Method for optimizing production of micro saw blade by using genetic algorithm

Abstract

The invention discloses a method for optimizing micro saw blade production by using a genetic algorithm. The method comprises the following steps: S1, establishing a virtual simulation environment; s2, parameter optimization is carried out in the virtual simulation environment, and a plurality of virtual production schemes are generated; s3, selecting an optimal scheme through high-dimensional topology hypersurface mapping and an asymmetric singular value perturbation technology; s4, applying the optimal scheme to actual production, and analyzing monitoring data by using a Schrodinger-Newman geometric gravitational field exceeding method; s5, when the deviation exceeds a preset threshold value, triggering a self-repairing mechanism, and performing optimization iteration again; s6, actual production parameters are adjusted based on the repairing scheme; s7, updating the digital twinborn model in real time to reflect the latest production condition; s8, analyzing historical data in combination with a machine learning algorithm, and optimizing future production parameter setting; and S9, circularly executing the steps S4 to S8 to achieve the production optimization target, the precision of the micro saw blade production process is improved, and continuous optimization and stability of the production quality are ensured.

Description

Technical Field

[0001] The invention relates to the field of industrial manufacturing and production optimization, and in particular to a method for optimizing the production of micro saw blades by using a genetic algorithm. Background Art

[0002] In the field of modern industrial manufacturing, micro saw blades, as an important precision tool, are widely used in fine cutting and processing in the electronics, medical, and machinery industries. With the advancement of technology and the continuous growth of market demand, how to improve the production efficiency and product quality of micro saw blades has become a major challenge facing manufacturing companies. The existing micro saw blade production technology mainly relies on experienced operators to manually adjust the production parameters and gradually optimize the production process through multiple tests. Although this method guarantees the quality of the product to a certain extent, it also has many shortcomings.

[0003] First, the existing manual adjustment and test methods have great uncertainty. Due to the various factors involved in the production process of micro saw blades, such as material properties, cutting parameters, heat treatment parameters, and production environment variables, the complex coupling relationship between these factors makes the optimization of the production process extremely complicated. In traditional processes, operators can only gradually find out the appropriate production parameters through repeated experiments and experience accumulation, but this method is not only time-consuming and labor-intensive, but also easily affected by human factors, resulting in low production efficiency and unstable product quality.

[0004] Secondly, the existing production control system lacks sufficient intelligence and adaptability. In the actual production process of micro saw blades, the physical properties of the materials and environmental conditions often change dynamically. These changes may lead to various abnormal conditions in the production process, reduced cutting accuracy of the saw blades, and poor cutting surface quality. However, traditional production control systems are unable to perceive and respond to these changes in real time, and often can only correct deviations in production through post-adjustments. This passive adjustment method not only prolongs the production cycle, but may also lead to the production of a large number of unqualified products, wasting production resources.

[0005] In addition, with the development of information technology, advanced technologies such as digital twins and virtual simulation have been gradually applied to the manufacturing industry, providing new possibilities for optimizing the production process. However, the existing micro-saw blade production process has not fully utilized these emerging technologies. Data collection, model building, and parameter optimization in the production process mostly rely on static, offline analysis methods, which makes it difficult to achieve real-time optimization and dynamic adjustment. This not only limits the intelligent development of production processes, but also hinders the further improvement of production efficiency and product quality.

[0006] Based on the above problems, it is urgent to use advanced optimization algorithms to intelligently optimize the production process of micro saw blades. In recent years, genetic algorithms, as an optimization algorithm that simulates the natural evolution process, have been widely used in solving complex optimization problems due to their strong global search capabilities and good adaptability. However, traditional genetic algorithms still have some limitations when applied to industrial production optimization. Specifically, they are manifested in the following aspects:

[0007] 1. The initial parameter selection and population initialization of genetic algorithms are difficult to optimize: In practical applications, the optimization effect of genetic algorithms depends largely on the setting of initial parameters. If the initial population is not selected properly, the algorithm may fall into a local optimum and fail to fully exert its global search capabilities. In the prior art, the parameter selection and population initialization of genetic algorithms are mostly based on experience, lacking a systematic and intelligent adjustment mechanism.

[0008] 2. Insufficient adaptability of genetic algorithms in multi-objective optimization: The production process of micro-saw blades involves multiple process parameters that affect each other, such as cutting speed, cutting force, and heat treatment temperature. The optimization of these parameters is often a complex multi-objective problem. When dealing with multi-objective optimization, existing genetic algorithms may find it difficult to achieve the optimal solution due to improper fitness function design or inflexible crossover and mutation operations, resulting in incomplete optimization of the production process.

[0009] 3. Lack of adaptive adjustment capabilities to dynamic environmental changes: During the production process, material properties, ambient temperature, and equipment status will change over time. Traditional genetic algorithms lack the ability to adapt to these dynamic changes and are difficult to maintain optimization effects in a dynamic environment. In addition, existing genetic algorithms have a slow response speed when processing feedback information in actual production and are unable to adjust optimization strategies in a timely manner, resulting in reduced optimization effects.

[0010] 4. The computational complexity of existing algorithms is high, making it difficult to apply them to large-scale data: With the advancement of Industry 4.0, the amount and dimensions of data generated in the production process continue to increase. How to efficiently apply genetic algorithms for optimization in large-scale data has become a major challenge. Existing genetic algorithms operate slowly on large-scale data sets, especially when dealing with high-dimensional optimization problems. The computational complexity increases exponentially, making it difficult to meet the needs of real-time optimization.

[0011] In summary, the prior art has certain defects and deficiencies in the optimization of the micro saw blade production process, and an improved intelligent optimization method is urgently needed, which can combine virtual simulation and digital twin technology to monitor and dynamically adjust the production process in real time, improve production efficiency and product quality stability. To this end, the present invention proposes a method for optimizing the production of micro saw blades using an improved genetic algorithm, aiming to achieve global optimization and intelligent control of the micro saw blade production process by introducing advanced adaptive adjustment mechanisms and dynamic feedback control, thereby overcoming the defects of the prior art.

