted, and combined with the spatial position of each component in the airflow channel and its influence on airflow performance, an airflow channel position influence coefficient is calculated. Based on this, the assembly criticality and airflow channel position influence coefficient are normalized, and the priority index of each component is calculated in a weighted manner, thereby forming a priority list of key components. The priority of key components is determined by the assembly criticality... Influence coefficient of airflow channel position The result is obtained by performing normalized weighted calculation: ;in, The structural weight coefficient is determined based on the variance ratio of dimensional sensitivity and aerodynamic influence coefficient in the critical dimension chain. This step prioritizes components that significantly impact assembly accuracy and are located in critical airflow positions for subsequent control, improving the processing and assembly assurance capabilities of key components. Secondly, based on the critical component priority list and component assembly relationship matrix, the execution order of multi-component material cutting is optimized, generating an optimized material cutting order dataset. Specifically, while ensuring the priority execution of key components, the original material cutting order is rearranged based on the connection constraints and potential interference relationships between components in the assembly relationship matrix. Simultaneously, during the order adjustment process, the execution path is optimized to reduce the number of times adjacent component assembly order switches are switched, making the material cutting process more continuous and stable. This step improves the execution efficiency of the processing process while ensuring assembly rationality and reduces the risk of assembly interference caused by unreasonable order. Based on the optimized material cutting order, the material cutting compensation amount for each component is coordinated and allocated. During the coordination process, the criticality of component assembly, the location of airflow channels, and historical material cutting deviations are comprehensively considered. The compensation amounts for critical components are appropriately adjusted to prioritize these values ​​to ensure critical dimensional accuracy and overall aerodynamic performance. Simultaneously, compensation amounts for non-critical components are rationally allocated based on their assembly relationships and historical deviations to maintain overall assembly consistency and processing efficiency. For example, in the assembly of deflectors, if the deflector dimensional deviation significantly impacts airflow channel resistance, its material cutting compensation is prioritized, while adjacent support plates are fine-tuned according to their assembly relationships to ensure the geometric accuracy of the entire airflow channel remains stable. Finally, the final optimized material cutting compensation dataset is generated using this coordination and allocation method. The final compensation amount of each component The calculation formula is: ； in, This is the initial compensation amount. For the criticality of component assembly. The influence coefficient of airflow channel position and The weighting coefficient is used to achieve priority compensation for critical components and overall consistency in aerodynamic performance. For example, for similar components, the historical average deviation data of the same material, thickness, and size in previous production can be used as a benchmark for the initial compensation amount. For new components, a reasonable initial compensation amount can be calculated based on the dimensions marked on the design drawings and the machining tolerance range, combined with the tool size, cutting depth, and blanking method, thus providing a basic adjustment value before blanking begins. Component assembly criticality is a quantitative description of the importance of a component in the overall assembly accuracy and aerodynamic performance. The acquisition method includes: judging the contribution of each component to the critical dimensions based on the critical dimension chain analyzed in step S1, such as the deviation of dimensions of airflow channels, sealing surfaces, and positioning holes, which directly affects assembly accuracy. Prioritize each critical dimension and assign higher assembly criticality values ​​to components with greater impact. Refer to historical assembly feedback data, such as the average contribution of a component's dimensional deviation to the overall assembly deviation, as a reference for criticality weighting. The airflow channel position influence coefficient is used to describe the degree of influence of the component's position in the airflow channel on the overall aerodynamic performance. Let the component... The index that occupies a key position in the airflow channel is The coefficient of influence of flow contribution or resistance is The influence coefficient of the airflow channel position can be expressed as: ,in, For components The location weight is determined by assigning higher values ​​(e.g., 1-10) to critical locations and lower values ​​(e.g., 1-2) to non-critical locations, based on design drawings and experience. For components The quantification of the impact on airflow can be expressed as local flow resistance, pressure change, or flow rate percentage, where N is the total number of components within the airflow channel; the denominator is used for normalization to ensure that the values ​​of all components are equal. The sum is 1, which facilitates combination with assembly criticality weights. (Obtain) Based on the airflow design drawings, the spatial position of each component in the airflow channel is determined; components in critical positions (such as guide vanes and connecting nodes) are assigned higher positional weights, while non-critical positions are assigned lower weights; empirical data, such as the impact of local pressure or drag changes on overall aerodynamic performance during assembly history, can be incorporated. This can be obtained by calculating the local flow rate percentage, local pressure difference, or local resistance coefficient. (This applies to all components.) Accumulation is used as the denominator to achieve normalization and ensure... This facilitates combination with assembly criticality weighting. The weighting coefficient is used to coordinate the contribution ratio of assembly criticality and airflow channel location to the compensation amount. Let the priority quantification value of critical components be... The quantification value of airflow influence is Then the weighting coefficient and It can be calculated as follows: ,in, For components The assembly criticality value can be calculated using the critical dimension chain and assembly relationship matrix (e.g., number of critical dimensions × priority coefficient). For components The quantitative value of the impact on airflow performance can be obtained through airflow channel analysis (e.g., airflow channel proportion × flow resistance coefficient). This ensures weight normalization and can be directly used to guide the material cutting equipment in performing processing operations. By prioritizing compensation for key components and coordinating overall allocation, not only is the accuracy of critical assembly dimensions ensured, but overall aerodynamic performance consistency is also achieved, while reducing the impact of cumulative errors in multi-component processing on the overall machine performance. Based on the optimized material cutting sequence dataset, the material cutting compensation amount for each component is collaboratively allocated to generate the final optimized material cutting compensation dataset. Specifically, based on the initial compensation amount, the compensation amount is adjusted in conjunction with the assembly criticality of each component and the influence coefficient of the airflow channel position, so that key components can obtain higher precision compensation control, while taking into account the overall dimensional consistency of the airflow channel. In the process of compensation amount allocation, statistical analysis of historical production data is used to determine the weight relationship between assembly criticality and airflow influence in compensation allocation, so that the compensation strategy is more in line with actual production laws. In practical applications, such as in the airflow channel structure of a wave machine, the guide vane is usually located in the main airflow path. Its assembly is critical and has a significant impact on airflow distribution, therefore it holds a high priority position and is processed first in the material cutting sequence, while also receiving a high weight in the compensation allocation. For components located in non-critical connection positions, secondary compensation is performed under the premise of meeting assembly constraints, thereby achieving coordinated optimization of the overall structure and aerodynamic performance. Through the implementation of step S7, the assembly sequence optimization and compensation control strategy are designed collaboratively, transforming the multi-component material cutting process from single-parameter optimization to a process of coordinated optimization of sequence and parameters. This not only ensures the accuracy consistency of the critical dimension chain but also effectively improves the overall aerodynamic performance stability of the airflow channel, while reducing the risk of assembly interference and improving the continuity and efficiency of the production process.

