Nanoscale saw blade shape optimization and production method adopting neural network

Abstract

The invention discloses a nanoscale saw blade shape optimization and production method adopting a neural network. The nanoscale saw blade shape optimization and production method comprises the following steps: S1, performing high-resolution imaging and modeling on an initial shape of a nanoscale saw blade by utilizing a scanning electron microscope and a three-dimensional confocal microscopic measurement technology; s2, constructing a shape optimization model based on a convolutional neural network; s3, generating a virtual saw blade shape data set by using the generative adversarial network, and training an optimization model in combination with experimental data; s4, performing multi-scale performance verification on the optimized saw blade shape by adopting a finite element analysis and molecular dynamics simulation technology; s5, realizing high-precision manufacturing based on a femtosecond laser nano processing technology; s6, performing shape measurement and surface roughness evaluation on the processed saw blade by using an optical interferometer and an atomic force microscope; and S7, integrating an automatic shape detection and feedback adjustment module through the programmable logic controller, and correcting the machining deviation in real time. The method has the advantages of being high in design precision, high in machining consistency and high in production efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of nano-scale saw blade shape optimization and production, and particularly to a nano-scale saw blade shape optimization and production method using a neural network. Background Art

[0002] With the rapid development of nanotechnology and intelligent manufacturing technology, the demand for high-precision machining tools has increased significantly. Especially in the field of cutting tools, the shape optimization and manufacturing of nano-scale saw blades have become key technologies to improve cutting efficiency and product precision. However, traditional saw blade shape design and processing methods have shown obvious deficiencies when faced with complex application scenarios and high-performance requirements. Optimizing the shape of nano-scale saw blades to improve their cutting performance and manufacturing precision still faces technical challenges.

[0003] In the prior art, traditional methods usually rely on empirical rules and trial-and-error processes for saw blade shape design and optimization, or use fixed machining paths and parameters for saw blade production. These methods have the following technical defects:

[0004] 1. Limitations in design optimization: Traditional methods are usually based on empirical models or single-objective optimization, and it is difficult to fully consider the complex relationships among saw blade shape, material properties, and cutting efficiency, resulting in design results that are difficult to meet the multi-objective optimization requirements.

[0005] 2. Insufficient machining precision: Traditional machining technologies are limited by the precision of equipment and the capabilities of control systems, and it is impossible to achieve high-precision machining of saw blade shapes at the nano-scale, resulting in large machining deviations and shape consistency that cannot meet high requirements.

[0006] 3. Insufficiency in simulation analysis: Existing simulation technologies mostly focus on the macroscopic level, lacking multi-scale performance verification of saw blade shapes and detailed analysis of surface micro-behaviors, and it is difficult to accurately predict the stress distribution and wear behaviors that may occur during the cutting process.

[0007] 4. Limited detection and feedback capabilities: Traditional detection systems are mostly offline detections, and it is impossible to achieve real-time shape detection and feedback adjustment, resulting in difficulty in dynamically maintaining shape consistency in mass production.

[0008] 5. Insufficient data processing capabilities: In the process of shape optimization and production, there is a lack of intelligent analysis and processing capabilities for large-scale design parameters and machining data, and it is impossible to effectively utilize modern artificial intelligence technologies for optimization.

[0009] Therefore, how to provide a nano-scale saw blade shape optimization and production method using a neural network is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0010] An object of the present invention is to propose a method for optimizing and producing a nanoscale saw blade shape based on a neural network. The present invention makes full use of neural network algorithms, femtosecond laser nanomachining technology, multi-scale simulation analysis technology, and automated production control technology, and details the specific steps for realizing the efficient production of saw blades through intelligent shape optimization, real-time detection, and feedback adjustment, with the advantages of high design accuracy, strong processing consistency, and high production efficiency.

[0011] A method for optimizing and producing a nanoscale saw blade shape using a neural network according to an embodiment of the present invention is characterized by including the following steps:

[0012] S1. Use a scanning electron microscope and three-dimensional confocal microscopy measurement technology to perform high-resolution imaging and modeling on the initial shape of the nanoscale saw blade, and extract key geometric parameters including tooth shape angle, edge curvature radius, and tooth peak spacing;

[0013] S2. Build a shape optimization model based on a convolutional neural network, use the key geometric parameters and material properties as input variables, and combine an adaptive weighted multi-objective optimization algorithm to optimize the cutting efficiency, durability, and production accuracy of the saw blade;

[0014] S3. Use a generative adversarial network to generate a virtual saw blade shape dataset, train the optimization model in combination with experimental data, and improve the prediction accuracy of the optimization result by adjusting the weights through multiple iterations;

[0015] S4. Use finite element analysis and molecular dynamics simulation technology to perform multi-scale performance verification on the optimized saw blade shape, and analyze its stress distribution and wear behavior under actual cutting conditions;

[0016] S5. Based on femtosecond laser nanomachining technology, achieve high-precision manufacturing according to the optimized saw blade parameters, and control the cutting path accuracy within 10 nanometers;

[0017] S6. Use an optical interferometer and an atomic force microscope to measure the shape of the processed saw blade and evaluate the surface roughness, so that the conformity of the shape with the optimized design is more than 99%;

[0018] S7. Integrate an automated shape detection and feedback adjustment module through a programmable logic controller to perform real-time correction on the processing deviation, and realize the dynamic maintenance of shape consistency during batch production.