[0012] Therefore, how to provide a method for optimizing the production of micro saw blades using genetic algorithms is an urgent problem to be solved by those skilled in the art. Summary of the invention

[0013] One purpose of the present invention is to propose a method for optimizing the production of micro saw blades using genetic algorithms. The present invention optimizes production parameters by introducing a virtual simulation environment and digital twin technology, combined with an adaptive genetic algorithm, to generate an optimal production plan. Real-time monitoring and self-repair of the actual production process are achieved through high-dimensional topological hypersurface mapping, asymmetric singular value perturbation technology, and a method that transcends Schrödinger-Newman geometric gravity field. The present invention has the advantages of high production process accuracy, high optimization efficiency, and stable product quality.

[0014] A method for optimizing micro saw blade production using a genetic algorithm according to an embodiment of the present invention comprises the following steps:

[0015] S1. Establish a virtual simulation environment and construct a digital twin model for micro saw blade production. The digital twin model includes simulation data of material properties, cutting parameters, heat treatment parameters and production environment variables;

[0016] S2. In a virtual simulation environment, an adaptive genetic algorithm is used for preliminary parameter optimization. The genetic algorithm introduces an adaptive crossover mechanism based on Arnold mapping chaotic sequence and a mutation rate adjustment mechanism based on hypersurface Weyl transformation to generate multiple virtual production plans.

[0017] S3. Perform fitness evaluation on multiple generated virtual production plans. The fitness evaluation is based on high-dimensional topological hypersurface mapping, and the stability and adaptability of the production plan in high-dimensional space are analyzed through asymmetric singular value perturbation technology, so as to optimize and select the virtual production plan with the highest fitness for actual production use;

[0018] S4. Apply the selected optimal virtual production solution to the actual production process, and monitor the deviation between the production parameters and the virtual model in real time through a distributed sensor network, record and analyze abnormal situations, and integrate the multi-dimensional data of the production process in real time through the monitoring system. And analyze the monitoring data by transcending the Schrödinger-Newman geometry gravity field method to identify production deviations and abnormal dynamics;

[0019] S5. When it is detected that the deviation between the actual production and the virtual model exceeds a predetermined threshold, the self-repair mechanism is triggered, and the improved genetic algorithm is reapplied in the virtual simulation environment through the feedback mechanism to perform optimization iterations and generate a new production plan that adapts to the current actual conditions;

[0020] S6. Based on the repaired production plan, the actual production parameters are automatically adjusted and applied to the production process in real time to optimize the continuity of production and the stability of product quality;

[0021] S7, update the digital twin model in real time to reflect the latest production conditions and optimization results;

[0022] S8. During the optimization process, the machine learning algorithm is used to analyze the historical production data, optimize the future production parameter settings, and optimize the prediction ability of the genetic algorithm;

[0023] S9, looping through steps S4 to S8 until the preset production optimization goal is reached, and finally outputting an optimized micro saw blade product.

[0024] Optionally, the S1 specifically includes:

[0025] S11. Obtaining material property data involved in the production process of micro saw blades. The material property data includes but is not limited to the density, hardness, toughness, elastic modulus and thermal expansion coefficient of the material. The material property data is obtained through experimental measurement or existing data tables, and is represented by the letter set M = {m1, m2, ..., m n} means, where m i represents the specific characteristics of the i-th material;

[0026] S12, determining the cutting parameters of the micro saw blade, including cutting speed v, cutting depth d, feed rate f and cutting force F, the cutting parameters are determined by combining the formula:

[0027] F=k·A·v:

[0028] Where k is the cutting coefficient of the material, A is the cutting area, and v is the cutting speed;

[0029] S13, obtaining and modeling the heat treatment parameters in the production of micro saw blades, the heat treatment parameters including heating temperature T, heating time t, cooling rate r and holding time t p, the parameters are expressed by formula combination, and the cooling rate r is expressed by the formula:

[0030]

[0031] Where T f and T i are the final temperature and the initial temperature respectively, t f and t i are the corresponding time points respectively;

[0032] S14. Establish a simulation model of production environment variables, where the production environment variables include but are not limited to the ambient temperature T e , ambient humidity H, equipment vibration amplitude V and air flow velocity v a , variables are monitored in real time by sensors and input into the simulation model in the form of time series data. The simulation model is based on discrete time series:

[0033] X(t)={T e (t), H(t), V(t), v a (t)};

[0034] Where t is the time step;

[0035] S15. Integrate the material properties, cutting parameters, heat treatment parameters and production environment variable data obtained in the above sub-steps S1.1 to S1.4 to form an input data set for the digital twin model. The data set is modeled through simulation software and generates corresponding simulation data to simulate the behavior and response in the actual production process.

[0036] Optionally, the S2 specifically includes:

[0037] S21. According to the input data set of the digital twin model described in claim 2, initialize the population of the genetic algorithm, the population consists of multiple individuals, each individual represents a possible production plan, and the individual is represented as a high-dimensional vector Among them, m i represents the material property vector, v i ,d i 、f i 、F i represents the cutting parameter vector, T i ,t i 、r i , represents the heat treatment parameter vector, H i 、V i , Represents the production environment variable vector;

[0038] S22. Apply an adaptive crossover mechanism based on Arnold map chaotic sequence to perform crossover operation on individuals in the initial population, with a crossover rate C r The crossover between individuals is disturbed by generating an Arnold map chaotic sequence and then adaptively adjusted:

[0039]

[0040] Among them, C AMCS (P i , P j ) is a chaotic sequence generated by Arnold mapping, which is used to measure the individual P i and P j The chaotic complexity of the crossover operation, Arnold mapping:

