High-precision complex curved surface machining method

Abstract

The invention relates to the technical field of high-precision machining, and discloses a high-precision complex curved surface machining method which comprises the following steps: S1, designing a three-dimensional model of a complex curved surface by using computer aided design software, and carrying out detailed structural analysis on the model to ensure design feasibility and structural stability; s2, adopting computer-aided manufacturing software, generating an accurate tool path based on an optimization algorithm, and performing iterative optimization on the path to improve the processing efficiency and reduce the resource consumption; and S3, deploying a real-time data monitoring system on the numerical control machine tool, and adjusting machining parameters through an adaptive technology to ensure high precision and high efficiency of the machining process. Through computer aided design and manufacturing software, particle swarm optimization, real-time monitoring and a machine learning model, the precision and efficiency of complex curved surface machining are improved, the machining time is shortened, tool abrasion is reduced, consistency is ensured, deviation is corrected in time, and the product quality and production efficiency are improved.

Description

Technical Field

[0001] The invention relates to the technical field of high-precision mechanical processing, and in particular to a high-precision complex curved surface processing method. Background Art

[0002] Complex surfaces are widely used in high-end manufacturing fields such as aerospace, automobile manufacturing, medical devices and consumer electronics. Due to the complex and changeable geometric shapes of complex surfaces, high precision and high finish are required, which poses a huge challenge to traditional processing technology. High-precision complex surfaces usually include free-form surfaces and parametric surfaces, which have the characteristics of non-uniform and orderly changes, and require strict control of surface quality and dimensional accuracy during the processing process.

[0003] At present, the processing of complex surfaces mainly relies on CNC machine tools and computer-aided design and manufacturing technologies. These technologies generate CNC programs to control the motion trajectory and cutting parameters of machine tools to achieve precise processing of complex surfaces. Common methods include multi-axis milling, multi-point machining and electrospark machining. These methods can achieve high-precision processing of complex surfaces to a certain extent, but due to the limitations of path planning and parameter settings, multiple trials and adjustments are often required to achieve the desired effect, which is inefficient.

[0004] Although the existing complex surface processing technology has made some progress, there are still some shortcomings. First, the traditional path planning and parameter setting methods rely on experience, which makes it difficult to ensure the stability and consistency of the processing process. Secondly, the lack of real-time monitoring and dynamic adjustment makes it impossible to timely detect and correct deviations in the processing process, affecting product quality. In addition, the lack of effective data analysis and feedback mechanism makes it difficult to extract valuable information from a large amount of processing data and optimize the processing technology. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a high-precision complex surface processing method. The present invention significantly improves the precision and efficiency of complex surface processing, reduces processing time and tool wear, ensures processing consistency, corrects processing deviations in time, and improves product quality and production efficiency through computer-aided design and manufacturing software, particle swarm optimization algorithm, real-time data monitoring and machine learning model.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A high-precision complex curved surface processing method, comprising the following steps:

[0007] S1. Use computer-aided design software to design three-dimensional models of complex surfaces and conduct detailed structural analysis of the models to ensure the feasibility of the design and the stability of the structure;

[0008] S2. Use computer-aided manufacturing software to generate accurate tool paths based on optimization algorithms and iteratively optimize the paths to improve processing efficiency and reduce resource consumption;

[0009] S3. Deploy a real-time data monitoring system on CNC machine tools and adjust processing parameters through adaptive technology to ensure high precision and high efficiency of the processing process;

[0010] S4. Implement machining operations and track machining status, such as cutting force and temperature, in real time through advanced monitoring systems to dynamically optimize the machining process;

[0011] S5. Use online quality control system to conduct real-time dimension inspection, and use high-precision scanning equipment and software tools to compare actual dimensions with design models to ensure that products meet quality standards;

[0012] S6. After processing, detailed post-processing steps such as grinding and polishing, as well as final dimensional verification using advanced measurement technology, are carried out to ensure that the product achieves the highest surface quality and dimensional accuracy;

[0013] S7. Collect key data during the machining process, such as cutting force, temperature, and vibration data, and analyze these data through statistical and machine learning methods to optimize future machining parameters and improve machining efficiency.