[0019] Optionally, the S1 specifically includes:

[0020] S11. Use a scanning electron microscope to perform multi-angle high-resolution imaging on the initial sample of the nanoscale saw blade to obtain a two-dimensional projection image of the initial shape of the saw blade;

[0021] S12. Perform image stitching and overlay on the two-dimensional projection image, and generate a complete three-dimensional contour model of the initial shape of the saw blade through an image processing algorithm;

[0022] S13. Use three-dimensional confocal microscopy measurement technology to measure the surface topography of the saw blade with nanometer-level resolution, record the height data z(x, y) of its surface micro feature points, where x and y are the horizontal and vertical coordinates of the measurement area, and z is the height data;

[0023] S14. Match the z(x, y) data obtained by microscopic measurement with the three-dimensional contour model generated by the scanning electron microscope, and use the error minimization algorithm to adjust the fitting parameters of the three-dimensional model to determine the accuracy of the measurement data;

[0024] S15. Extract key geometric parameters from the fitted three-dimensional model through a geometric analysis algorithm, including the tooth profile angle θ, the edge curvature radius r, and the tooth peak spacing d:

[0025]

[0026] where κ is the local curvature of the edge, and d is the horizontal distance between adjacent tooth peaks;

[0027] S16. Conduct data collation and statistical analysis on the extracted geometric parameters, store them as input parameters of the shape optimization model, and record the characteristic parameters of the initial shape.

[0028] Optionally, the specific steps of S2 include:

[0029] S21. Define the objective function F(x) for saw blade shape optimization, where x = [x 1 , x 2 ,..., x n are the key geometric parameters of the saw blade, including the tooth profile angle θ, the edge curvature radius r, and the tooth peak spacing d:

[0030] F(x) = w 1 f 1 (x) + w 2 f 2 (x) + w 3 f 3 (x);

[0031] where f 1 (x), f 2 (x), and f 3 (x) respectively represent the performance index functions of cutting efficiency, durability, and production accuracy, and w 1 , w 2 , w 3 are weight coefficients, satisfying w 1 + w 2 + w3 = 1;

[0032] S22. Establish a shape optimization model with a convolutional neural network as the core. The input of the model is a set of geometric parameters and material property parameters, and the output is an estimated value of the objective function F(x);

[0033] S23. Generate an initial training dataset, map the key geometric parameters and material properties to the actual performance index values through experimental and simulation results, and standardize the data;

[0034] S24. Use a generative adversarial network to expand the training dataset, generate diverse virtual geometric parameter combinations and corresponding material property parameters through the adversarial network, and enhance the generalization ability of the model;

[0035] S25. Train the model using an adaptive weighted multi-objective optimization algorithm, optimize the objective function value by dynamically adjusting the weights w 1 , w 2 , w 3 , update the network parameters, and obtain the optimized neural network;

[0036] S26. Use the optimized shape model for performance prediction, verify its prediction accuracy for the cutting efficiency, durability, and production accuracy of the saw blade, and record the optimization results.

[0037] Optionally, the specific steps of S3 include:

[0038] S31. Construct a generative adversarial network model, including a generator G and a discriminator D. Among them, the generator G is used to generate virtual saw blade shape data, and the discriminator D is used to determine whether the input data is real data;

[0039] S32. Construct the experimental measurement obtained saw blade shape geometric parameter data x = [x 1 , x 2 ,..., x n and the corresponding material property parameters p = [p 1 , p 2 ,..., p m into an initial real dataset T r , and initialize the parameters of the generative adversarial network model;

[0040] S33. Define the objective function L G :

[0041] L G = -E[log(D(G(z)))];

[0042] Among them, z is the random noise vector input to the generator, and G(z) represents the virtual saw blade shape data output by the generator;

[0043] S34. Define the objective function \(L\) of the discriminator D :

[0044] \(L\) D = -E[log(D(x))] - E[log(1 - D(G(z)))];

[0045] where \(D(x)\) represents the output probability of the discriminator for real data, and \(D(G(z))\) represents the output probability of the discriminator for virtual data;

[0046] S35. Use the stochastic gradient descent method to alternately optimize the parameters of the generator and the discriminator, and make the virtual saw blade shape data generated by the generator close to the real data by minimizing \(L\) G and \(L\) D ;

[0047] S36. During the training process of the generative adversarial network, introduce the constraint conditions of key geometric parameters:

[0048]

[0049] where \(x'\) is the virtual data generated by the generator, \(x\) is the real data, \(\theta\) is the tooth profile angle, \(r\) is the edge curvature radius, and \(d\) is the tooth peak spacing;

[0050] S37. Merge the generated virtual saw blade shape data set with the real data set \(T\) r to construct an enhanced data set \(T\) a , and use \(T\) a to train the convolutional neural network model and update the network weights through multiple iterations.

[0051] Optionally, the specific steps of S4 include:

[0052] S41. Based on the optimized saw blade shape parameters, construct a three-dimensional geometric model and define the dynamic response characteristics of the material. The material stress tensor \(\sigma\) is:

[0053]

[0054] where \(V\) is the material volume, \(N\) is the number of particles in the element, \(m\) i is the particle mass, \(v\) i is the particle velocity, \(r\) i is the position vector, \(f\) i is the force vector, represents the tensor outer product operation;

[0055] S42. Apply a dynamic cutting load to the optimized model, use the finite element analysis technology to solve the stress distribution field of the material during the cutting process, record the maximum stress concentration point, and calculate the local thermal stress generated during the cutting process through thermo-mechanical coupling analysis;

[0056] S43. Introduce a material interface interaction model in the molecular dynamics simulation, define the intermolecular potential function U(r), and calculate the frictional force F between the molecules on the saw blade surface and the cutting material molecules. ij :

[0057] U(r) = Ae -αr -Be -βr ;

[0058]

[0059] where r is the intermolecular distance, A and B are potential function parameters, and α and β are attenuation factors. represents the gradient operation, and jr ij is the distance between molecules i and j;

[0060] S44. Simulate the atomic-level motion trajectory of the saw blade under dynamic cutting conditions and record the surface wear depth Δh:

[0061]

[0062] where ΔE k is the energy change of the surface atoms in the k-th layer, τ k is the corresponding time step, and n is the total number of time steps;

[0063] S45. Combine the finite element analysis and the molecular dynamics simulation results to construct a multi-scale evaluation model for the cutting performance of the saw blade, and analyze the stress concentration area and the hot spot area of wear behavior;

[0064] S46. Generate a verification report, feedback the results to the shape optimization model, and improve the objective function and optimization parameters based on the verification report.