[0041]

[0042] in, Represents the current two-dimensional coordinate point, It represents the new coordinate point obtained after applying Arnold mapping, that is, the position of the mapped point on the two-dimensional plane. The complexity of the mapping optimization crossover operation and the diversity of the population form an adaptive feedback;

[0043] S23. After the adaptive crossover mechanism takes effect, a mutation rate adjustment mechanism based on hypersurface Weyl transformation is used to perform mutation operations on the individuals generated by the crossover. The mutation rate M r Individual quantum states are manipulated by applying Weyl transformations on high-dimensional hypersurfaces and adjusted by their geometric invariants:

[0044]

[0045] in, For individual P i Weyl transformation operator in high-dimensional hypersurfaces, acting on the density matrix is the quantization constant, is the geometric invariant obtained after Weyl transformation;

[0046] S24. Evaluate the fitness of the mutated individuals, and perform selection operations based on the algebraic K-group in K-theory. Classify the individuals through the calculation of the algebraic K-group, and optimize the population based on the homotopy properties of the K-group:

[0047]

[0048] in, For individual P i Classification in algebraic K-groups with homotopy dimension n, λ nis the weight coefficient related to the classification level, Representing Algebraic K-Groups The rank of a classification is the importance or influence of the classification in the algebraic topological space. The rank is usually used to quantify the relative complexity or dimension of a classification in the structure. K-group (P i ) This is the fitness function used to evaluate the rank of the production solution;

[0049] S25. Repeat steps S2.2 to S2.4 until a preset number of iterations is reached or the fitness of the population converges to a set threshold, and multiple virtual production plans are generated.

[0050] Optionally, the S3 specifically includes:

[0051] S31, performing preliminary fitness evaluation on multiple virtual production plans generated in step S2, wherein the fitness evaluation is based on high-dimensional topological hypersurface mapping, which captures the relationship between plans in multi-dimensional space by embedding the fitness of each production plan into a high-dimensional topological hypersurface;

[0052] S32. Apply asymmetric singular value perturbation technology to perform perturbation analysis on the embedded topological hypersurface and analyze the stability and adaptability of the production plan in high-dimensional space:

[0053]

[0054] Among them, Δσ n represents the change of the nth singular value under disturbance conditions, The production plan P for singular value pairs i The partial derivative of , which measures the sensitivity of the scheme to singular values, is an asymmetric perturbation matrix applied to individual P i Introducing asymmetry, λ j is the disturbance intensity coefficient, which is used to control the disturbance effect in different directions. Represents plan P i The higher-order partial derivative tensor of describes the propagation effect of the disturbance in high-dimensional space;

[0055] S33. Based on the quantum conformal field renormalization method, the fitness of the scheme after asymmetric singular value perturbation is reorganized. Based on the quantum conformal field renormalization method, the fitness of each production scheme is redefined and optimized through the renormalization group flow in quantum conformal field theory:

[0056]

[0057] in, For production plan P i The renormalized fitness value of is the effective Lagrangian of scheme Pi, based on the scalar field φ(P i ) and the metric tensor g μv definition, To reduce Planck's constant and control the scale of quantum effects, is the volume element in the quantum state space, μ is the integral measure;

[0058] S34. Based on the renormalized fitness value in step S3.3, select the virtual production plan with the highest fitness.

[0059] Optionally, the S4 specifically includes:

[0060] S41. Apply the optimal virtual production solution selected in step S3 to the actual production process, and monitor the multi-dimensional parameters of the production process, such as temperature, pressure, vibration and humidity, in real time through a distributed sensor network. The real-time data is integrated into a high-dimensional spatiotemporal data set and continuously compared with the virtual model to detect any deviation between the actual production and the virtual model.

[0061] S42. Using the method beyond Schrödinger-Newman geometric gravity field, real-time monitoring data is embedded in a complex geometric space with nonlinear characteristics. By integrating the Schrödinger equation and the Newman geometric gravity field, the complex interactions and non-equilibrium dynamics hidden in the monitoring data are captured to identify the deviation between actual production and the virtual model:

[0062]

[0063] in, represents the Lagrangian universal function beyond the Schrödinger-Newman geometric gravity field, which is used to describe the behavior of real-time monitoring data in the complex geometric space. Ψ is a complex-valued state function, which represents the distribution of sensor data in the geometric field. is the curvature tensor, reflecting the bending and deformation characteristics of the data in the geometric space, is the Kronecker delta symbol, regulating the local asymmetry of the geometric space, and ω b ∧ω a is the outer product of 1-form, describing the interaction between geometric flows, Λ is the cosmological constant, controlling the global characteristics of the geometric space, and g is the metric tensor of the geometric space, controlling the mobility and distribution characteristics of data in space;

[0064] S43. When the deviation between the monitoring data and the virtual model exceeds a predetermined threshold, the quantum fiber cluster state switching mechanism is activated to dynamically adjust the virtual model. The quantum fiber cluster state switching mechanism optimizes the structural parameters of the virtual model by introducing non-trivial topological switching in the high-dimensional fiber cluster path:

[0065]

[0066] in, is the evolution operator of the production parameter P in the switching of quantum fiber bundles, represents the sum of all possible fiber bundle paths, To reduce Planck's constant and control the scale of quantum effects, γ fib is a fiber bundle path, which is used to describe the evolution trajectory of the virtual model in the high-dimensional quantum state space. is the standard potential field, describing the quantum phase change in the fiber bundle path, is the curvature tensor of the fiber bundle, describing the high-dimensional geometric properties of the virtual model;

[0067] S44. Record all deviations between actual production and the virtual model, use multi-scale quantum state analysis methods to analyze abnormal situations, and use multi-scale quantum state analysis technology to track and identify factors that cause deviations in the production process.