[0014] Preferably, the S1 comprises:

[0015] S11: Create 3D models of complex surfaces in computer-aided design software;

[0016] S12: Set the material properties of the model, including density, elastic modulus, and Poisson’s ratio;

[0017] S13: Apply loads and boundary conditions to perform structural analysis and calculate stress distribution and deformation;

[0018] S14: Use the von Mises stress formula to determine the yield condition of materials.

[0019] Preferably, S2 includes:

[0020] S21: Import the optimized 3D model into computer-aided manufacturing software;

[0021] S22: Generate initial toolpath using multi-axis milling function;

[0022] S23: Apply particle swarm optimization algorithm to optimize tool path and reduce machining time and tool wear;

[0023] S24: Setting the parameters of the particle swarm optimization algorithm, including the number of particles, the number of iterations, and the acceleration constant, and updating the tool path.

[0024] Preferably, S3 includes:

[0025] S31: Install force sensors, temperature sensors and vibration sensors on CNC machine tools;

[0026] S32: Calibrate the sensor to ensure data accuracy;

[0027] S33: Use LabVI EW programming to implement PID control algorithm and collect sensor data in real time;

[0028] S34: Dynamically adjust cutting speed and feed rate based on real-time data.

[0029] Preferably, S4 includes:

[0030] S41, input the optimized G code on the CNC machine tool;

[0031] S42, perform an idling test to confirm that there is no abnormal noise or vibration;

[0032] S43, start processing, and monitor cutting force, temperature and vibration in real time;

[0033] S44. Adjust processing parameters according to monitoring data to maintain optimal performance.

[0034] Preferably, S5 includes:

[0035] S51, use 3D scanner for real-time scanning;

[0036] S52, set the scanning accuracy to 0.05mm, and automatically scan once every 5 minutes;

[0037] S53, comparing the scanned data with the computer-aided design software model to calculate the dimensional deviation;

[0038] S54. If the dimensional deviation exceeds the tolerance range, the processing parameters of the CNC machine tool are automatically adjusted.

[0039] Preferably, S6 includes:

[0040] S61, grinding and polishing using AutoMet250;

[0041] S62, setting the grinding and polishing time to ensure that the surface roughness reaches Ra0.6μm~Ra1.0μm;

[0042] S63, use a three-dimensional coordinate measuring machine to perform three-dimensional geometric dimension inspection;

[0043] S64. Ensure that all dimensions are within the allowable tolerance range and apply the Kalman filter algorithm to filter the noise of the measurement data.

[0044] Preferably, the S7 includes:

[0045] S71, collect data such as cutting force, temperature, vibration, processing time and tool wear;

[0046] S72, use the matrix laboratory to analyze data and apply Fourier transform to analyze vibration frequency;

[0047] S73. Use Python’s open source machine learning library to build a machine learning model to predict machining quality;

[0048] S74. Optimize processing parameters according to the prediction results and feed back to the computer-aided design software and computer-aided manufacturing software system.

[0049] Preferably, the specific parameters of the particle swarm optimization algorithm are set as:

[0050] The inertia factor ranges from 0.7 to 0.9;

[0051] The number of particles ranges from 30 to 60;

[0052] The number of iterations ranges from 80 to 120;

[0053] The acceleration constants C1 and C2 range from 1.5 to 2.5.

[0054] Preferably, the machine learning model analyzes the relationship between cutting parameters and product quality through a logistic regression model, and the expression of the logistic regression model is:

[0055]

[0056] Among them, P is the probability of the target event, x1, x2, etc. are influencing factors, and β0, β1, etc. are regression coefficients.