[0065] Optionally, the S5 specifically includes:

[0066] S51. Design a machining path model according to the optimized geometric parameters of the saw blade, and use computer-aided design tools to generate a three-dimensional machining path. The path parameters include the cutting path width, depth, and machining speed;

[0067] S52. Set the machining parameters based on the femtosecond laser nanomachining technology, including the laser pulse energy, repetition frequency, and scanning speed, and upload the parameters to the machining control system;

[0068] S53. During the machining process, achieve selective material removal by controlling the laser focusing position and scanning path, and control the machining surface accuracy within the designed target range;

[0069] S54. Detect the surface topography deviation in the machining area using a real-time monitoring system, adjust the laser parameters and path through feedback control, and dynamically correct the deviation during the machining process;

[0070] S55. Use optical interference technology to perform real-time measurement on the machined surface, evaluate the consistency between the surface shape and the design parameters, and monitor the machining accuracy of the key areas;

[0071] S56. Optimize and iterate the machining path by combining the machining results and the monitoring data, so that the accuracy of the cutting path is within 10 nanometers. After the machining is completed, conduct a comprehensive shape inspection and surface quality evaluation on the machined saw blade, and record the inspection data.

[0072] Optionally, the specific steps of S6 include:

[0073] S61. Measure the surface shape of the machined saw blade using an optical interferometer, generate an interference pattern of the surface profile, and record the height data z(x, y) of the surface shape, where x and y are two-dimensional spatial coordinates, and z(x, y) is the surface height value;

[0074] S62. Calculate the conformity C of the surface shape through the optical interference data f :

[0075]

[0076] where z opt (x, y) is the theoretical height of the optimized design, A is the measurement area, and |z(x, y) - z opt (x, y)| represents the deviation value between the actual height and the theoretical height;

[0077] S63. Measure the surface roughness of the saw blade using an atomic force microscope, record the height distribution of the microscopic surface through three-dimensional scanning, and the surface roughness parameter R s is:

[0078]

[0079] where z i is the height of the scanning point, is the average height, and n is the total number of scanning points;

[0080] S64. Combine the measurement results of the optical interferometer and the atomic force microscope to generate a comprehensive evaluation report, and use the statistical results of the conformity C f and the roughness parameter R s for subsequent adjustment of the shape optimization model, and record the measurement data for production closed-loop optimization.

[0081] Optionally, the specific steps of S7 include:

[0082] S71. Integrate a shape detection module using a programmable logic controller, obtain the shape and surface data of the processed saw blade through an optical interferometer and an atomic force microscope, and transmit the data to the programmable logic controller system in real time for data processing;

[0083] S72. Preprocess the detected data, including denoising and standardization, and calculate the machining deviation Δz(x,y):

[0084] Δz(x,y) = z(x,y) - z opt (x,y);

[0085] where z(x,y) is the actually measured height data, and z opt (x,y) is the theoretical height of the optimized design;

[0086] S73. Input the machining deviation Δz(x,y) into the feedback adjustment module, and construct a real-time correction algorithm using a dynamic deviation distribution model:

[0087]

[0088] where, represents the deviation gradient, quantifies the change rate of the machining deviation within the machining area A;

[0089] S74. Dynamically optimize the machining parameters, including the laser focus position, scanning speed, and pulse energy, in combination with the feedback adjustment results, reduce the deviation gradient, and achieve real-time dynamic maintenance of shape consistency;

[0090] S75. Evaluate the shape consistency of the saw blades produced in batches, calculate the consistency index C u , and generate a shape detection and consistency evaluation report:

[0091]

[0092] where A is the machining area, |Δz(x,y)| is the absolute value of the machining deviation, and z opt (x,y) is the optimized design height.

[0093] The beneficial effects of the present invention are:

[0094] (1) By combining neural network algorithms, convolutional neural network models, and generative adversarial network technologies, the present invention realizes the intelligent optimization of the shape of nano-level saw blades, can comprehensively consider multi-objective requirements such as cutting efficiency, durability, and production accuracy, breaks through the limitations of traditional single optimization methods based on experience, and thus significantly improves the scientificity and accuracy of saw blade design.

[0095] (2) The present invention adopts femtosecond laser nano-processing technology and realizes nano-level precision material removal and shape manufacturing by dynamically adjusting the laser processing path and parameters, so that the processing accuracy of the cutting path is controlled within 10 nanometers. At the same time, combined with the real-time monitoring technology of optical interferometer and atomic force microscope, a high degree of consistency between the shape and the optimized design parameters is achieved.

[0096] (3) The present invention uses multi-scale performance verification of finite element analysis and molecular dynamics simulation technology, which can not only comprehensively analyze the stress distribution and wear behavior of the saw blade during the cutting process, but also provide reliable simulation data support for optimized design, thereby ensuring the stability and durability of the saw blade performance.