[0068] Optionally, the S5 specifically includes:

[0069] S51. When the deviation between the real-time monitoring data in the actual production process and the virtual model exceeds a predetermined threshold, the self-repair mechanism is automatically triggered to collect real-time production data through a distributed sensor network, identify and record the specific source and nature of the deviation, and feed the information back to the virtual simulation environment;

[0070] S52. In a virtual simulation environment, the improved genetic algorithm is reapplied for optimization iteration according to the feedback deviation information. The genetic algorithm generates an updated production plan by adjusting the initial conditions, crossover and mutation rates of the population;

[0071] S53. After generating a new production plan, immediately verify it, evaluate its performance in the virtual environment, select the best production plan through multiple iterations, and apply it to the actual production process;

[0072] S54. After applying the new solution, the deviation between the actual production parameters and the virtual model is monitored in real time, and the new solution can be optimized to be implemented stably.

[0073] Beneficial Effects

[0074] 1. Significant improvement in production efficiency: The present invention can quickly generate multiple virtual production plans by constructing a digital twin model in a virtual simulation environment and preliminarily optimizing production parameters in combination with an improved genetic algorithm. The optimal plan can be predicted and selected before actual production, reducing the repeated trials and adjustment processes in traditional methods and greatly improving production efficiency.

[0075] 2. High accuracy and stability of the production process: By using high-dimensional topological hypersurface mapping and asymmetric singular value perturbation technology, the present invention can deeply analyze the stability and adaptability of multiple virtual production plans, thereby optimizing and screening out the best plan to ensure the precise control of various parameters in actual production. Combined with distributed sensor networks and the method of transcending Schrödinger-Newman geometric gravity field, it can monitor multi-dimensional parameters in the production process in real time and detect and analyze abnormal situations in a timely manner to ensure the stability of the production process.

[0076] 3. Dynamic response and self-repair capability: When there is a deviation between the actual production and the virtual model, the present invention can automatically trigger the self-repair mechanism, reapply the improved genetic algorithm in the virtual simulation environment through the feedback mechanism for optimization iteration, generate a new production plan and adjust the production parameters in real time. This dynamic response and self-repair capability effectively prevents quality problems caused by environmental changes or fluctuations in material properties during the production process, and further ensures the continuity of production and product quality.

[0077] 4. Continuous optimization and prediction capabilities: By updating the digital twin model in real time and analyzing historical production data in combination with machine learning algorithms, the present invention has the ability to continuously optimize and predict future production parameters. This continuous learning and adjustment process can keep the production process in an optimal state in a constantly changing environment, and enhance the prediction capabilities of the genetic algorithm, providing more accurate parameter settings for future production.

[0078] 5. Reduce production costs: Since the present invention reduces human intervention and repeated experiments in the production optimization process, it shortens the production cycle and avoids waste of resources. At the same time, through real-time monitoring and automatic adjustment mechanisms, it effectively reduces the production of unqualified products, thereby reducing the overall production cost.

[0079] To sum up, the present invention not only significantly improves the efficiency and quality of micro saw blade production by introducing advanced adaptive genetic algorithms, virtual simulation, digital twins, dynamic feedback and self-repair mechanism technologies, but also has strong dynamic response and continuous optimization capabilities, which comprehensively improves the intelligence level of the production process and achieves the beneficial effects of optimizing production costs and ensuring product quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] 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:

[0081] Figure 1 A summary of a method for optimizing micro saw blade production using a genetic algorithm proposed by the present invention;

[0082] Figure 2The figure of claim 2 of the method for optimizing the production of micro saw blades by using a genetic algorithm proposed by the present invention;

[0083] Figure 3 The figure of claim 3 of the method for optimizing the production of micro saw blades by using genetic algorithm proposed by the present invention

[0084] Figure 4 The figure of claim 4 of the method for optimizing the production of micro saw blades using genetic algorithm proposed by the present invention. DETAILED DESCRIPTION

[0085] 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.

[0086] refer to Figure 1-4 , a method for optimizing micro saw blade production using genetic algorithm, comprising the following steps:

[0087] S1. Establish a virtual simulation environment and construct a digital twin model for micro saw blade production. The digital twin model includes simulation data of material properties, cutting parameters, heat treatment parameters and production environment variables;

[0088] S2. In a virtual simulation environment, an adaptive genetic algorithm is used for preliminary parameter optimization. The genetic algorithm introduces an adaptive crossover mechanism based on Arnold mapping chaotic sequence and a mutation rate adjustment mechanism based on hypersurface Weyl transformation to generate multiple virtual production plans.

[0089] S3. Perform fitness evaluation on multiple generated virtual production plans. The fitness evaluation is based on high-dimensional topological hypersurface mapping, and the stability and adaptability of the production plan in high-dimensional space are analyzed through asymmetric singular value perturbation technology, so as to optimize and select the virtual production plan with the highest fitness for actual production use;

[0090] S4. Apply the selected optimal virtual production solution to the actual production process, and monitor the deviation between the production parameters and the virtual model in real time through a distributed sensor network, record and analyze abnormal situations, and integrate the multi-dimensional data of the production process in real time through the monitoring system. And analyze the monitoring data by transcending the Schrödinger-Newman geometry gravity field method to identify production deviations and abnormal dynamics;

[0091] S5. When it is detected that the deviation between the actual production and the virtual model exceeds a predetermined threshold, the self-repair mechanism is triggered, and the improved genetic algorithm is reapplied in the virtual simulation environment through the feedback mechanism to perform optimization iterations and generate a new production plan that adapts to the current actual conditions;

[0092] S6. Based on the repaired production plan, the actual production parameters are automatically adjusted and applied to the production process in real time to optimize the continuity of production and the stability of product quality;

[0093] S7, update the digital twin model in real time to reflect the latest production conditions and optimization results;

[0094] S8. During the optimization process, the machine learning algorithm is used to analyze the historical production data, optimize the future production parameter settings, and optimize the prediction ability of the genetic algorithm;

[0095] S9, looping through steps S4 to S8 until the preset production optimization goal is reached, and finally outputting an optimized micro saw blade product.

