High-precision mold forming method for improving manufacturing precision of super-large blade

Abstract

The invention relates to the technical field of precision manufacturing, and discloses a high-precision mold forming method for improving the manufacturing precision of an oversized blade, which comprises the following steps of: a, acquiring temperature, pressure and stress data in a blade forming environment by using a multi-sensor system in a mold forming process, the multi-sensor system comprises a temperature sensor, a pressure sensor and a stress sensor; b, transmitting the data acquired by the sensor to a block chain network through a data gateway, and filtering the sensor data by using a Kalman filter; and c, in the block chain network, carrying out Hash operation on the filtered data, and storing corresponding Hash values and metadata so as to ensure that the data cannot be tampered. Through the multi-sensor monitoring and block chain technology, high-precision and automatic manufacturing of the oversized blade is achieved, the production efficiency and the product quality are remarkably improved, fluctuation and errors are reduced, the percent of pass is increased, and the rejection rate is reduced.

Description

Technical Field

[0001] The present invention relates to the field of precision manufacturing technology, and in particular to a high-precision mold forming method for improving the manufacturing accuracy of super-large blades. Background Art

[0002] With the development of wind power generation and aerospace, oversized blades play a key role in these applications, and their manufacturing accuracy directly affects the operating efficiency and service life of the equipment. In order to meet the high-precision requirements for blades, mold forming processes are generally used in the existing technology, and various sensors are used to monitor and control parameters such as temperature, pressure, and stress during the forming process.

[0003] The existing mold forming process collects data through a single sensor and uses a local control system to adjust the process parameters to ensure the molding quality of the blade. These methods include the conventional application of temperature sensors and pressure sensors, as well as static stress analysis and manual quality control. However, most of these systems rely on local storage and processing, the data is easily interfered with, the monitoring and control methods are relatively simple, and quality control mainly relies on post-manual inspection.

[0004] The existing technology has the following shortcomings in the manufacturing of ultra-large blades: first, the single sensor monitoring method is difficult to fully reflect the changes in multi-dimensional parameters during the molding process, resulting in limited accuracy and efficiency; second, the local storage method cannot guarantee the integrity and security of the data, affecting the consistency of product quality control; finally, the quality control and adjustment methods that rely on manual work have slow response speeds and large errors, and cannot effectively improve product qualification rates and reduce scrap rates. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a high-precision mold forming method for improving the manufacturing accuracy of ultra-large blades, which solves the problems in the prior art of single monitoring means, insufficient data security, quality control relying on manual labor and slow response, and large errors in the manufacturing of ultra-large blades.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a high-precision mold forming method for improving the manufacturing accuracy of super-large blades, comprising the following steps:

[0007] a. During the mold forming process, a multi-sensor system is used to collect temperature, pressure and stress data in the blade forming environment, wherein the multi-sensor system includes a temperature sensor, a pressure sensor and a stress sensor;

[0008] b. Transmitting the data collected by the sensor to the blockchain network through a data gateway, and filtering the sensor data using a Kalman filter;

[0009] c. In the blockchain network, the filtered data is hashed and the corresponding hash value and metadata are stored to ensure that the data cannot be tampered with;

[0010] d. Use the finite element analysis model to simulate the stress distribution of the blade during the forming process, and consider the nonlinear elastic-plastic behavior of the material, wherein the stress-strain relationship in the finite element analysis is:

[0011] σ=E·ε+α·(ε-ε y ) n

[0012] Among them, σ is stress, E is elastic modulus, ε is strain, and ε y is the yield strain, α and n are the hardening parameters.

[0013] Preferably, the Kalman filter in step b comprises the following steps:

[0014] i) Prediction: Based on the state transfer matrix A and control input u k -1, predict the state vector X at the next moment k and covariance matrix;

[0015] ii) Update: Using the current observation data, update the state vector and covariance matrix according to the observation matrix H.

[0016] Preferably, the finite element analysis model includes coupled thermal-structural analysis, and the mathematical expression of the heat conduction analysis model is:

[0017]

[0018] Where ρ is the material density, c ρ is the specific heat capacity, T is the temperature, k is the thermal conductivity, and Q is the internal heat source term.

[0019] Preferably, the multi-sensor system includes a plurality of temperature sensors and pressure sensors, which are respectively deployed at key positions of the mold to monitor the temperature and pressure distribution during the molding process in real time, wherein the data of each temperature sensor is obtained by the formula:

[0020]

[0021] Processing, T0 is the initial temperature, is the heat change per unit time, m is the mass, and c is the specific heat capacity.