[0097] (4) The present invention integrates a programmable logic controller and an automated feedback adjustment module, and uses a dynamic deviation distribution model to perform real-time correction of processing deviations to ensure dynamic maintenance of shape consistency in mass production. At the same time, it generates detailed inspection and evaluation reports to provide data support for subsequent production optimization.

[0098] (5) The present invention makes full use of neural networks and intelligent control technologies in the shape detection, simulation verification and manufacturing processes, improves production efficiency, reduces manual intervention, and realizes the effective integration of high precision and high consistency in mass production, providing new ideas and technical guarantees for the industrialization of high-end cutting tools. BRIEF DESCRIPTION OF THE DRAWINGS

[0099] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0100] Figure 1 This is the overall framework diagram of the neural network-based nano-scale saw blade shape optimization and production method proposed in the present invention. DETAILED DESCRIPTION

[0101] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0102] refer to Figure 1 , a nano-scale saw blade shape optimization and production method using a neural network, characterized in that it includes the following steps:

[0103] S1. Use scanning electron microscopy and 3D confocal microscopy to perform high-resolution imaging and modeling of the initial shape of the nano-scale saw blade, and extract key geometric parameters including tooth angle, edge curvature radius, and tooth peak spacing;

[0104] In this embodiment, S1 specifically includes:

[0105] S11. Perform multi-angle high-resolution imaging on the initial sample of the nanoscale saw blade using a scanning electron microscope to obtain a two-dimensional projection image of the initial shape of the saw blade;

[0106] S12. Perform image stitching and superposition on the two-dimensional projection image, and generate a complete three-dimensional contour model of the initial shape of the saw blade through an image processing algorithm;

[0107] S13. Use three-dimensional confocal microscopy measurement technology to perform nanoscale resolution topography measurement on the surface of the saw blade, and record the height data z(x, y) of its surface micro feature points, where x and y are the horizontal and vertical coordinates of the measurement area, and z is the height data;

[0108] S14. Match the z(x, y) data obtained by microscopic measurement with the three-dimensional contour model generated by the scanning electron microscope, and use the error minimization algorithm to adjust the fitting parameters of the three-dimensional model to determine the accuracy of the measurement data;

[0109] S15. Extract key geometric parameters from the fitted three-dimensional model through a geometric analysis algorithm, including the tooth profile angle θ, the edge curvature radius r, and the tooth peak spacing d:

[0110]

[0111] Among them, κ is the local curvature of the edge, and d is the horizontal distance between adjacent tooth peaks;

[0112] S16. Perform data sorting and statistical analysis on the extracted geometric parameters, store them as input parameters of the shape optimization model, and record the characteristic parameters of the initial shape.

[0113] S2. Build a shape optimization model based on a convolutional neural network, use the key geometric parameters and material properties as input variables, and optimize the cutting efficiency, durability, and production accuracy of the saw blade in combination with an adaptive weighted multi-objective optimization algorithm;

[0114] In this embodiment, S2 specifically includes:

[0115] S21. Define the objective function F(x) for saw blade shape optimization, where x = [x 1 , x 2 ,..., x n are the key geometric parameters of the saw blade, including the tooth profile angle θ, the edge curvature radius r, and the tooth peak spacing d:

[0116] F(x) = w 1 f 1 (x) + w 2 f 2 (x) + w3 f 3 (x);

[0117] Among them, f 1 (x), f 2 (x) and f 3 (x) respectively represent the performance index functions of cutting efficiency, durability and production accuracy. w 1 , w 2 , w 3 are weight coefficients, satisfying w 1 +w 2 +w 3 = 1;

[0118] S22. Establish a shape optimization model with a convolutional neural network as the core. The input of the model is a set of geometric parameters and material property parameters, and the output is an estimated value of the objective function F(x);

[0119] S23. Generate an initial training data set, map the key geometric parameters and material properties to the actual performance index values through experimental and simulation results, and standardize the data;

[0120] S24. Use a generative adversarial network to expand the training data set, generate diverse virtual geometric parameter combinations and corresponding material property parameters through the adversarial network, and enhance the generalization ability of the model;

[0121] S25. Train the model using an adaptive weighted multi-objective optimization algorithm, optimize the objective function value by dynamically adjusting the weights w 1 , w 2 , w 3 , and update the network parameters to obtain an optimized neural network;

[0122] S26. Use the optimized shape model for performance prediction, and verify its prediction accuracy for the cutting efficiency, durability and production accuracy of the saw blade, and record the optimization results.

[0123] S3. Use a generative adversarial network to generate a virtual saw blade shape data set, train and optimize the model in combination with experimental data, and improve the prediction accuracy of the optimization results through multiple iterations of adjusting the weights;

[0124] In this embodiment, S3 specifically includes:

[0125] S31. Construct a generative adversarial network model, including a generator G and a discriminator D. Among them, the generator G is used to generate virtual saw blade shape data, and the discriminator D is used to discriminate whether the input data is real data;

[0126] S32. The saw blade shape geometric parameter data x = [x 1 , x 2,...,x n and the corresponding material property parameters p = [p 1 , p 2 ,..., p m to construct the initial real dataset T r , and initialize the parameters of the generative adversarial network model;

[0127] S33. Define the objective function L of the generator G :

[0128] L G = -E[log(D(G(z)))];

[0129] where z is the random noise vector input to the generator, and G(z) represents the virtual saw blade shape data output by the generator;

[0130] S34. Define the objective function L of the discriminator D :

[0131] L D = -E[log(D(x))] - E[log(1 - D(G(z)))];

[0132] where D(x) represents the output probability of the discriminator for real data, and D(G(z)) represents the output probability of the discriminator for virtual data;

[0133] S35. Use the stochastic gradient descent method to alternately optimize the parameters of the generator and the discriminator, and make the virtual saw blade shape data generated by the generator close to the real data by minimizing L G and L D ;

[0134] S36. During the training process of the generative adversarial network, introduce the constraint conditions of key geometric parameters:

[0135]

[0136] where x' is the virtual data generated by the generator, x is the real data, θ is the tooth profile angle, r is the edge curvature radius, and d is the tooth peak spacing;

[0137] S37. Merge the generated virtual saw blade shape dataset with the real dataset T r to construct the enhanced dataset T a , and use T a to train the convolutional neural network model and update the network weights through multiple iterations.