[0096] In this implementation, S1 specifically includes:

[0097] S11. Obtaining material property data involved in the production process of micro saw blades. The material property data includes but is not limited to the density, hardness, toughness, elastic modulus and thermal expansion coefficient of the material. The material property data is obtained through experimental measurement or existing data tables, and is represented by the letter set M = {m1, m2, ..., m n} means, where m i represents the specific characteristics of the i-th material;

[0098] S12, determining the cutting parameters of the micro saw blade, including cutting speed v, cutting depth d, feed rate f and cutting force F, the cutting parameters are determined by combining the formula:

[0099] F = k·A·v;

[0100] Where k is the cutting coefficient of the material, A is the cutting area, and v is the cutting speed;

[0101] S13, obtaining and modeling the heat treatment parameters in the production of micro saw blades, the heat treatment parameters including heating temperature T, heating time t, cooling rate r and holding time t p , the parameters are expressed by formula combination, and the cooling rate r is expressed by the formula:

[0102]

[0103] Where T f and T i are the final temperature and the initial temperature respectively, t f and t i are the corresponding time points respectively;

[0104] S14. Establish a simulation model of production environment variables, where the production environment variables include but are not limited to the ambient temperature T e , ambient humidity H, equipment vibration amplitude V and air flow velocity va , variables are monitored in real time by sensors and input into the simulation model in the form of time series data. The simulation model is based on discrete time series:

[0105] X(t)={T e (t), H(t), V(t), v a (t)};

[0106] Where t is the time step;

[0107] S15. Integrate the material properties, cutting parameters, heat treatment parameters and production environment variable data obtained in the above sub-steps S1.1 to S1.4 to form an input data set for the digital twin model. The data set is modeled through simulation software and generates corresponding simulation data to simulate the behavior and response in the actual production process.

[0108] In this implementation, S2 specifically includes:

[0109] S21. According to the input data set of the digital twin model described in claim 2, initialize the population of the genetic algorithm, the population consists of multiple individuals, each individual represents a possible production plan, and the individual is represented as a high-dimensional vector Among them, m i represents the material property vector, v i d i 、f i 、F i represents the cutting parameter vector, T i ,t i 、r i , represents the heat treatment parameter vector, H i 、V i , Represents the production environment variable vector;

[0110] S22. Apply an adaptive crossover mechanism based on Arnold map chaotic sequence to perform crossover operation on individuals in the initial population, with a crossover rate C r The crossover between individuals is disturbed by generating an Arnold map chaotic sequence and then adaptively adjusted:

[0111]

[0112] Among them, C AMCS (P i , P j ) is a chaotic sequence generated by Arnold mapping, which is used to measure the individual P i and P j The chaotic complexity of the crossover operation, Arnold mapping:

[0113]

[0114] in, Represents the current two-dimensional coordinate point, It represents the new coordinate point obtained after applying Arnold mapping, that is, the position of the mapped point on the two-dimensional plane. The complexity of the mapping optimization crossover operation and the diversity of the population form an adaptive feedback;

[0115] S23. After the adaptive crossover mechanism takes effect, a mutation rate adjustment mechanism based on hypersurface Weyl transformation is used to perform mutation operations on the individuals generated by the crossover. The mutation rate M r Individual quantum states are manipulated by applying Weyl transformations on high-dimensional hypersurfaces and adjusted by their geometric invariants:

[0116]

[0117] in, For individual P i Weyl transformation operator in high-dimensional hypersurfaces, acting on the density matrix is the quantization constant, is the geometric invariant obtained after Weyl transformation;

[0118] S24. Evaluate the fitness of the mutated individuals, and perform selection operations based on the algebraic K-group in K-theory. Classify the individuals through the calculation of the algebraic K-group, and optimize the population based on the homotopy properties of the K-group:

[0119]

[0120] in, For individual P i Classification in algebraic K-groups with homotopy dimension n, λ n is the weight coefficient related to the classification level, Representing Algebraic K-Groups The rank of a classification is the importance or influence of the classification in the algebraic topological space. The rank is usually used to quantify the relative complexity or dimension of a classification in the structure. K-group (P i ) This is the fitness function used to evaluate the rank of the production solution;

[0121] S25. Repeat steps S2.2 to S2.4 until a preset number of iterations is reached or the fitness of the population converges to a set threshold, and multiple virtual production plans are generated.

[0122] In this implementation, S3 specifically includes:

[0123] S31, performing preliminary fitness evaluation on multiple virtual production plans generated in step S2, wherein the fitness evaluation is based on high-dimensional topological hypersurface mapping, which captures the relationship between plans in multi-dimensional space by embedding the fitness of each production plan into a high-dimensional topological hypersurface;

[0124] S32. Apply asymmetric singular value perturbation technology to perform perturbation analysis on the embedded topological hypersurface and analyze the stability and adaptability of the production plan in high-dimensional space:

[0125]

[0126] Among them, Δσ n represents the change of the nth singular value under disturbance conditions, The production plan P for singular value pairs i The partial derivative of , which measures the sensitivity of the scheme to singular values, is an asymmetric perturbation matrix applied to individual P i Introducing asymmetry, λ j is the disturbance intensity coefficient, which is used to control the disturbance effect in different directions. Represents plan P i The higher-order partial derivative tensor of describes the propagation effect of the disturbance in high-dimensional space;

[0127] S33. Based on the quantum conformal field renormalization method, the fitness of the scheme after asymmetric singular value perturbation is reorganized. Based on the quantum conformal field renormalization method, the fitness of each production scheme is redefined and optimized through the renormalization group flow in quantum conformal field theory:

[0128]

[0129] in, For production plan P i The renormalized fitness value of is the effective Lagrangian of scheme Pi, based on the scalar field φ(P i ) and the metric tensor g μv definition, To reduce Planck's constant and control the scale of quantum effects, v is the volume element in the quantum state space, and μ is the integral measure;

[0130] S34. Based on the renormalized fitness value in step S3.3, select the virtual production plan with the highest fitness.