[0022] Preferably, the hash operation in step c uses the SHA-256 algorithm to generate a hash value of the data:

[0023] Hash=SHA-256(sensor_id+timestamp+value)

[0024] Among them, sensor_id is the unique identifier of the sensor, timestamp is the timestamp of the collected data, and value is the actual value of the sensor data.

[0025] Preferably, a smart contract for verifying data integrity is deployed in the blockchain network. The smart contract can automatically verify whether the hash value of each piece of data is consistent with the hash value stored in the blockchain, and trigger an alarm when the data is abnormal.

[0026] Preferably, the smart contract can automatically trigger complex event notifications based on multi-condition verification results of temperature, pressure and stress. When multiple parameters exceed preset thresholds at the same time, the smart contract will record abnormal data and send an alarm.

[0027] Preferably, the finite element analysis in step d also includes modal analysis and harmonic response analysis, wherein the equation of modal analysis is:

[0028] [K-ω 2 M]{u}={0}

[0029] Wherein, K is the stiffness matrix, M is the mass matrix, ω is the natural frequency, and u is the corresponding vibration mode vector. Preferably, the equation of the harmonic response analysis is:

[0030] K(ω)u(ω)=F(ω)

[0031] Where ω is the excitation frequency and F is the external load.

[0032] Preferably, high cycle fatigue analysis and low cycle fatigue analysis of the blade are also included, wherein the low cycle fatigue analysis is performed based on the following formula:

[0033] Δε=ε a (2N f ) b +ε b (2N f ) c

[0034] Where Δε is the strain range, ε a , ε b is the material constant, N f is the cycle life, b and c are the fatigue strength indexes.

[0035] The present invention provides a high-precision mold forming method for improving the manufacturing accuracy of super-large blades. It has the following beneficial effects:

[0036] 1. The present invention realizes all-round real-time control of the forming process through multi-sensor data acquisition and real-time monitoring, combined with thermal-structural coupling analysis and dynamic stress prediction, greatly reducing the fluctuation range of temperature, pressure and stress, thereby significantly improving the precision and production efficiency of ultra-large blade manufacturing.

[0037] 2. The present invention uses blockchain technology to store and verify sensor data, ensuring the integrity and non-tamperability of the data, and realizes automated quality control and exception handling through smart contracts, thereby improving the credibility of the data and ensuring the stability of the manufacturing process and the consistency of product quality.

[0038] 3. The present invention can detect and automatically respond to abnormal situations in the production process in real time through smart contracts, quickly adjust process parameters, significantly improve the product qualification rate, and greatly reduce the scrap rate, thereby improving the overall production quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a process flow chart of the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] Please refer to the attached Figure 1 The present invention relates to a high-precision mold forming method for improving the manufacturing accuracy of ultra-large blades. Specifically, it involves an overall solution that realizes high-precision molding and ensures product quality by integrating multiple advanced technical means such as multi-sensor data acquisition, blockchain data storage and verification, finite element analysis, and smart contract triggering to monitor and precisely control the manufacturing process of ultra-large blades in real time.

[0042] 1. Multi-sensor data acquisition system

[0043] In the blade manufacturing process, due to the complex geometry and material properties of the super-large blade, accurate control of the temperature, pressure and stress distribution during the molding process is the key to ensuring the quality of the blade. To this end, the present invention designs a multi-sensor data acquisition system for real-time monitoring of key process parameters during the molding process.

[0044] 1.1 Temperature Sensor

[0045] The present invention uses high-precision PT100 RTD temperature sensors, which are deployed in multiple key locations of the mold, including the mold interior, mold surface, and temperature-sensitive areas of the blade molding area. The temperature sensor outputs 4-20mA or 0-10V signals and is connected to the data gateway for real-time monitoring of temperature distribution during the molding process.

[0046] 1.2 Pressure Sensor

[0047] In the process of blade molding, it is crucial to accurately control the pressure during injection molding or die casting. The present invention uses a Kistler 6189A piezoelectric pressure sensor with an accuracy of ±0.5% full scale, which is deployed in the high-pressure area inside the mold and near the injection port to detect pressure changes when the material flows. These data can help accurately adjust the molding process parameters to avoid deformation or defects of the blade caused by excessive or low pressure.