[0057] The present invention provides a high-precision complex curved surface processing method. It has the following beneficial effects:

[0058] 1. The present invention significantly improves the precision and efficiency of complex surface processing by using advanced computer-aided design and manufacturing software, combined with particle swarm optimization algorithm and real-time data monitoring technology. The optimized tool path and adaptively adjusted cutting parameters reduce processing time and tool wear, thereby improving the processing efficiency of complex surfaces.

[0059] 2. The present invention utilizes a high-precision 3D scanner and sensor system to monitor key parameters in the processing process, such as cutting force, temperature and vibration, in real time, and performs dynamic optimization and adjustment through PID control and machine learning models, thereby ensuring that deviations can be discovered and corrected in time during the processing process and improving product consistency.

[0060] 3. The present invention collects and analyzes a large amount of data during the processing, applies advanced data analysis techniques such as Fourier transform and logistic regression model, predicts processing quality and optimizes parameters, thereby improving product quality and production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 is a flow chart of the method of the present invention;

[0062] Figure 2 It is a method flow chart of the particle swarm optimization algorithm of the present invention;

[0063] Figure 3 This is a flow chart of the method for real-time data monitoring of the present invention;

[0064] Figure 4 This is a flow chart of the 3D scanning method of the present invention. DETAILED DESCRIPTION

[0065] 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.

[0066] Please see attached Figure 1 -Attached Figure 4 The embodiment of the present invention provides a high-precision complex curved surface processing method, comprising the following steps:

[0067] S1. Use computer-aided design software to design a three-dimensional model of a complex surface, and conduct a detailed structural analysis of the model to ensure the feasibility of the design and the stability of the structure. Use computer-aided design software to design a three-dimensional model of a complex surface. Designers will input design parameters such as size, shape, and curvature, and the software will generate an accurate three-dimensional visual model. Furthermore, through the built-in structural analysis tool, the model will be analyzed to ensure that its mechanical properties and structural stability meet the expected conditions of use, thereby improving the accuracy and reliability of product design and reducing the risk of rework and failure in later production;

[0068] S2. Use computer-aided manufacturing software to generate accurate tool paths based on optimization algorithms, and iteratively optimize the paths to improve processing efficiency and reduce resource consumption. Through the iterative optimization process, the path is systematically refined to reduce cutting time and tool wear while maintaining maximum efficiency of material removal, thereby improving processing efficiency, reducing energy consumption and raw material consumption, and reducing overall production costs;

[0069] S3. Deploy a real-time data monitoring system on CNC machine tools and adjust processing parameters through adaptive technology to ensure high precision and high efficiency of the processing process. The dynamic adjustment mechanism can optimize processing conditions based on real-time feedback to ensure processing accuracy and efficiency, thereby improving processing accuracy and consistency, reducing processing defects, and improving production efficiency;

[0070] S4. Implement processing operations and track processing status in real time through advanced monitoring systems, such as cutting force and temperature, to dynamically optimize the processing process. The immediate response mechanism ensures the stability and optimality of the processing process. Optimizing the processing process improves processing efficiency, ensures product quality, and reduces production downtime and maintenance requirements;

[0071] S5. Use online quality control system for real-time dimension detection, use high-precision scanning equipment and software tools to compare actual dimensions with design models to ensure that products meet quality standards. Real-time dimension detection ensures that products strictly meet quality standards, greatly reducing scrap rate and subsequent quality inspection time, and conduct final dimension verification to ensure that all dimensions meet strict tolerance requirements;

[0072] S6. After processing is completed, meticulous post-processing steps such as grinding and polishing, as well as final dimensional verification using advanced measurement technology, are carried out to ensure that the product achieves the highest surface quality and dimensional accuracy, grinding and polishing to improve the surface quality of the product, and final dimensional verification is carried out to ensure that the product meets the highest standards and meets the customer's meticulous requirements;

[0073] S7. Collect key data during the machining process, such as cutting force, temperature and vibration data, and analyze these data through statistical and machine learning methods to optimize future machining parameters and improve machining efficiency. Through continuous data collection and analysis, continuous improvement of the machining process is achieved, cost is reduced, and production efficiency and product quality are improved.