[0037] S8: Based on the processing cycle time and real-time processing status, the control execution parameters are adaptively adjusted to achieve processing cycle time matching and efficiency optimization.

[0038] Specifically, the processing cycle time information of the current material feeding process is obtained, including the single-piece processing time of each component, the waiting time between processes, and the overall production cycle time requirements. Simultaneously, combined with the processing status data collected in real-time in step S4, the current equipment operating status is analyzed. This processing status includes tool running stability, equipment load, processing temperature changes, and feed execution status. Through comprehensive analysis of the processing cycle time and real-time processing status, it is determined whether there is a cycle time deviation or efficiency bottleneck in the current processing process. Based on this, the control execution parameters are adaptively adjusted. Specifically, when a slow processing cycle time or low equipment load is detected, the material feeding speed and tool feed rate are appropriately increased to shorten the processing cycle, while meeting the processing accuracy requirements. When excessive equipment load, increased tool vibration, or abnormally high processing temperature is detected, the material feeding speed is correspondingly reduced or the feed parameters are adjusted to ensure processing stability and prevent a decline in processing quality. Furthermore, during parameter adjustment, differentiated control strategies are adopted for critical and non-critical components based on their assembly criticality. This prioritizes ensuring processing accuracy for critical components, while optimizing processing efficiency for non-critical components within permissible limits. Furthermore, during continuous multi-component processing, the control parameters of subsequent components are pre-adjusted based on the actual processing time and error variations of preceding components. For example, if the processing time of a critical preceding component is detected to be long and the equipment is stable, the processing speed of subsequent non-critical components can be appropriately increased to achieve overall cycle time balance; if processing fluctuations are detected in preceding components, conservative parameter settings are maintained for subsequent components to avoid further error accumulation. This method achieves coordinated cycle time adjustment during multi-component processing. For instance, during the continuous feeding process of the airflow guide plate and air chamber housing of the air wave machine, if the system detects a low equipment load and good tool condition, the feeding speed of the air chamber housing can be appropriately increased to improve overall production efficiency; while when processing the guide plate, since it is located in a critical airflow channel, relatively stable processing parameters are maintained to ensure its dimensional accuracy, thus balancing efficiency and accuracy requirements. By implementing this step, dynamic matching between processing cycle time and control execution parameters is achieved, enabling the material feeding process to adaptively adjust according to the real-time processing status. Under the premise of ensuring the processing accuracy and assembly consistency of key components, production efficiency is effectively improved, equipment load fluctuations are reduced, and the stability and continuity of the overall processing process are enhanced.