[0131] In this implementation, S4 specifically includes:

[0132] S41. Apply the optimal virtual production solution selected in step S3 to the actual production process, and monitor the multi-dimensional parameters of the production process, such as temperature, pressure, vibration and humidity, in real time through a distributed sensor network. The real-time data is integrated into a high-dimensional spatiotemporal data set and continuously compared with the virtual model to detect any deviation between the actual production and the virtual model.

[0133] S42. Using the method beyond Schrödinger-Newman geometric gravity field, real-time monitoring data is embedded in a complex geometric space with nonlinear characteristics. By integrating the Schrödinger equation and the Newman geometric gravity field, the complex interactions and non-equilibrium dynamics hidden in the monitoring data are captured to identify the deviation between actual production and the virtual model:

[0134]

[0135] in, represents the Lagrangian universal function beyond the Schrödinger-Newman geometric gravity field, which is used to describe the behavior of real-time monitoring data in the complex geometric space. Ψ is a complex-valued state function, which represents the distribution of sensor data in the geometric field. is the curvature tensor, reflecting the bending and deformation characteristics of the data in the geometric space, is the Kronecker delta symbol, regulating the local asymmetry of the geometric space, ω b ∧ω a is the outer product of 1-form, describing the interaction between geometric flows, ∧ is the cosmological constant, controlling the global characteristics of the geometric space, and g is the metric tensor of the geometric space, controlling the mobility and distribution characteristics of data in space;

[0136] S43. When the deviation between the monitoring data and the virtual model exceeds a predetermined threshold, the quantum fiber cluster state switching mechanism is activated to dynamically adjust the virtual model. The quantum fiber cluster state switching mechanism optimizes the structural parameters of the virtual model by introducing non-trivial topological switching in the high-dimensional fiber cluster path:

[0137]

[0138] in, is the evolution operator of the production parameter P in the switching of quantum fiber bundles, represents the sum of all possible fiber bundle paths, To reduce Planck's constant and control the scale of quantum effects, γ fib is a fiber bundle path, which is used to describe the evolution trajectory of the virtual model in the high-dimensional quantum state space. is the standard potential field, describing the quantum phase change in the fiber bundle path, is the curvature tensor of the fiber bundle, describing the high-dimensional geometric properties of the virtual model;

[0139] S44. Record all deviations between actual production and the virtual model, use multi-scale quantum state analysis methods to analyze abnormal situations, and use multi-scale quantum state analysis technology to track and identify factors that cause deviations in the production process.

[0140] In this implementation manner, S5 specifically includes:

[0141] S51. When the deviation between the real-time monitoring data in the actual production process and the virtual model exceeds a predetermined threshold, the self-repair mechanism is automatically triggered to collect real-time production data through a distributed sensor network, identify and record the specific source and nature of the deviation, and feed the information back to the virtual simulation environment;

[0142] S52. In a virtual simulation environment, the improved genetic algorithm is reapplied for optimization iteration according to the feedback deviation information. The genetic algorithm generates an updated production plan by adjusting the initial conditions, crossover and mutation rates of the population;

[0143] S53. After generating a new production plan, immediately verify it, evaluate its performance in the virtual environment, select the best production plan through multiple iterations, and apply it to the actual production process;

[0144] S54. After applying the new solution, the deviation between the actual production parameters and the virtual model is monitored in real time, and the new solution can be optimized to be implemented stably.

[0145] Example

[0146] In order to verify the feasibility of the present invention in implementation, the present invention is applied to a precision manufacturing enterprise in a certain city that mainly produces micro saw blades for electronic equipment and medical devices, and its production process involves a variety of materials and complex processing techniques. Due to the extremely small size of the micro saw blades and the extremely high requirements for cutting accuracy and surface quality, the traditional manual adjustment of production parameters and repeated trials are difficult to meet the increasingly stringent quality requirements, and the production efficiency is low, and the product qualification rate fluctuates greatly. In order to solve these problems, the enterprise decided to introduce the method of optimizing the production of micro saw blades using genetic algorithms of the present invention, and comprehensively optimize its production process through digital twin technology, virtual simulation, dynamic feedback and self-repair mechanism.

[0147] Before actual production, the company uses virtual simulation software to build a digital twin model of micro saw blade production. The model covers the material properties involved in the production process, hardness, toughness, density, cutting parameters, cutting speed, cutting depth, feed rate, cutting force, heat treatment parameters, heating temperature, heating time, cooling rate, and production environment variables. By inputting this data into the simulation software, the company can generate a high-precision virtual simulation environment to simulate various process behaviors and responses that may occur in the actual production process.

[0148] Enterprises use the improved genetic algorithm in the present invention to optimize the production parameters in the above virtual simulation environment. In the initial stage, the genetic algorithm generates multiple possible virtual production plans by introducing an adaptive crossover mechanism based on the Arnold map chaotic sequence and a mutation rate adjustment mechanism based on the hypersurface Weyl transformation. After hundreds of iterations, multiple plans with high fitness are finally screened out. These plans can cover a wider range of process parameter combinations through the adaptive adjustment of the genetic algorithm, thereby finding the optimal cutting and heat treatment parameters.