[0074] In a preferred embodiment of the present invention, S1 includes:

[0075] S11: Create 3D models of complex surfaces in computer-aided design software. Through precise 3D modeling, this step ensures that the design intent is accurately expressed and provides accurate geometric data for subsequent processing steps, thereby improving the quality and accuracy of the final product;

[0076] S12: Set the material properties of the model, including density, elastic modulus, and Poisson's ratio. By accurately setting the material properties, you can more realistically simulate the stress and deformation in the physical world, thereby optimizing product design, reducing the number of prototype iterations, and improving development efficiency and cost-effectiveness.

[0077] S13: Apply loads and boundary conditions to perform structural analysis, calculate stress distribution and deformation, and help engineers identify potential weaknesses and stress concentration areas by simulating the mechanical response in a real working environment, thereby optimizing the design. This pre-emptive problem identification and resolution greatly reduces the failure rate in actual operation and improves product reliability and safety;

[0078] S14: Use the von Mises stress formula to determine the yield of the material. The von Mises stress formula can accurately predict the yield of the material under multiaxial stress state. The von Mises stress formula is:

[0079]

[0080] This formula calculates the equivalent stress under three-dimensional stress state, which is used to compare the yield limit of the material to avoid fatigue damage or sudden fracture in actual application, thus ensuring the strength and durability of the final product;

[0081] The CAD / CAM setting table is as follows:

[0082] project Design Software Model Type Size parameters Path Algorithm Design and Path Generation Autodesk Complex Surface Specific values ​​of each dimension Particle Swarm Optimization

[0083] The above table is used to record and standardize the settings in computer-aided design and manufacturing software, ensuring that the parameters used in the design and processing process meet the predetermined technical requirements, thereby helping to ensure the consistency of design and the accuracy of processing paths, and improving the quality of the final product.

[0084] In a preferred embodiment of the present invention, S2 includes:

[0085] S21: Import optimized 3D models into computer-aided manufacturing software to ensure seamless transition from design to manufacturing, avoid data loss during model conversion, and ensure the accuracy of the processing model;

[0086] S22: Generate initial tool paths using multi-axis milling capabilities. Multi-axis milling technology can significantly improve machining flexibility and efficiency, allowing the realization of more complex designs. It reduces tool changes and machine tool adjustments, thereby shortening production cycles and reducing costs;

[0087] S23: Apply particle swarm optimization algorithm to optimize tool path and reduce processing time and tool wear. Particle swarm optimization algorithm is an optimization technology based on group collaboration. It optimizes tool path by simulating the behavior of a group of particles searching for the optimal solution in the solution space and constantly adjusting the position and speed of particles. It can effectively reduce the length and complexity of the processing path, thereby reducing processing time and tool wear. This optimization not only improves production efficiency, but also extends tool life and reduces production costs;

[0088] S24: Set the parameters of the particle swarm optimization algorithm, including the number of particles, the number of iterations, and the acceleration constant, and update the tool path. The number of particles determines the breadth of the search space, the number of iterations determines the depth of the search, and the acceleration constant affects the speed and direction of the particle search. By carefully selecting these parameters, the particle swarm optimization algorithm can more effectively find the optimal or near-optimal solution. Correct parameter settings make the path optimization process more efficient, ensuring the economic and technical feasibility of the machining process while maintaining high-quality machining results.