[0039] Example 2 Based on the aforementioned adaptive control method for feeding parameters in the assembly of a wave machine, and using the same inventive concept, this application also provides an adaptive control system for feeding parameters in the assembly of a wave machine. Please refer to the appendix. Figure 2 The system includes: Assembly criticality analysis module 11 is used to obtain the assembly structure and dimensional requirements of each component of the air wave machine, construct the assembly relationship of the components and the critical dimension chain, and calculate the assembly criticality of each component based on the critical dimension chain. The status data acquisition and processing module 12 is used to collect raw material size information, processing equipment operating status information, tool status information and environmental parameters, and to process the collected data in a unified manner to generate a material feeding status dataset. The control parameter generation module 13 is used to generate target size control parameters and compensation control parameters for each component based on the assembly criticality and the material cutting status dataset, combined with the assembly sequence and the distribution relationship of the airflow channel position. The execution control adjustment module 14 is used to input the target size control parameters and compensation control parameters into the control execution unit, and to acquire processing status information in real time during the execution process. Based on the processing status information, the target size control parameters and compensation control parameters are dynamically adjusted to achieve precision control of the blanking process. The dimension detection and analysis module 15 is used to acquire the actual dimension detection data of each component, and compare and analyze the actual dimension detection data with the target dimension control parameters to generate blanking deviation data; Deviation feedback correction module 16 is used to correct the target size control parameters and compensation control parameters based on the material feeding deviation data, and feed the correction results back to steps S3 and S4 to form closed-loop control; The sequence compensation optimization module 17 is used to coordinately optimize the control execution sequence and compensation control parameters based on the assembly sequence and the spatial position relationship of each component in the airflow channel, so as to ensure the consistency of key dimensions and aerodynamic performance. The cycle time adaptive adjustment module 18 is used to adaptively adjust the control execution parameters according to the processing cycle time and real-time processing status, so as to achieve cycle time matching and efficiency optimization in the processing process.

[0040] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Figure 1 The adaptive control method and specific example for blanking parameters of a wave machine assembly described in Embodiment 1 are also applicable to the adaptive control system for blanking parameters of a wave machine assembly in this embodiment. Through the foregoing detailed description of the adaptive control method for blanking parameters of a wave machine assembly, those skilled in the art can clearly understand the adaptive control system for blanking parameters of a wave machine assembly in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here. As for the system disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant details can be found in the method section.

[0041] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0042] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. An adaptive control method for feeding parameters in the assembly of a wave machine, characterized in that, The method includes: S1. Obtain the assembly structure and dimensional requirements of each component of the air wave machine, construct the assembly relationship of the components and the critical dimension chain, and calculate the assembly criticality of each component based on the critical dimension chain; S2. Collect raw material size information, processing equipment operating status information, tool status information and environmental parameters, and process the collected data in a unified manner to generate a material feeding status dataset; S3. Based on the assembly criticality and the material cutting status dataset, and combined with the assembly sequence and the distribution relationship of airflow channels, generate target size control parameters and compensation control parameters for each component. S4. Input the target size control parameters and compensation control parameters into the control execution unit, and acquire the processing status information in real time during the execution process. Adjust the target size control parameters and compensation control parameters dynamically according to the processing status information to achieve precision control of the blanking process. S5. Obtain the actual size detection data of each component, and compare and analyze the actual size detection data with the target size control parameters to generate blanking deviation data; S6. Based on the material feeding deviation data, the target size control parameters and compensation control parameters are corrected, and the correction results are fed back to steps S3 and S4 to form a closed-loop control. S7. Based on the assembly sequence and the spatial relationship of each component in the airflow channel, the control execution sequence and compensation control parameters are optimized in a coordinated manner to ensure the consistency of key dimensions and aerodynamic performance. S8. Based on the processing cycle time and real-time processing status, the control execution parameters are adaptively adjusted to achieve processing cycle time matching and efficiency optimization.