[0138] S4. Use finite element analysis and molecular dynamics simulation techniques to conduct multi-scale performance verification on the optimized saw blade shape, and analyze its stress distribution and wear behavior under actual cutting conditions;

[0139] In this embodiment, S4 specifically includes:

[0140] S41. Construct a three-dimensional geometric model based on the optimized saw blade shape parameters, and define the dynamic response characteristics of the material. The material stress tensor σ is:

[0141]

[0142] where V is the material volume, N is the number of particles in the element, m i is the particle mass, v i is the particle velocity, r i is the position vector, f i is the force vector, represents the tensor outer product operation;

[0143] S42. Apply dynamic cutting loads to the optimized model, use finite element analysis technology to solve the stress distribution field of the material during cutting, record the maximum stress concentration point, and calculate the local thermal stress generated during cutting through thermo-mechanical coupling analysis;

[0144] S43. Introduce a material interface interaction model in the molecular dynamics simulation, define the intermolecular potential function U(r), and calculate the friction force F between the molecules on the saw blade surface and the cutting material molecules ij :

[0145] U(r) = Ae -αr -Be -βr ;

[0146]

[0147] where r is the intermolecular distance, A and B are potential function parameters, α and β are attenuation factors, represents the gradient operation, jr ij is the distance between molecules i and j;

[0148] S44. Simulate the atomic-level motion trajectory of the saw blade under dynamic cutting conditions, and record the surface wear depth Δh:

[0149]

[0150] where ΔE k is the energy change of the k-th layer of surface atoms, τ k is the corresponding time step, and n is the total number of time steps;

[0151] S45. Combine the finite element analysis and molecular dynamics simulation results to construct a multi-scale evaluation model for the cutting performance of the saw blade, and analyze the stress concentration area and the hot spot area of wear behavior;

[0152] S46. Generate a verification report, feedback the results to the shape optimization model, and improve the objective function and optimization parameters based on the verification report.

[0153] S5. Based on the femtosecond laser nanomachining technology, achieve high-precision manufacturing according to the optimized saw blade parameters, and control the cutting path accuracy within 10 nanometers.

[0154] In this embodiment, S5 specifically includes:

[0155] S51. Design a machining path model according to the optimized saw blade geometric parameters, and use computer-aided design tools to generate a three-dimensional machining path. The path parameters include the cutting path width, depth, and machining speed.

[0156] S52. Set machining parameters based on the femtosecond laser nanomachining technology, including laser pulse energy, repetition frequency, and scanning speed, and upload the parameters to the machining control system.

[0157] S53. During the machining process, achieve selective material removal by controlling the laser focus position and scanning path, and control the machining surface accuracy within the designed target range.

[0158] S54. Use a real-time monitoring system to detect the surface topography deviation of the machining area, and adjust the laser parameters and path through feedback control to dynamically correct the deviation during the machining process.

[0159] S55. Use optical interference technology to perform real-time measurement on the machining surface, evaluate the consistency between the surface shape and the design parameters, and monitor the machining accuracy of the key areas.

[0160] S56. Optimize and iterate the machining path by combining the machining results and monitoring data to make the accuracy of the cutting path within 10 nanometers. After machining, conduct a comprehensive shape inspection and surface quality assessment on the machined saw blade, and record the inspection data.

[0161] S6. Use an optical interferometer and an atomic force microscope to measure the shape of the machined saw blade and evaluate the surface roughness, so that the conformity of the shape with the optimized design is more than 99%.

[0162] In this embodiment, S6 specifically includes:

[0163] S61. Use an optical interferometer to measure the surface shape of the machined saw blade, generate an interference pattern of the surface profile, and record the height data z(x, y) of the surface shape, where x and y are two-dimensional spatial coordinates, and z(x, y) is the surface height value.

[0164] S62. Calculate the conformity C of the surface shape through the optical interference data f :

[0165]

[0166] Among them, z opt (x, y) is the theoretical height of the optimized design, A is the area of the measurement region, and |z(x, y) - z opt (x, y)| represents the deviation value between the actual height and the theoretical height;

[0167] S63. Use an atomic force microscope to measure the surface roughness of the saw blade, record the height distribution of the microscopic surface through three-dimensional scanning, and the surface roughness parameter R s is:

[0168]

[0169] Among them, z i is the height of the scanning point, is the average height, and n is the total number of scanning points;

[0170] S64. Combine the measurement results of the optical interferometer and the atomic force microscope to generate a comprehensive evaluation report, and use the compliance C f and the statistical results of the roughness parameter R s for subsequent adjustment of the shape optimization model, and record the measurement data for production closed-loop optimization.

[0171] S7. Integrate an automated shape detection and feedback adjustment module through a programmable logic controller to perform real-time correction of machining deviations and achieve dynamic maintenance of shape consistency during batch production.