[0089] In a preferred embodiment of the present invention, S3 includes:

[0090] S31: Install force sensors, temperature sensors and vibration sensors on CNC machine tools. Install high-precision force sensors, temperature sensors and vibration sensors on the spindle, tool clamping system and workbench of CNC machine tools. These sensors are responsible for real-time monitoring of physical and thermodynamic parameters during the processing, such as the force applied by the tool to the workpiece, the temperature of the working area and the vibration state of the entire equipment. By monitoring these key parameters, possible abnormalities in the processing process can be detected in time, which helps to maintain the stability of equipment operation and processing quality, thereby ensuring product consistency and reliability;

[0091] S32: Calibrate the sensor to ensure data accuracy. After the sensor is installed, perform system calibration. The calibration process includes setting the zero point for each sensor, detecting sensitivity, performing performance tests, and adjusting until the predetermined accuracy requirements are met;

[0092] S33: Use LabVIEW to implement the PID control algorithm and collect sensor data in real time. The PID control algorithm provides a robust control strategy through its three components - proportional, integral and differential - to achieve the expected performance standards of the machining process. This automatic adjustment mechanism can optimize the machining process, respond quickly, reduce overshoot and oscillation, and improve machining accuracy and surface quality;

[0093] S34: Dynamically adjust cutting speed and feed rate based on real-time data. Based on the data collected from the PID control system, the cutting speed and feed rate of the CNC machine tool are dynamically adjusted. These adjustments are based on real-time monitoring of cutting force, temperature, vibration and other data. Through real-time adjustments, they can adapt to different material properties or changes in cutting conditions, thereby reducing tool wear, preventing workpiece damage, and ensuring high efficiency and high precision of the machining process, ultimately improving the overall quality of the product;

[0094] The particle swarm optimization parameter table is as follows:

[0095]

[0096] The above table records in detail the key parameter settings of the particle swarm optimization algorithm, including inertia factor, number of particles, etc. The optimal configuration of these parameters is crucial to improving the efficiency and quality of the tool path generation process, helping to reduce processing time and improve accuracy.

[0097] In a preferred embodiment of the present invention, S4 includes:

[0098] S41. Input the optimized G code on the CNC machine. After completing all designs and path optimization, upload the generated G code to the CNC machine via a USB driver. Using the optimized G code can maximize processing efficiency and accuracy, reduce processing deviations caused by human errors or data conversion errors, and thus ensure processing quality and repeatability.

[0099] S42. Perform an idle running test to confirm that there is no abnormal noise or vibration. This step is used to detect whether the machine tool runs smoothly and whether there is any abnormal noise or vibration. It can effectively predict and avoid potential mechanical failures or operating errors, thereby protecting the machine tool and tools from damage and ensuring the safety and continuity of the production line;

[0100] S43, start processing, monitor cutting force, temperature and vibration in real time. By monitoring these key parameters in real time, any abnormality or deviation in the processing process, such as tool wear, overheating or overload, can be discovered in time. Real-time monitoring helps the operator or automatic control system to take quick measures to adjust parameters or suspend processing to prevent quality problems or equipment damage, ensure processing accuracy and extend the service life of machine tools and tools;

[0101] S44, adjust processing parameters based on monitoring data to maintain optimal performance. Dynamic adjustment of processing parameters can adapt to changes in processing conditions, such as tool wear, material non-uniformity, or slight changes in design specifications;

[0102] The real-time monitoring data record table is as follows:

[0103] Data Types Cutting force(N) Temperature(℃) Vibration frequency (Hz) Remark Monitoring data 0-2000N 20-200℃ 0-1000Hz Status description

[0104] The above table is used to record key parameters collected in real time during the processing, such as cutting force, temperature and vibration. Real-time monitoring of these data helps to detect and correct abnormalities in the processing process in a timely manner, ensuring the stability of the processing process and the consistency of product quality.

[0105] In a preferred embodiment of the present invention, S5 includes:

[0106] S51. Use 3D scanners for real-time scanning. During the processing, use high-precision 3D scanners to continuously scan the workpiece in real time. This method can monitor the processing quality and accuracy in real time, quickly detect deviations or defects, and make timely adjustments to ensure that the final product is highly consistent with the design model;

[0107] S52, set the scanning accuracy to 0.05mm, and automatically scan once every 5 minutes. By setting a fixed scanning interval and high accuracy, it can ensure continuous tracking and recording of subtle changes during the entire processing process, thereby reducing the need for manual inspection and related time delays, and improving production efficiency;