2. The adaptive control method for feeding parameters of a wave machine assembly according to claim 1, characterized in that, Obtain the assembly structure and dimensional requirements of each component of the airwave machine, construct the assembly relationships and critical dimension chains of the components, and calculate the assembly criticality of each component based on the critical dimension chains, including: S11. Obtain the design drawings of the air wave machine, and perform structural analysis on the design drawings of the air wave machine to extract the geometric dimension parameters, shape feature parameters and assembly positioning feature parameters of each component, and form a basic feature dataset of the components. S12. Based on the component basic feature dataset, establish an assembly relationship matrix between components. The assembly relationship matrix is ​​used to characterize the connection sequence, assembly constraint relationship and potential interference relationship of each component. S13. Based on the assembly relationship matrix and component basic feature dataset, identify the key components and corresponding key dimensions involved in the airflow channel formation and assembly cooperation, construct a key dimension chain, and prioritize the key dimensions according to their order of action and constraint relationship in the assembly process. The key dimension chain includes pneumatic channel dimensions, connection surface gaps, and assembly positioning feature dimensions. S14. Based on the critical dimension chain, combined with assembly clearance parameters, pneumatic channel layout features and connection method parameters, the influence of each component in the critical dimension chain is quantitatively calculated to obtain the assembly criticality of each component. S15. Based on the material thermal expansion characteristics parameters and the processing environment temperature and humidity parameters, the environmental impact correction is applied to each dimension in the critical dimension chain to generate a corrected critical dimension chain, and the assembly criticality is updated based on the corrected critical dimension chain.

3. The adaptive control method for feeding parameters in the assembly of a wave machine according to claim 2, characterized in that, Based on the critical dimension chain, and combined with assembly clearance parameters, pneumatic channel layout characteristics, and connection method parameters, the influence of each component in the critical dimension chain is quantitatively calculated to obtain the assembly criticality of each component, including: S14-1 Calculate dimensional sensitivity based on the geometric tolerance limit of each dimension in the critical dimension chain. The formula is: ； in, For the overall assembly accuracy F, the component dimensions are... The sensitivity coefficient is obtained through perturbation analysis using the assembly relationship matrix. The actual dimensional error range of the component under the current processing conditions is obtained statistically from the blanking status dataset. The design tolerance range for the component is determined by the design drawings; S14-2. Based on the aerodynamic channel structure, perform CFD simulation analysis of single-factor disturbances on each component, and calculate the aerodynamic influence coefficient of each component on airflow performance. The calculation formula is: ； in, To change only the first component while keeping the structural parameters of other components unchanged. i The change in airflow pressure caused by the change in the dimensional parameters of each component. For the corresponding number j The change in airflow pressure of each component under the same simulation conditions, where N is the total number of components; S14-3. Based on the assembly sequence and assembly relationship matrix, analyze the error propagation relationship between components and calculate the assembly sequence weight of each component. The calculation formula is as follows: ； in, For the first i The dimensional deviation of the first component is transmitted to the first... j The cumulative error generated by each component; S14-4, Based on size sensitivity Aerodynamic influence coefficient and assembly sequence weight Calculate the assembly criticality of each component. The formula is: ； in, For the first i The criticality of assembling each component These are the weighting coefficients for size sensitivity, aerodynamic effects, and assembly sequence, respectively.