[0048] 1.3 Stress / strain sensors

[0049] In order to monitor the stress distribution of the blade during cooling and curing, the present invention installs Micro-Measurements CEA series resistance strain gauges at key structural parts of the blade (such as the root and tip of the blade). Connected to the data gateway through the strain gauge amplifier, these sensors can capture the tiny strains generated by the blade during cooling to avoid stress concentration causing material cracking.

[0050] 2. Data processing and blockchain storage

[0051] 2.1 Data Gateway and Kalman Filter

[0052] The data gateway in the present invention is responsible for collecting real-time data from various sensors and performing preliminary processing on these data. The data processing process includes dynamically filtering the sensor data using a Kalman filter to eliminate noise and estimate the true state of the system. The state transition and observation equations of the Kalman filter are as follows:

[0053] x k =Ax k-1 +Bu k-1 +W k-1

[0054] z k =Hx k +v k

[0055] Among them, x k is the system state vector, A is the state transfer matrix, H is the observation matrix, W k-1 and v k are process noise and observation noise respectively.

[0056] The processed sensor data is transmitted to the blockchain network via the MQTT protocol. Each piece of data is hashed using the SHA-256 algorithm before being uploaded to generate the hash value of the data:

[0057] Hash=SHA-256(sensor_id+timestamp+value)

[0058] Among them, sensor_id is the unique identifier of the sensor, timestamp is the timestamp of the collected data, and value is the actual value of the sensor data.

[0059] The generated hash value and corresponding metadata (such as timestamp, sensor ID) are stored on the blockchain, ensuring the immutability and integrity of the data. Each node in the blockchain verifies and records data through the PBFT (Practical Byzantine Fault Tolerance) consensus algorithm to ensure the robustness and consistency of the system.

[0060] 3. Finite element analysis and simulation calculation

[0061] 3.1 Thermal-structural coupling analysis

[0062] The finite element analysis model in this invention combines heat conduction and structural stress analysis to accurately simulate the thermal stress distribution of the blade during the forming process. The basic equation of heat conduction is:

[0063]

[0064] Where ρ is the material density, c ρ is the specific heat capacity, T is the temperature, k is the thermal conductivity, and Q is the internal heat source term. The model takes into account the heat conduction at the contact surface between the mold and the material and the convection boundary conditions on the outer surface of the mold.

[0065] Based on heat conduction, thermal-structural coupling analysis is performed to solve the stress distribution caused by temperature changes. The stress-strain relationship is described using a nonlinear elastic-plastic model:

[0066] σ=E·ε+α·(ε-ε y ) n

[0067] Among them, σ is stress, E is elastic modulus, ε is strain, and ε y is the yield strain, α and n are the hardening parameters.

[0068] 3.2 Dynamic response and fatigue analysis

[0069] In addition to static stress analysis, the present invention also includes dynamic response analysis of the blade. The natural frequency and vibration mode of the blade are determined by modal analysis, and the core equation is:

[0070] [K-ω 2 M]{u}={0}

[0071] Among them, K is the stiffness matrix, M is the mass matrix, ω is the natural frequency, and u is the corresponding vibration mode vector.

[0072] On this basis, harmonic response analysis is performed to calculate the dynamic response of the blade under external excitation to ensure that the blade will not produce resonance or excessive vibration stress during operation. The equation for harmonic response analysis is:

[0073] K(ω)u(ω)=F(ω)

[0074] Where ω is the excitation frequency and F is the external load.

[0075] At the same time, fatigue analysis is performed to predict the fatigue life of the blade in long-term service. Fatigue analysis is divided into high-cycle fatigue and low-cycle fatigue. The strain-life relationship of low-cycle fatigue is:

[0076] Δε=ε a (2N f ) b +ε b (2N f ) c

[0077] Where Δε is the strain range, ε a , ε b is the material constant, N f is the cycle life, b and c are the fatigue strength indexes.

[0078] 4. Smart contracts and automated control

[0079] 4.1 Smart Contract Design

[0080] To achieve automated quality control, the present invention deploys smart contracts on the blockchain to automatically verify and process sensor data. When multiple sensor data exceed a predetermined threshold at the same time, the smart contract triggers an alarm and executes corresponding control measures.

[0081] For example, when the temperature sensor and pressure sensor simultaneously detect values ​​outside the normal range, the smart contract automatically triggers the "CriticalCondition" event and records the abnormal data on the blockchain. This automated process ensures that quality issues can be discovered and handled in the first place, thereby reducing scrap rates and potential safety risks.