[0108] S53, compare the scanned data with the computer-aided design software model to calculate the dimensional deviation. This direct comparison with the computer-aided design software model makes the dimensional deviation immediately visible and quantifiable. By providing real-time feedback of the deviation information, the production process can be quickly adjusted to reduce material waste and improve product consistency and qualification rate;

[0109] S54. If the dimensional deviation exceeds the tolerance range, the processing parameters of the CNC machine tool are automatically adjusted. When the detected dimensional deviation exceeds the preset tolerance range, the system will automatically send a signal to the control system of the CNC machine tool to prevent the production of defective products, improve production flexibility and response speed, reduce the need for manual intervention, optimize resource utilization, ensure product quality, and reduce production delays and costs;

[0110] The 3D scanning and quality inspection data table is as follows:

[0111] Scanning accuracy(mm) Actual size (mm) CAD size (mm) Deviation value(mm) Eligible status 0.05mm 50.00-500.00mm 50.00-500.00mm ±0.00-±0.10mm Pass / Fail

[0112] The above table records the 3D scanning results and their comparison data with the CAD model, which is used to calculate dimensional deviations and evaluate whether the product meets quality standards.

[0113] In a preferred embodiment of the present invention, S6 includes:

[0114] S61. Use AutoMet250 for grinding and polishing. Using AutoMet250 equipment for grinding and polishing can effectively remove tiny surface unevenness and residual burrs and improve surface finish. It not only improves the aesthetics of the product, but also significantly reduces the friction coefficient and improves corrosion resistance, thereby improving the overall quality and service life of the product;

[0115] S62. Set the grinding and polishing time to ensure that the surface roughness reaches Ra0.6μm~Ra1.0μm. By accurately controlling the grinding and polishing time, the workpiece surface can be ensured to achieve the ideal smoothness and roughness. Not only does it improve the surface quality of the workpiece, it also helps to improve the functional performance of the product, such as reducing the wear rate and improving the contact quality, and ultimately improving the reliability and durability of the product;

[0116] S63. Use a three-dimensional coordinate measuring machine to detect three-dimensional geometric dimensions. Using a high-precision three-dimensional coordinate measuring machine for detection can accurately measure all geometric dimensions of the workpiece to ensure that it meets the design specifications and tolerance requirements. High-precision measurement data helps to verify the quality of the processing process and provide necessary feedback information for further optimization of the processing technology to ensure the high quality and consistency of each product;

[0117] S64. Ensure that all dimensions are within the allowable tolerance range, apply the Kalman filter algorithm to filter the noise of the measurement data, and after obtaining the measurement data of the high-precision three-dimensional coordinate measuring machine, apply the Kalman filter algorithm to process the measurement data to remove noise and errors, and effectively reduce the random errors in the measurement data and improve the accuracy and reliability of the measurement data.

[0118] In a preferred embodiment of the present invention, S7 includes:

[0119] S71. Collect data such as cutting force, temperature, vibration, processing time and tool wear. By collecting comprehensive processing data, you can understand the physical and mechanical changes in each processing step in detail. These data not only help monitor the current processing status, but also provide a rich data source for subsequent analysis and optimization, which helps to improve processing quality and efficiency;

[0120] S72. Use the matrix laboratory to analyze data and apply Fourier transform to analyze vibration frequency. Use the matrix laboratory to analyze data, especially apply Fourier transform to vibration data to convert from time domain to frequency domain and identify different frequency components in the processing process. Fourier transform can decompose complex time signals into different frequency components, making vibration analysis more intuitive and accurate. By identifying specific frequency components, the resonance problem of the machine tool, the tool status or material characteristics can be diagnosed, so as to make targeted adjustments and optimizations, reduce processing defects and improve product quality;