4. The adaptive control method for feeding parameters of a wave machine assembly according to claim 3, characterized in that, Based on the assembly criticality and the material cutting status dataset, and combined with the assembly sequence and airflow channel position distribution relationship, target size control parameters and compensation control parameters for each component are generated, including: S31. Extract the assembly criticality and material cutting status data of each component, and associate and map the assembly criticality and material cutting status data with the assembly sequence and the location of critical airflow channels to construct a component control dataset; the component control dataset includes the basic geometric parameters of each component, assembly criticality sorting information, and assembly constraint relationship information. S32. Based on the basic geometric parameters and assembly constraints in the component control dataset, calculate the target size offset of each component, generate the initial target blanking size, and prioritize the correction of critical components according to the assembly criticality ranking. S33. Based on the tool wear parameters, processing equipment thermal deformation parameters, and processing environment temperature and humidity parameters during the blanking process, the initial target blanking size is corrected to generate a corrected target blanking size; S34. Based on the correspondence between the actual material cutting deviation of each component and the assembly criticality in historical production data, the sliding window statistical analysis method is used to calculate the average deviation and deviation fluctuation range of different criticality intervals, and adjust the material cutting compensation amount of each component accordingly to optimize the dimensional consistency and batch stability of critical components. S35. Based on the assembly sequence and the positional relationship of key airflow channels, construct the component error propagation sequence, and optimize and adjust the allocation sequence of material compensation amount with the minimum cumulative deviation of each component as the constraint. S36. Based on the corrected target blanking size and the optimized compensation amount allocation sequence, generate the final blanking control parameters. The blanking control parameters include the target blanking size of each component, the blanking compensation amount, and the blanking execution sequence, which are used to guide the blanking equipment to perform processing control.

5. The adaptive control method for feeding parameters of a wave machine assembly according to claim 4, characterized in that, The target size control parameters and compensation control parameters are input into the control execution unit, and processing status information is acquired in real time during execution. The target size control parameters and compensation control parameters are dynamically adjusted based on the processing status information to achieve precision control of the blanking process, including: S41. Import the feeding control parameters into the feeding equipment control system and complete the initialization configuration to determine the initial processing control benchmark and execution path; S42. During the material feeding process, the processing status data is collected in real time through sensors and data acquisition units. The processing status data includes tool vibration information, cutting force information, workpiece surface temperature information and processing position information, and forms a processing status data stream. S43. Based on the processing status data stream, perform real-time quantitative analysis on the current processing deviation to generate a blanking error assessment dataset, which includes real-time dimensional deviation, cumulative error, and tool wear influence factor. S44. Based on the material feeding error assessment dataset, the material feeding speed, tool feed rate and material feeding compensation amount are adjusted in a coordinated manner to generate an optimized control parameter set, so as to realize the dynamic correction and stable control of the processing error. S45. Based on the optimized control parameter set and processing status data stream, determine the abnormal state during the processing, identify the equipment abnormality type, material abnormality type and processing fluctuation abnormality type respectively, generate corresponding abnormality warning information, and associate and mark the abnormality warning information with the corresponding control parameters. S46. After the material is cut, summarize the processing data and generate a processing process record dataset. The processing process record dataset includes the control parameter change trajectory of each component, the processing error change trajectory, tool wear change information, and abnormal warning records.

6. The adaptive control method for feeding parameters of a wave machine assembly according to claim 5, characterized in that, Based on the machining status data stream, the current machining deviation is quantitatively analyzed in real time to generate a blanking error assessment dataset. This dataset includes real-time dimensional deviations, cumulative errors, and tool wear influencing factors, including: S43-1. Based on the processing status data, extract the actual blanking dimensions of each component. And calculate its relationship with the target blanking size. The deviation amount is used to obtain the real-time feeding error. : ； S43-2, Based on the aforementioned real-time feeding error Compared with the historical average processing error Calculate the cumulative error index : ； in, This represents the real-time error weight, with a value range of... ; S43-3, According to the cumulative error index Combined with the tool usage time Cutting force With vibration amplitude Calculate the influence coefficient of tool wear. This error is then added to the overall material cutting error estimate. : ； ； Among them, weight Determined by historical production data and statistical analysis, meeting the requirements , These are the tool life threshold, the maximum allowable cutting force of the equipment, and the upper limit of vibration, respectively. S43-4, Combine the overall cutting error of each component This data is associated and encapsulated with the corresponding processing status data to form a data set for evaluating material feeding errors. : 。 7. The adaptive control method for feeding parameters in the assembly of a wave machine according to claim 6, characterized in that, Based on the aforementioned blanking error assessment dataset, the blanking speed, tool feed rate, and blanking compensation amount are adjusted collaboratively to generate an optimized control parameter set, thereby achieving dynamic correction and stable control of machining errors, including: S44-1, For each component Based on the comprehensive material cutting error Calculate the initial material cutting compensation amount : ； in, The compensation coefficient is determined by the least squares fitting method based on the proportional relationship between the error correction amount and the actual compensation amount of each component in historical production data. It is used to characterize the mapping strength from error to compensation amount. S44-2. Based on assembly sequence information and key airflow channel position constraints, the compensation amounts and machining execution parameters of each component are collaboratively reconstructed to generate an optimized blanking compensation dataset. : ； in, To increase material feeding speed, For tool feed rate, the sorting rules are based on the priority of key components and airflow channel constraints to ensure the consistency of critical assembly dimensions and aerodynamic performance; The collaborative reconfiguration process includes: using the assembly criticality ranking result as a priority constraint, the airflow channel continuity constraint as a path constraint, and taking the minimum change in compensation amount of each component as the optimization objective, rearranging and matching the execution order and processing parameters of each component.