[0172] In this embodiment, S7 specifically includes:

[0173] S71. Use a programmable logic controller to integrate a shape detection module, obtain the shape and surface data of the machined saw blade through an optical interferometer and an atomic force microscope, and transmit them to the programmable logic controller system for data processing in real time;

[0174] S72. Perform preprocessing on the detection data, including denoising and standardization, and calculate the machining deviation Δz(x, y):

[0175] Δz(x , y) = z(x , y) - z opt (x, y);

[0176] Among them, z(x, y) is the actually measured height data, and z opt (x, y) is the theoretical height of the optimized design;

[0177] S73. Input the machining deviation Δz(x, y) into the feedback adjustment module, and use the dynamic deviation distribution model to construct a real-time correction algorithm:

[0178]

[0179] Among them, represents the deviation gradient, quantifying the change rate of the machining deviation within the machining area A;

[0180] S74. Dynamically optimize the machining parameters in combination with the feedback adjustment results, including the laser focus position, scanning speed, and pulse energy, reduce the deviation gradient, and achieve real-time dynamic maintenance of shape consistency;

[0181] S75. Evaluate the shape consistency of the saw blades produced in batches, calculate the consistency index C u , and generate a shape detection and consistency evaluation report:

[0182]

[0183] Among them, A is the machining area, |Δz(x,y)| is the absolute value of the machining deviation, and z opt (x,y) is the optimized design height.

[0184] Example 1:

[0185] To verify the feasibility of the present invention, the present invention is applied to the saw blade production line of Company B, a high-end precision tool manufacturing enterprise. This enterprise mainly provides high-precision cutting tools for the aerospace and medical device industries and has extremely high requirements for the shape accuracy, production efficiency, and durability of saw blades. The traditional saw blade manufacturing process has been difficult to meet its high standards. Company B decides to introduce the neural network-based nano-level saw blade shape optimization and production method proposed by the present invention to transform its existing production line technically.

[0186] The saw blade production line of Company B needs to produce a large number of nano-level saw blades for precision machining. These saw blades need to meet the following requirements: the accuracy of the cutting path reaches within 10 nanometers, the conformity of the shape parameters with the design values is not less than 99%, and the shape consistency is maintained during mass production. However, due to the traditional design and processing methods relying on empirical models, there are problems such as insufficient shape optimization, large machining deviations, insufficient simulation analysis, and poor batch production consistency, which seriously restrict the production efficiency and product quality.

[0187] In the actual application of Company B, first, according to the performance requirements of the saw blade, the neural network optimization module of the present invention was used to redesign the shape of the saw blade. The convolutional neural network and the generative adversarial network were used in combination. Through multi-objective optimization of cutting efficiency, durability, and production accuracy, the optimized shape parameters of the saw blade were obtained. After the optimization was completed, femtosecond laser nanomachining technology was used for high-precision machining, and a real-time shape detection and surface roughness evaluation of the machined saw blade were carried out by an optical interferometer and an atomic force microscope. Combined with the PLC system, dynamic correction of machining deviation and maintenance of consistency in batch production were achieved.

[0188] During the three-month production, Company B conducted a comparative analysis of the effects of the production line adopting the technology of the present invention and the traditional production line. The specific data is shown in Table 1 below:

[0189] Table 1 Comparison of the application effects of the present invention in the saw blade production line of Company B

[0190]

[0191] As can be seen from Table 1 above, in the embodiment, before the adoption of the present invention, the average shape conformity of each batch of saw blades of Company B was 92.3%. After the application of the present invention, this data increased to 99.4%. Through femtosecond laser machining combined with real-time detection and dynamic correction, the deviation of the cutting path of the saw blade was reduced from ±50 nm to ±8 nm. The average value of the surface roughness was reduced from 45 nm to 12 nm, significantly improving the machining quality of the saw blade. In terms of production efficiency, the traditional process took an average of 10 hours to complete a batch of production, while after adopting the technology of the present invention, it was shortened to 7 hours, and the total production efficiency increased by 30%.

[0192] In a specific case, the machining shape of a certain batch of saw blades was detected by an optical interferometer. Compared with the optimized design, it was found that the average shape deviation was ±6 nm. Through real-time feedback adjustment, the conformity was finally increased to 99.7%. In the surface roughness evaluation, the key areas of the saw blade were scanned by an atomic force microscope, and it was found that the roughness parameter \(R_s\) was reduced to less than 10 nm, providing a guarantee for reducing friction and extending the life of the saw blade during the cutting process.

[0193] In the mass production stage, through the dynamic feedback adjustment module of the PLC, the production data of different batches were monitored and corrected in real time, so that the consistency of the shape parameters of the saw blade was increased from 85.6% to 98.7%. In the durability test, the average service life of each saw blade was increased by about 30%, reducing the replacement frequency and usage cost of cutting tools for customers.

[0194] It can be seen from these data that the present invention has significantly improved the limitations of traditional processes in terms of design, processing, inspection, and production consistency maintenance. By using neural networks to optimize shape parameters, multi-scale simulation analysis of saw blade performance, and the combination of femtosecond laser processing and dynamic feedback, not only the shape accuracy and surface quality of the saw blade have been improved, but also the processing deviation and resource waste in production have been reduced. In addition, the degree of intelligence of the production line has been improved, reducing the need for manual intervention and providing technical support for large-scale industrial applications.

[0195] The present invention realizes the production goals of high precision and high efficiency by combining neural network optimization algorithms, femtosecond laser nanofabrication technology, optical detection and feedback control technology, comprehensively improving the performance and quality of saw blades. In applications, it has not only successfully solved the core problems of traditional processes, but also provided advanced technical support and innovative directions for the further development of high-end precision tool manufacturing.