[0149] After generating multiple virtual production plans, the company uses high-dimensional topological hypersurface mapping to evaluate the fitness of these plans, and uses asymmetric singular value perturbation technology to analyze the stability and adaptability of the plans in high-dimensional space. This analysis process can deeply identify the performance of each plan under different process conditions and select the best plan for use in actual production. In this way, the company can ensure that the theoretically optimal plan has been obtained before actual production, reducing the cost of trial and error.

[0150] The company applies the best solution to the actual production process and monitors the key parameters of the production process, such as cutting force, temperature, and vibration, in real time through a distributed sensor network. These sensor networks can integrate the multi-dimensional data of the production process, such as temperature, pressure, vibration, and humidity, into a high-dimensional spatiotemporal data set, and analyze the monitoring data by transcending the Schrödinger-Newman geometric gravity field method to identify deviations and abnormal dynamics between the actual production and the virtual model.

[0151] During the production process, if the deviation between the monitoring data and the virtual model exceeds the predetermined threshold, the system will automatically trigger the self-repair mechanism. Through the feedback mechanism, the enterprise can reapply the improved genetic algorithm in the virtual simulation environment for optimization iteration and generate a new production plan that adapts to the current actual conditions. This dynamic optimization capability ensures the continuity and stability of the production process, and can make timely adjustments and maintain production efficiency even when process parameters change.

[0152] During the production process, the company updates the digital twin model in real time and analyzes historical production data in combination with machine learning algorithms to further optimize future production parameter settings. Machine learning algorithms can automatically identify potential problems in production based on historical data and continuously adjust the parameter settings of genetic algorithms to improve the algorithm's predictive capabilities.

[0153] In the actual application of this production line, the company selected the data of 1,000 batches of micro saw blades produced continuously as the analysis sample. The following is a comparison of the data before and after the introduction of the method of the present invention:

[0154] Table 1: Comparison of data before and after

[0155]

[0156] After introducing the method of the present invention, the production efficiency of micro saw blades is significantly improved, the single-batch production time is shortened from 35 minutes to 28 minutes, and the efficiency is increased by 20%; the product qualification rate is increased from 92% to 98.5%, and the defective rate is significantly reduced; the material waste rate is reduced from 8% to 3%, effectively saving production resources; the downtime adjustment time caused by parameter deviation is shortened from 12 minutes to 5 minutes, and the abnormality handling efficiency is improved by 58%; in terms of process optimization, through virtual simulation and improved genetic algorithm, the optimal solution is determined after 300 iterations, and the self-repair mechanism in actual production is triggered 25 times cumulatively, and each optimization iteration takes an average of 3 minutes, ensuring the stability and continuity of the production process.

[0157] Through the above data comparison, enterprises can clearly feel the significant improvement of the method of the present invention in production efficiency, product quality, resource utilization and exception handling. These data fully prove the beneficial effect of the method of the present invention in actual production application, effectively solve the problems of low production efficiency, large quality fluctuations, serious waste of resources, etc. in the prior art, and comprehensively improve the intelligent level and competitiveness of micro saw blade production.

[0158] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for optimizing the production of micro saw blades using a genetic algorithm, characterized in that: The steps include: S1. Establish a virtual simulation environment and construct a digital twin model for micro saw blade production. The digital twin model includes simulation data of material properties, cutting parameters, heat treatment parameters and production environment variables; S2. In a virtual simulation environment, an adaptive genetic algorithm is used for preliminary parameter optimization. The genetic algorithm introduces an adaptive crossover mechanism based on Arnold mapping chaotic sequence and a mutation rate adjustment mechanism based on hypersurface Weyl transformation to generate multiple virtual production plans. S3. Perform fitness evaluation on multiple generated virtual production plans. The fitness evaluation is based on high-dimensional topological hypersurface mapping, and analyzes the stability and adaptability of the production plan in high-dimensional space through asymmetric singular value perturbation technology, thereby optimizing and selecting the virtual production plan with the highest fitness; S4. Apply the selected optimal virtual production solution to the actual production process, and monitor the deviation between the production parameters and the virtual model in real time through a distributed sensor network, record and analyze abnormal situations, and integrate the multi-dimensional data of the production process in real time through the monitoring system. And analyze the monitoring data by transcending the Schrödinger-Newman geometry gravity field method to identify production deviations and abnormal dynamics; S5. When it is detected that the deviation between the actual production and the virtual model exceeds a predetermined threshold, the self-repair mechanism is triggered, and the improved genetic algorithm is reapplied in the virtual simulation environment through the feedback mechanism to perform optimization iterations and generate a new production plan that adapts to the current actual conditions; S6. Based on the repaired production plan, the actual production parameters are automatically adjusted and applied to the production process in real time to optimize the continuity of production and the stability of product quality; S7, update the digital twin model in real time to reflect the latest production conditions and optimization results; S8. During the optimization process, the historical production data is analyzed in combination with machine learning algorithms to optimize future production parameter settings and the prediction capabilities of genetic algorithms; S9, looping through steps S4 to S8 until the preset production optimization goal is reached, and finally outputting an optimized micro saw blade product.

2. A method for optimizing micro saw blade production using a genetic algorithm according to claim 1, characterized in that: The S1 specifically includes: S11. Obtain the material property data involved in the production process of micro saw blades. The material property data include the density, hardness, toughness, elastic modulus and thermal expansion coefficient of the material. The material property data is obtained through experimental measurement or existing data tables, and is represented by the letter set M = {m1, m2, ..., m n } means, where m i represents the specific characteristics of the i-th material; S12, determining the cutting parameters of the micro saw blade, including cutting speed v, cutting depth d, feed rate f and cutting force F, the cutting parameters are determined by combining the formula: F = k·A·v; Where k is the cutting coefficient of the material, A is the cutting area, and v is the cutting speed; S13, obtaining and modeling the heat treatment parameters in the production of micro saw blades, the heat treatment parameters including heating temperature T, heating time t, cooling rate r and holding time t p , the parameters are expressed by formula combination, and the cooling rate r is expressed by the formula: Where T f and T i are the final temperature and the initial temperature respectively, t f and t i are the corresponding time points respectively; S14. Establish a simulation model of production environment variables, where the production environment variables include but are not limited to the ambient temperature T e , ambient humidity H, equipment vibration amplitude V and air flow velocity v a , the variables are monitored in real time by sensors and input into the simulation model in the form of time series data. The simulation model is based on discrete time series: X(t)={T e (t),H(t),V(t),v a (t)}; Where t is the time step; S15. Integrate the material properties, cutting parameters, heat treatment parameters and production environment variable data obtained in the above sub-steps S1.1 to S1.4 to form an input data set for the digital twin model. The data set is modeled through simulation software and generates corresponding simulation data to simulate the behavior and response in the actual production process.