[0082] 4.2 Data Integrity and Security

[0083] The Merkle tree structure is used in smart contracts to verify and store large-scale data, ensuring that sensor data will not be tampered with during transmission and storage. The root hash value of the Merkle tree is stored on the blockchain, ensuring the integrity and credibility of the data.

[0084] Example 1: Multi-sensor data collection and real-time monitoring

[0085] Specific steps:

[0086] Sensor Installation:

[0087] PT100 temperature sensors, Kistler 6189A pressure sensors and Micro-Measurements CEA series strain gauges are installed at key locations inside and outside the mold.

[0088] Temperature sensors are set on the inner and outer surfaces of the mold, pressure sensors are set on the injection port and the high-pressure area of ​​the mold cavity, and strain gauges are set on the root and tip of the blade.

[0089] Data collection:

[0090] The acquisition frequency of the temperature sensor is set to 1 Hz, the pressure sensor to 10 Hz, and the strain gauge to 5 Hz.

[0091] The data is transmitted to the Moxa UC-8100 data gateway via the 4-20mA and RS485 interfaces.

[0092] Data processing:

[0093] The Kalman filter is used to filter the collected data and eliminate noise.

[0094] The processed data is uploaded to the blockchain via the MQTT protocol.

[0095] Data Storage:

[0096] The data hash value is calculated using the SHA-256 algorithm and stored in the blockchain to ensure data integrity.

[0097] Process parameters:

[0098] Temperature control range: 80℃-200℃.

[0099] Pressure control range: 50-900bar.

[0100] Data transmission frequency: 1Hz (temperature), 10Hz (pressure), 5Hz (stress).

[0101] Comparative Example 1: Single Sensor Monitoring and Local Storage

[0102] Specific steps:

[0103] Sensor Installation:

[0104] A single temperature sensor is installed inside and outside the mold, and an RTD temperature sensor is used for temperature monitoring. Data collection:

[0105] The temperature data is transmitted to the local control system via a 4-20mA interface.

[0106] The data acquisition frequency was set to 1 Hz.

[0107] Data processing:

[0108] Without filtering, the collected data is directly transmitted to the local storage system.

[0109] Data Storage:

[0110] Temperature data is stored in a local database and is not encrypted or hashed.

[0111] Process parameters:

[0112] Temperature control range: 80℃-200℃.

[0113] Pressure and stress are not monitored.

[0114] Data transmission frequency: 1Hz.

[0115] Table 1:

[0116]

[0117]

[0118] Example 1 uses multi-sensor data collection and blockchain storage to monitor key parameters in the molding process in real time and ensure the integrity and security of the data. Compared with the single sensor monitoring method of Comparative Example 1, it significantly improves the control accuracy and reliability of the molding process.

[0119] Example 2: Thermal-Structural Coupling Analysis and Dynamic Stress Prediction

[0120] Specific steps:

[0121] Geometry Modeling:

[0122] The blade three-dimensional model was established using CAD software and imported into ANSYS for thermal-structural coupling analysis.

[0123] Heat transfer analysis:

[0124] Set material properties (ρ = 2700kg / m 3 , c ρ=896J / kg℃,k=167W / mK)

[0125] The heat conduction equation is used to calculate the temperature field distribution inside the mold.

[0126] Structural stress analysis:

[0127] The stress calculation is performed using a nonlinear elastic-plastic model with yield stress and hardening parameters set.

[0128] Consider the effect of temperature changes on the mechanical properties of materials and calculate thermal stress.

[0129] Dynamic stress prediction:

[0130] Combined with dynamic load analysis, the stress changes of the blades in actual operation are predicted.

[0131] Process parameters:

[0132] Temperature range: 80℃-200℃.

[0133] Material properties: density ρ, specific heat c ρ , thermal conductivity k.

[0134] Load type: temperature load, mechanical load.

[0135] Comparative Example 2: No thermal-structural coupling analysis, only static stress analysis

[0136] Specific steps:

[0137] Geometry Modeling:

[0138] The blade three-dimensional model was built using CAD software and imported into ANSYS for static stress analysis.

[0139] Static Stress Analysis:

[0140] Directly apply external mechanical loads and calculate static stresses.

[0141] The effect of temperature changes on structural stress is not considered.

[0142] Temperature influence:

[0143] No heat conduction analysis was performed and the effect of temperature field on material properties was ignored.