[0196] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, making equivalent substitutions or changes, should be covered by the protection scope of the present invention.

Claims

1. A method for optimizing and producing nano-scale saw blade shape using a neural network, characterized in that: The following steps are involved: S1. Use scanning electron microscopy and 3D confocal microscopy to perform high-resolution imaging and modeling of the initial shape of the nano-scale saw blade, and extract key geometric parameters including tooth angle, edge curvature radius, and tooth peak spacing; S2. Construct a shape optimization model based on a convolutional neural network, taking key geometric parameters and material properties as input variables, and combining an adaptive weighted multi-objective optimization algorithm to optimize saw blade cutting efficiency, durability, and production accuracy; S3. Generate a virtual saw blade shape dataset using a generative adversarial network, train an optimization model based on experimental data, and improve the prediction accuracy of the optimization results by adjusting the weights through multiple iterations; S4. Use finite element analysis and molecular dynamics simulation technology to verify the multi-scale performance of the optimized saw blade shape and analyze its stress distribution and wear behavior under actual cutting conditions; S5, based on femtosecond laser nano-machining technology, high-precision manufacturing is achieved according to the optimized saw blade parameters, so that the cutting path accuracy is controlled within 10 nanometers; S6. Use optical interferometer and atomic force microscope to measure the shape and evaluate the surface roughness of the processed saw blade, so that the conformity between the shape and the optimized design is more than 99%; S7. Through the programmable logic controller, the automatic shape detection and feedback adjustment module is integrated to correct the processing deviation in real time, so as to realize the dynamic maintenance of shape consistency in the mass production process.

2. The method for optimizing and producing nano-scale saw blade shape using a neural network according to claim 1, characterized in that: The S1 specifically includes: S11, using a scanning electron microscope to perform multi-angle high-resolution imaging of the initial sample of the nano-scale saw blade to obtain a two-dimensional projection image of the initial shape of the saw blade; S12, stitching and superimposing the two-dimensional projection images, and generating a complete three-dimensional contour model of the initial shape of the saw blade through an image processing algorithm; S13, using three-dimensional confocal microscopy measurement technology to measure the surface of the saw blade with nanometer-level resolution, and record the height data z(x, y) of the microscopic feature points on the surface, where x and y are the horizontal and vertical coordinates of the measurement area, and z is the height data; S14, matching the z(x,y) data obtained by microscopic measurement with the three-dimensional contour model generated by the scanning electron microscope, adjusting the fitting parameters of the three-dimensional model using an error minimization algorithm, and determining the accuracy of the measurement data; S15. Extract key geometric parameters from the fitted three-dimensional model through a geometric analysis algorithm, including tooth profile angle θ, cutting edge curvature radius r, and tooth peak spacing d: Where, κ is the local curvature of the cutting edge, and d is the horizontal distance between adjacent tooth peaks; S16. Perform data sorting and statistical analysis on the extracted geometric parameters, store them as input parameters of the shape optimization model, and record various characteristic parameters of the initial shape.

3. The method for optimizing and producing nano-scale saw blade shape using a neural network according to claim 1, characterized in that: The S2 specifically includes: S21, define the objective function F(x) of saw blade shape optimization, where x=[x1, x2, ..., x n ] are the key geometric parameters of the saw blade, including the tooth profile angle θ, the edge curvature radius r and the tooth peak spacing d: F(x)=w1f1(x)+w2f2(x)+w3f3(x); Among them, f1(x), f2(x) and f3(x) represent the performance index functions of cutting efficiency, durability and production accuracy respectively, w1, w2, w3 are weight coefficients, satisfying w1+w2+w3=1; S22. Establish a shape optimization model with a convolutional neural network as the core, the input of the model is a set of geometric parameters and material performance parameters, and the output is an estimated value of the objective function F(x); S23, generating an initial training data set, mapping key geometric parameters and material properties to actual performance index values ​​through experimental and simulation results, and standardizing the data; S24. Use generative adversarial networks to expand the training data set, generate diverse virtual geometric parameter combinations and corresponding material performance parameters through adversarial networks, and enhance the generalization ability of the model; S25, using an adaptive weighted multi-objective optimization algorithm to train the model, optimizing the objective function value by dynamically adjusting weights w1, w2, and w3, updating network parameters, and obtaining an optimized neural network; S26. Use the optimized shape model for performance prediction, verify its prediction accuracy for saw blade cutting efficiency, durability and production precision, and record the optimization results.

4. The method for optimizing and producing nano-scale saw blade shape using a neural network according to claim 1, characterized in that: The S3 specifically includes: S31, constructing a generative adversarial network model, including a generator G and a discriminator D, wherein the generator G is used to generate virtual saw blade shape data, and the discriminator D is used to determine whether the input data is real data; S32, the saw blade shape geometric parameter data x=[x1, x2, ..., x n ] and the corresponding material performance parameters p=[p1,p2,...,p m ] is constructed into the initial real data set T r , initialize the parameters of the generative adversarial network model; S33. Define the objective function L of the generator G : Where z is the random noise vector input by the generator, and G(z) represents the virtual saw blade shape data output by the generator; S34. Define the objective function L of the discriminator D : L D =-E[log(D(x))]-E[log(1-D(G(z)))]; Where D(x) represents the output probability of the discriminator for real data, and D(G(z)) represents the output probability of the discriminator for virtual data; S35, use the stochastic gradient descent method to alternately optimize the parameters of the generator and the discriminator, by minimizing L G and L D Make the virtual saw blade shape data generated by the generator close to the real data; S36. In the process of generative adversarial network training, the constraints of key geometric parameters are introduced: Among them, x′ is the virtual data generated by the generator, x is the real data, θ is the tooth profile angle, r is the edge curvature radius, and d is the tooth peak spacing; S37, compare the generated virtual saw blade shape data set with the real data set T r Merge and build an enhanced dataset T a , and T a Used to train convolutional neural network models and update network weights through multiple iterations.