[0121] S73. Use Python's open source machine learning library to build a machine learning model to predict machining quality. Use Python's open source machine learning library to build a prediction model. Collected cutting force, temperature, vibration and other data are used as input features, and supervised learning algorithms are used to train the model to predict machining quality. Through machine learning models, feature rules can be learned and extracted from a large amount of historical data to accurately predict machining results and quality. Such prediction capabilities help to discover potential problems in advance, adjust machining parameters, optimize production processes, and improve product consistency and qualification rates;

[0122] S74. Optimize the processing parameters based on the prediction results and feed them back to the computer-aided design software and computer-aided manufacturing software system. By converting the prediction results into actual processing parameter optimization, closed-loop control can be achieved to improve the flexibility and adaptability of the processing process. This feedback mechanism can not only significantly improve production efficiency, but also ensure the stability and consistency of product quality, reduce scrap rate and rework rate, and reduce production costs.

[0123] In a preferred embodiment of the present invention, the specific parameters of the particle swarm optimization algorithm are set as:

[0124] The inertia factor ranges from 0.7 to 0.9. In the particle swarm optimization algorithm, the inertia factor is used to control the attenuation of the particle velocity. The inertia factor is set to adjust the particle velocity in each iteration to control the search range of the particle. Setting the inertia factor between 0.7 and 0.9 can ensure that the search process has both global search capability and local convergence capability.

[0125] The number of particles ranges from 30 to 60. The number of particles determines the algorithm's search ability in the solution space. Selecting 30 to 60 particles can ensure that the algorithm has sufficient exploration ability while avoiding the computational overhead caused by too many particles;

[0126] The number of iterations ranges from 80 to 120, and the number of iterations determines the number of rounds of searching and updating by the particle swarm. By setting the number of iterations between 80 and 120, it can ensure that the algorithm can fully iterate during the search process and find the optimal or near-optimal solution;

[0127] The range of acceleration constants C1 and C2 is 1.5 to 2.5, which can ensure that the particle swarm can make full use of individual experience and learn from group wisdom during the search process.

[0128] In a preferred embodiment of the present invention, the machine learning model analyzes the relationship between cutting parameters and product quality through a logistic regression model, and the expression of the logistic regression model is:

[0129]

[0130] Among them, P is the probability of the target event, x1, x2, etc. are influencing factors, β0, β1, etc. are regression coefficients. In the process of complex surface processing, sensors installed on CNC machine tools are used to collect various data including cutting force, temperature, vibration, processing time and tool wear in real time, and the collected data are cleaned, normalized, and feature selected. Preprocessing can improve the quality and consistency of data and reduce errors in the model training process. Feature selection helps to improve the predictive ability and efficiency of the model, making the model more interpretable and applicable, thereby improving the accuracy of overall processing quality prediction. The logistic regression model estimates parameters by maximizing the likelihood function, so that the model can effectively predict product quality. Through model training, the impact of cutting parameters on product quality can be identified and quantified. Through model verification, the performance of the model in actual data can be evaluated, the deficiencies of the model can be identified and improved, and at the same time, the scrap rate and rework rate can be reduced, production costs can be reduced, and the intelligence level and competitiveness of the production line can be improved.

[0131] 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 complex curved surface processing method, characterized in that: The following steps are involved: S1. Use computer-aided design software to design three-dimensional models of complex surfaces and conduct detailed structural analysis of the models to ensure the feasibility of the design and the stability of the structure; S2. Use computer-aided manufacturing software to generate accurate tool paths based on optimization algorithms and iteratively optimize the paths to improve processing efficiency and reduce resource consumption; S3. Deploy a real-time data monitoring system on CNC machine tools and adjust processing parameters through adaptive technology to ensure high precision and high efficiency of the processing process; S4. Implement machining operations and track machining status, such as cutting force and temperature, in real time through advanced monitoring systems to dynamically optimize the machining process; S5. Use online quality control system to conduct real-time dimension inspection, and use high-precision scanning equipment and software tools to compare actual dimensions with design models to ensure that products meet quality standards; S6. After processing, detailed post-processing steps such as grinding and polishing, as well as final dimensional verification using advanced measurement technology, are carried out to ensure that the product achieves the highest surface quality and dimensional accuracy; S7. Collect key data during the machining process, such as cutting force, temperature, and vibration data, and analyze these data through statistical and machine learning methods to optimize future machining parameters and improve machining efficiency.