[0144] Process parameters: Material properties: only mechanical properties (elastic modulus E, Poisson's ratio v) are considered.

[0145] Load type: Mechanical load.

[0146] Table 2:

[0147] Parameters / Features Example 2 Comparative Example 2 Analysis Type Thermal-Structural Coupling Analysis Static Stress Analysis Temperature field considerations yes no Material Model Nonlinear elastoplastic model Linear elastic model Dynamic stress prediction Combined with dynamic load prediction Not conducted Stress calculation accuracy high Low Stress Concentration Prediction Can predict stress concentration areas Unable to accurately predict

[0148] Example 2 can more accurately simulate the stress distribution and dynamic changes of the blade during the molding process through thermal-structural coupling analysis and dynamic stress prediction, thereby improving the accuracy and reliability of stress calculation. However, the static analysis method of Example 2 cannot effectively consider the impact of the temperature field on material properties, and there are stress prediction errors.

[0149] Example 3: Smart Contract Automated Quality Control

[0150] Specific steps:

[0151] Smart contract design:

[0152] Write smart contracts that include data validation, anomaly detection, and automatic alarm functions.

[0153] Set normal ranges and thresholds for multi-sensor data (temperature, pressure, stress).

[0154] Smart contract deployment:

[0155] Deploy smart contracts on the Hyperledger Fabric blockchain network and execute them on each Peer node in the form of chain code.

[0156] Abnormal detection and alarm:

[0157] When it is detected that the sensor data exceeds the preset range, the smart contract automatically triggers an alarm and records the abnormal data.

[0158] The system sends abnormal information to the mobile device or computer of the relevant operator.

[0159] Data storage and verification:

[0160] The hash values ​​of all sensor data are stored in the blockchain, and the contract automatically verifies the integrity of the data.

[0161] Process parameters:

[0162] Temperature threshold: >200℃ alarm.

[0163] Pressure threshold: >900bar alarm.

[0164] Stress threshold: >300MPa alarm.

[0165] Comparative Example 3: Manual Monitoring and Manual Quality Control

[0166] Specific steps:

[0167] Manual monitoring:

[0168] The operator manually monitors the temperature and pressure data and determines abnormal situations based on experience.

[0169] Manual quality control:

[0170] When an abnormality is found, the operator manually records it and makes adjustments.

[0171] Data is recorded in a local database without encryption or authentication.

[0172] Alarm and processing:

[0173] There is no automatic alarm system, and the operator's judgment and handling are dependent on their experience.

[0174] Process parameters:

[0175] Temperature monitoring range: 80℃-200℃.

[0176] Pressure monitoring range: 50-900bar.

[0177] Stress Monitoring: No.

[0178] Table 3:

[0179] Parameters / Features Example 3 Comparative Example 3 Monitoring Type Smart contract automatic monitoring and alarm Manual monitoring Data Validation Automatic verification, blockchain storage No authentication, local storage Exception handling Automatic alarm and processing Manual judgment and processing Data Integrity Ensure immutability through blockchain Susceptible to interference, poor integrity Response speed Fast (real time) Slow (manually dependent)

[0180] Example 3 realizes automated monitoring and quality control of the molding process through smart contracts, can respond to abnormal situations in production in a timely manner, and significantly improves production efficiency and product consistency. The manual monitoring method of Example 3 relies on manual experience, has a slow response speed and is prone to errors.

[0181] Example 4: Multi-physics coupling analysis and fatigue life prediction

[0182] Specific steps:

[0183] Thermal-structural coupling analysis:

[0184] ANSYS software is used to couple the temperature field and stress field of the blade, and the nonlinear elastic-plastic material model is combined to simulate the stress distribution of the blade at different temperatures.

[0185] Modal Analysis:

[0186] Through modal analysis, the natural frequency and vibration mode of the blade are determined to avoid resonance problems in the design.

[0187] Fatigue analysis:

[0188] Based on high-cycle and low-cycle fatigue analysis, the strain-life (ε-N) curve is used to predict the fatigue life of the blade, considering the effect of cyclic load on the blade life.

[0189] Dynamic stress prediction:

[0190] Harmonic response analysis is used to simulate the stress changes of the blade in the actual working environment and predict the possible fatigue damage area process parameters:

[0191] Temperature range: 80℃-200℃.

[0192] Dynamic Loads: Simulate wind loads.

[0193] Fatigue analysis parameters: SN curve, strain-life curve.