8. The adaptive control method for feeding parameters of a wave machine assembly according to claim 1, characterized in that, Based on the assembly sequence and the spatial relationship of each component in the airflow channel, the control execution sequence and compensation control parameters are collaboratively optimized to ensure consistency in critical dimensions and aerodynamic performance, including: S71. Based on the assembly sequence and airflow channel layout information of each component of the air wave machine, perform a criticality ranking analysis on the set of components involved in the assembly and construct a priority list of critical components. Among them, the priority of key components is determined by the assembly criticality. Influence coefficient of airflow channel position The result is obtained by performing normalized weighted calculation: ； in, The structural weighting coefficient is determined based on the variance ratio of size sensitivity to aerodynamic influence coefficient in the critical size chain. S72. Based on the priority list of key components and the component assembly relationship matrix, optimize and adjust the material cutting execution order of multiple components to generate an optimized material cutting order dataset. The optimization process uses the priority execution of key components as a constraint, the satisfaction of assembly interference relationship as a boundary condition, and the minimum number of switching times of assembly order of adjacent components as the optimization objective, and rearranges the execution order of components to generate the desired result. S73. Based on the optimized material cutting sequence dataset, collaboratively allocate the material cutting compensation amount for each component to generate the final optimized material cutting compensation dataset. The final compensation amount of each component The calculation formula is: ； in, This is the initial compensation amount. This is the influence coefficient of the airflow channel position. and The weighting coefficients are obtained through statistical regression analysis of historical production data and are used to achieve priority compensation for key components and overall consistency of aerodynamic performance.

9. An adaptive control system for feeding parameters in the assembly of a wave machine, characterized in that, The system is used to implement the adaptive control method for feeding parameters of a wave machine assembly as described in any one of claims 1 to 8, wherein the system comprises: The assembly criticality analysis module is used to obtain the assembly structure and dimensional requirements of each component of the air wave machine, construct the assembly relationship of the components and the critical dimension chain, and calculate the assembly criticality of each component based on the critical dimension chain. The status data acquisition and processing module is used to collect raw material size information, processing equipment operating status information, tool status information and environmental parameters, and to process the collected data in a unified manner to generate a material feeding status dataset. The control parameter generation module is used to generate target size control parameters and compensation control parameters for each component based on the assembly criticality and the material cutting status dataset, combined with the assembly sequence and the distribution relationship of airflow channels. The execution control adjustment module is used to input the target size control parameters and compensation control parameters into the control execution unit, and to acquire processing status information in real time during the execution process. Based on the processing status information, the target size control parameters and compensation control parameters are dynamically adjusted to achieve precision control of the blanking process. The dimension detection and analysis module is used to acquire the actual dimension detection data of each component, compare and analyze the actual dimension detection data with the target dimension control parameters, and generate blanking deviation data. The deviation feedback correction module is used to correct the target size control parameters and compensation control parameters based on the material feeding deviation data, and feed the correction results back to steps S3 and S4 to form a closed-loop control. The sequence compensation optimization module is used to coordinately optimize the control execution sequence and compensation control parameters based on the assembly sequence and the spatial position relationship of each component in the airflow channel, so as to ensure the consistency of key dimensions and aerodynamic performance. The cycle time adaptive adjustment module is used to adaptively adjust the control execution parameters according to the processing cycle time and real-time processing status, so as to achieve cycle time matching and efficiency optimization in the processing process.

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