2. A high-precision complex curved surface processing method according to claim 1, characterized in that: The S1 includes: S11: Create 3D models of complex surfaces in computer-aided design software; S12: Set the material properties of the model, including density, elastic modulus, and Poisson’s ratio; S13: Apply loads and boundary conditions to perform structural analysis and calculate stress distribution and deformation; S14: Use the von Mises stress formula to determine the yield condition of materials.

3. A high-precision complex curved surface processing method according to claim 1, characterized in that: The S2 includes: S21: Import the optimized 3D model into computer-aided manufacturing software; S22: Generate initial toolpath using multi-axis milling function; S23: Apply particle swarm optimization algorithm to optimize tool path and reduce machining time and tool wear; S24: Setting the parameters of the particle swarm optimization algorithm, including the number of particles, the number of iterations, and the acceleration constant, and updating the tool path.

4. A high-precision complex curved surface machining method according to claim 1, characterized in that: The S3 includes: S31: Install force sensors, temperature sensors and vibration sensors on CNC machine tools; S32: Calibrate the sensor to ensure data accuracy; S33: Use LabVIEW to implement PID control algorithm and collect sensor data in real time; S34: Dynamically adjust cutting speed and feed rate based on real-time data.

5. A high-precision complex curved surface machining method according to claim 1, characterized in that: The S4 includes: S41, input the optimized G code on the CNC machine tool; S42, perform an idling test to confirm that there is no abnormal noise or vibration; S43, start processing, and monitor cutting force, temperature and vibration in real time; S44. Adjust processing parameters according to monitoring data to maintain optimal performance.

6. A high-precision complex curved surface machining method according to claim 1, characterized in that: The S5 includes: S51, use 3D scanner for real-time scanning; S52, set the scanning accuracy to 0.05mm, and automatically scan once every 5 minutes; S53, comparing the scanned data with the computer-aided design software model to calculate the dimensional deviation; S54. If the dimensional deviation exceeds the tolerance range, the processing parameters of the CNC machine tool are automatically adjusted.

7. A high-precision complex curved surface machining method according to claim 1, characterized in that: The S6 includes: S61, grinding and polishing using AutoMet250; S62, setting the grinding and polishing time to ensure that the surface roughness reaches Ra0.6μm~Ra1.0μm; S63, use a three-dimensional coordinate measuring machine to perform three-dimensional geometric dimension inspection; S64. Ensure that all dimensions are within the allowable tolerance range and apply the Kalman filter algorithm to filter the noise of the measurement data.

8. A high-precision complex curved surface machining method according to claim 1, characterized in that: The S7 includes: S71, collect data such as cutting force, temperature, vibration, processing time and tool wear; S72, use the matrix laboratory to analyze data and apply Fourier transform to analyze vibration frequency; S73. Use Python’s open source machine learning library to build a machine learning model to predict machining quality; S74. Optimize processing parameters according to the prediction results and feed back to the computer-aided design software and computer-aided manufacturing software system.

9. A high-precision complex curved surface machining method according to claim 3, characterized in that: The specific parameters of the particle swarm optimization algorithm are set as: The inertia factor ranges from 0.7 to 0.9; The number of particles ranges from 30 to 60; The number of iterations ranges from 80 to 120; The acceleration constants C1 and C2 range from 1.5 to 2.

5.

10. A high-precision complex curved surface machining method according to claim 8, characterized in that: The machine learning model analyzes the relationship between cutting parameters and product quality through a logistic regression model, and the expression of the logistic regression model is: Among them, P is the probability of the target event, x1, x2, etc. are influencing factors, and β0, β1, etc. are regression coefficients.

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