[0194] Comparative Example 4: No dynamic stress analysis, only static fatigue analysis

[0195] Specific steps:

[0196] Static Stress Analysis:

[0197] ANSYS software is used to perform static stress analysis on the blade, apply mechanical loads, and calculate static stress.

[0198] Fatigue life prediction:

[0199] Based only on the static stress distribution and S

[0200] -N curve is used to predict fatigue life, ignoring the influence of dynamic stress changes.

[0201] Process parameters:

[0202] Static Load: Simulates static wind loads.

[0203] Fatigue analysis parameters: SN curve.

[0204] Table 4:

[0205]

[0206]

[0207] Example 4 can accurately predict the fatigue life and failure mode of the blade in actual work through multi-physical field coupling analysis and dynamic stress prediction, while Comparative Example 4 is only based on static analysis and cannot accurately reflect the stress changes of the blade in a dynamic environment, resulting in a large prediction error.

[0208] Example 5: Integration of data integrity assurance and automated control

[0209] Specific steps:

[0210] Data collection and processing:

[0211] A multi-sensor system is used to collect temperature, pressure and stress data in real time. After the data is processed by Kalman filtering, it is uploaded to the blockchain through the MQTT protocol.

[0212] Data storage and verification:

[0213] The data is hashed using the SHA-256 algorithm, and the hash value and metadata are stored in the blockchain. The system automatically verifies the integrity of the data.

[0214] Smart contract trigger:

[0215] Smart contracts automatically detect anomalies based on sensor data. When temperature, pressure or stress exceeds the preset range, the contract automatically triggers an alarm and records the anomaly.

[0216] Automation control:

[0217] The system automatically adjusts the molding process parameters according to the instructions of the smart contract, such as adjusting the injection pressure or heating temperature, to ensure the quality of blade molding.

[0218] Process parameters:

[0219] Temperature threshold: >200℃ alarm.

[0220] Pressure threshold: >900bar alarm.

[0221] Stress threshold: >300MPa alarm.

[0222] Comparative Example 5: No data verification and manual control

[0223] Specific steps:

[0224] Data collection and processing:

[0225] A single temperature sensor monitors the temperature and the data is transmitted directly to the local storage system without filtering.

[0226] Data Storage:

[0227] The data is stored in a local database and is not hashed or verified.

[0228] Abnormal monitoring:

[0229] Operators monitor temperature manually and are unable to detect abnormal conditions of pressure and stress in real time.

[0230] Manual control:

[0231] The process parameters are manually adjusted by the operator and automatic control cannot be achieved.

[0232] Process parameters:

[0233] Temperature monitoring range: 80℃-200℃.

[0234] Pressure and stress monitoring: None.

[0235] Table 5:

[0236]

[0237] Example 5 integrates data integrity assurance and automated control to detect anomalies in the production process in real time and automatically adjust process parameters, significantly improving production efficiency and product quality. However, the manual control method of Example 5 has problems such as slow response speed and incomplete data.

[0238] Comparative Experimental Design

[0239] In order to further verify the advantages of the present invention, a comparative experiment was designed to compare the performance of Example 5 and Comparative Example 5 in actual production. The experiment applied the two methods on the same production line, and recorded the key data in the production process and the quality of the finished product.

[0240] Comparative experimental steps:

[0241] Subjects:

[0242] Production line A uses the method of Example 5 to produce 100 super-large blades.

[0243] Production line B uses the method of comparative example 5 to produce 100 super-large blades.

[0244] Experimental data collection:

[0245] Record real-time data of temperature, pressure and stress during the production process.

[0246] Monitor exception occurrences, response times, and adjustments.

[0247] Record the qualified rate and scrap rate of each batch of products.

[0248] Experimental data analysis:

[0249] Compare the temperature, pressure and stress fluctuations of the two production lines.

[0250] Analyze the abnormal response speed and the effect of process parameter adjustment.

[0251] Statistics on finished product qualification rate and scrap rate, and analysis of production efficiency.

[0252] Experimental data table

[0253]

[0254]

[0255] It can be seen from the experimental data that production line A (Example 5) shows a smaller fluctuation range in the control of temperature, pressure and stress, indicating that its process parameter control is more accurate. At the same time, the abnormal response time of production line A is significantly shorter than that of production line B, and the process parameters can be automatically adjusted to restore the normal state, showing obvious intelligence and efficiency. In addition, the finished product qualification rate of production line A is as high as 98%, and the scrap rate is only 2%, which is significantly superior to the 90% qualification rate and 10% scrap rate of production line B (Comparative Example 5).