5. The method for optimizing and producing nano-scale saw blade shape using a neural network according to claim 1, characterized in that: The S4 specifically includes: S41. Construct a three-dimensional geometric model based on the optimized saw blade shape parameters, define the dynamic response characteristics of the material, and the material stress tensor σ is: Where V is the material volume, N is the number of particles in the unit, and m i is the particle mass, v i is the particle velocity, r i is the position vector, f i is the force vector, Represents a tensor outer product operation; S42, applying dynamic cutting load to the optimized model, solving the stress distribution field of the material during the cutting process by using finite element analysis technology, recording the maximum stress concentration point, and calculating the local thermal stress generated during the cutting process by thermal-mechanical coupling analysis; S43. Introduce the material interface interaction model in the molecular dynamics simulation, define the intermolecular potential function U(r), and calculate the friction force F between the saw blade surface molecules and the cutting material molecules ij : U(r)=Ae -αr -Be -βr ; Among them, r is the distance between molecules, A and B are potential function parameters, α and β are attenuation factors, represents the gradient operation, jr ij is the distance between molecules i and j; S44, simulate the atomic-level motion trajectory of the saw blade under dynamic cutting conditions and record the surface wear depth Δh: Where, ΔE k is the energy change of the surface atoms in the kth layer, τ k is the corresponding time step, n is the total number of time steps; S45. Combining the results of finite element analysis and molecular dynamics simulation, a multi-scale evaluation model of saw blade cutting performance is constructed to analyze stress concentration areas and hot spots of wear behavior; S46. Generate a verification report, feed the results back to the shape optimization model, and improve the objective function and optimization parameters based on the verification report.

6. The method for optimizing and producing nano-scale saw blade shape using a neural network according to claim 1, characterized in that: The S5 specifically includes: S51, designing a processing path model according to the optimized geometric parameters of the saw blade, and using a computer-aided design tool to generate a three-dimensional processing path, wherein the path parameters include cutting path width, depth, and processing speed; S52, setting processing parameters based on femtosecond laser nano-processing technology, including laser pulse energy, repetition frequency and scanning speed, and uploading the parameters to the processing control system; S53. During the processing, selective material removal is achieved by controlling the laser focus position and scanning path, so that the processing surface accuracy is controlled within the design target range; S54, using a real-time monitoring system to detect surface topography deviations in the processing area, adjusting laser parameters and paths through feedback control, and dynamically correcting deviations during processing; S55. Use optical interferometry technology to measure the processed surface in real time, evaluate the consistency of surface shape with design parameters, and monitor the processing accuracy of key areas; S56. Optimize and iterate the processing path based on the processing results and monitoring data to ensure that the accuracy of the cutting path is within 10 nanometers. After the processing is completed, conduct a comprehensive shape inspection and surface quality assessment on the processed saw blade and record the inspection data.

7. The method for optimizing and producing nano-scale saw blade shape using a neural network according to claim 1, characterized in that: The S6 specifically includes: S61, using an optical interferometer to measure the surface shape of the processed saw blade, generate an interference diagram of the surface profile, and record height data z(x, y) of the surface shape, where x and y are two-dimensional space coordinates and z(x, y) is the surface height value; S62. Calculate the conformity of the surface shape through optical interference data C f : Among them, z opt (x,y) is the theoretical height of the optimized design, A is the area of ​​the measurement area, |z(x,y)-z opt (x,y)| represents the deviation between the actual height and the theoretical height; S63, use atomic force microscopy to measure the surface roughness of the saw blade, record the height distribution of the microscopic surface through three-dimensional scanning, and the surface roughness parameter R s for: Among them, z i is the height of the scanning point, is the average height, n is the total number of scanning points; S64, combine the measurement results of optical interferometer and atomic force microscope to generate a comprehensive evaluation report, and f and roughness parameter R s The statistical results are used for subsequent shape optimization model adjustment, and the measurement data are recorded for production closed-loop optimization.

8. The method for optimizing and producing nano-scale saw blade shape using a neural network according to claim 1, characterized in that: The S7 specifically includes: S71, using a programmable logic controller integrated shape detection module, obtaining the shape and surface data of the processed saw blade through an optical interferometer and an atomic force microscope, and transmitting the data to the programmable logic controller system in real time for data processing; S72, pre-process the detection data, including denoising and standardization, and calculate the processing deviation Δz(x,y): Δz(x,y)=z(x,y)-z opt (x,y); Among them, z(x,y) is the actual measured height data, z opt (x, y) is the theoretical height of the optimized design; S73, input the processing deviation Δz(x,y) into the feedback adjustment module, and use the dynamic deviation distribution model to build a real-time correction algorithm: in, represents the deviation gradient, Quantify the rate of change of machining deviation within machining area A; S74, dynamically optimize processing parameters including laser focus position, scanning speed and pulse energy based on feedback adjustment results, reduce deviation gradient, and achieve real-time dynamic maintenance of shape consistency; S75. Evaluate the shape consistency of mass-produced saw blades and calculate the consistency index C u , and generate shape detection and consistency assessment reports: Among them, A is the processing area, |Δz(x,y)|| is the absolute value of the processing deviation, and z opt (x,y) is the optimized design height.

Images