[0256] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A high-precision mold forming method for improving the manufacturing accuracy of ultra-large blades, characterized in that: The following steps are involved: a. During the mold forming process, a multi-sensor system is used to collect temperature, pressure and stress data in the blade forming environment, wherein the multi-sensor system includes a temperature sensor, a pressure sensor and a stress sensor; b. Transmitting the data collected by the sensor to the blockchain network through a data gateway, and filtering the sensor data using a Kalman filter; c. In the blockchain network, the filtered data is hashed and the corresponding hash value and metadata are stored to ensure that the data cannot be tampered with; d. Use the finite element analysis model to simulate the stress distribution of the blade during the forming process, and consider the nonlinear elastic-plastic behavior of the material, wherein the stress-strain relationship in the finite element analysis is: σ=E·ε+α·(ε-ε y ) n Among them, σ is stress, E is elastic modulus, ε is strain, and ε y is the yield strain, α and n are the hardening parameters.

2. The high-precision mold forming method for improving the manufacturing accuracy of super-large blades according to claim 1 is characterized in that: The Kalman filter in step b comprises the following steps: i) Prediction: Based on the state transfer matrix A and control input u k -1, predict the state vector X at the next moment k and covariance matrix; ii) Update: Using the current observation data, update the state vector and covariance matrix according to the observation matrix H.

3. The high-precision mold forming method for improving the manufacturing accuracy of super-large blades according to claim 1 is characterized in that: The finite element analysis model includes coupled thermal-structural analysis, and the mathematical expression of the heat conduction analysis model is: Where ρ is the material density, c ρ is the specific heat capacity, T is the temperature, k is the thermal conductivity, and Q is the internal heat source term.

4. The high-precision mold forming method for improving the manufacturing accuracy of super-large blades according to claim 1 is characterized in that: The multi-sensor system includes multiple temperature sensors and pressure sensors, which are respectively deployed at key positions of the mold to monitor the temperature and pressure distribution during the molding process in real time, wherein the data of each temperature sensor is obtained by the formula: Processing, T0 is the initial temperature, is the heat change per unit time, m is the mass, and c is the specific heat capacity.

5. The high-precision mold forming method for improving the manufacturing accuracy of super-large blades according to claim 1 is characterized in that: The hash operation in step c uses the SHA-256 algorithm to generate a hash value of the data: Hash=SHA-256(sensor_id+timestamp+value) Among them, sensor_id is the unique identifier of the sensor, timestamp is the timestamp of the collected data, and value is the actual value of the sensor data.

6. The high-precision mold forming method for improving the manufacturing accuracy of super-large blades according to claim 1 is characterized in that: A smart contract for verifying data integrity is deployed in the blockchain network. The smart contract can automatically verify whether the hash value of each piece of data is consistent with the hash value stored in the blockchain, and trigger an alarm when the data is abnormal.

7. The high-precision mold forming method for improving the manufacturing accuracy of super-large blades according to claim 6 is characterized in that: The smart contract can automatically trigger complex event notifications based on multi-condition verification results of temperature, pressure and stress. When multiple parameters exceed the preset thresholds at the same time, the smart contract will record abnormal data and send an alarm.

8. The high-precision mold forming method for improving the manufacturing accuracy of super-large blades according to claim 1 is characterized in that: The finite element analysis in step d also includes modal analysis and harmonic response analysis, wherein the equation of modal analysis is: [K-ω 2 M]{u}={0} in, K is the stiffness matrix, M is the mass matrix, ω is the natural frequency, and u is the corresponding vibration mode vector.

9. The high-precision mold forming method for improving the manufacturing accuracy of super-large blades according to claim 8, characterized in that: The equation for the harmonic response analysis is: K(ω)u(ω)=F(ω) Where ω is the excitation frequency and F is the external load.

10. The high-precision mold forming method for improving the manufacturing accuracy of super-large blades according to claim 1, characterized in that: It also includes high-cycle fatigue analysis and low-cycle fatigue analysis of the blades, where the low-cycle fatigue analysis is based on the following formula: No = yes a (2N f ) b +e b (2N f ) c Where Δε is the strain range, ε a , ε b is the material constant, N f is the cycle life, b and c are the fatigue strength indexes.

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