3. A method for optimizing micro saw blade production using genetic algorithm according to claim 1, characterized in that: The S2 specifically includes: S21. According to the input data set of the digital twin model described in claim 2, initialize the population of the genetic algorithm, the population consists of multiple individuals, each individual represents a possible production plan, and the individual is represented as a high-dimensional vector Among them, m i represents the material property vector, v i d i 、f i 、F i represents the cutting parameter vector, represents the heat treatment parameter vector, Represents the production environment variable vector; S22. Apply an adaptive crossover mechanism based on Arnold map chaotic sequence to perform crossover operation on individuals in the initial population, with a crossover rate C r The crossover between individuals is disturbed by generating an Arnold map chaotic sequence and then adaptively adjusted: Among them, C AMCS (P i ,P j ) is a chaotic sequence generated by Arnold mapping, which is used to measure the individual P i and P j The chaotic complexity of the crossover operation, Arnold mapping: in, Represents the current two-dimensional coordinate point, It represents the new coordinate point obtained after applying Arnold mapping, the position of the mapped point on the two-dimensional plane, and the adaptive feedback between the complexity of the mapping optimization crossover operation and the diversity of the population; S23. After the adaptive crossover mechanism takes effect, a mutation rate adjustment mechanism based on hypersurface Weyl transformation is used to perform mutation operations on the individuals generated by the crossover. The mutation rate M r Individual quantum states are manipulated by applying Weyl transformations on high-dimensional hypersurfaces and adjusted by their geometric invariants: in, For individual P i Weyl transformation operator in high-dimensional hypersurfaces, acting on the density matrix is the quantization constant, is the geometric invariant obtained after Weyl transformation; S24. Evaluate the fitness of the mutated individuals, and perform selection operations based on the algebraic K-group in K-theory. Classify the individuals through the calculation of the algebraic K-group, and optimize the population based on the homotopy properties of the K-group: in, For individual P i Classification in algebraic K-groups with homotopy dimension n, λ n is the weight coefficient related to the classification level, Representing Algebraic K-Groups The rank of the corresponding classification is the importance or influence of the classification in the algebraic topological space. The rank is used to quantify the relative complexity or dimension of a classification in the structure. K-group (P i ) This is the fitness function used to evaluate the rank of the production solution; S25. Repeat steps S2.2 to S2.4 until a preset number of iterations is reached or the fitness of the population converges to a set threshold, and multiple virtual production plans are generated.

4. A method for optimizing micro saw blade production using a genetic algorithm according to claim 1, characterized in that: The S3 specifically includes: S31, performing preliminary fitness evaluation on multiple virtual production plans generated in step S2, wherein the fitness evaluation is based on high-dimensional topological hypersurface mapping, which captures the relationship between plans in multi-dimensional space by embedding the fitness of each production plan into a high-dimensional topological hypersurface; S32. Apply asymmetric singular value perturbation technology to perform perturbation analysis on the embedded topological hypersurface and analyze the stability and adaptability of the production plan in high-dimensional space: Among them, Δσ n represents the change of the nth singular value under disturbance conditions, The production plan P for singular value pairs i The partial derivative of , which measures the sensitivity of the scheme to singular values, is an asymmetric perturbation matrix applied to individual P i Introducing asymmetry, λ j is the disturbance intensity coefficient, which is used to control the disturbance effect in different directions. Represents plan P i The higher-order partial derivative tensor of describes the propagation effect of the disturbance in high-dimensional space; S33. Based on the quantum conformal field renormalization method, the fitness of the scheme after asymmetric singular value perturbation is reorganized. Based on the quantum conformal field renormalization method, the fitness of each production scheme is redefined and optimized through the renormalization group flow in quantum conformal field theory: in, For production plan P i The renormalized fitness value of For plan P i The effective Lagrangian of is based on the scalar field φ(P i ) and the metric tensor g μν definition, To reduce Planck's constant and control the scale of quantum effects, is the volume element in the quantum state space, μ is the integral measure; S34. Based on the renormalized fitness value in step S3.3, select the virtual production plan with the highest fitness.

5. A method for optimizing micro saw blade production using genetic algorithm according to claim 1, characterized in that: The S4 specifically includes: S41. Apply the optimal virtual production solution selected in step S3 to the actual production process, and monitor the multi-dimensional parameters of the production process, such as temperature, pressure, vibration and humidity, in real time through a distributed sensor network. The real-time data is integrated into a high-dimensional spatiotemporal data set and continuously compared with the virtual model to detect any deviation between the actual production and the virtual model. S42. Using the method beyond Schrödinger-Newman geometric gravity field, real-time monitoring data is embedded in a complex geometric space with nonlinear characteristics. By integrating the Schrödinger equation and the Newman geometric gravity field, the complex interactions and non-equilibrium dynamics hidden in the monitoring data are captured to identify the deviation between actual production and the virtual model: in, represents the Lagrangian universal function beyond the Schrödinger-Newman geometric gravity field, which is used to describe the behavior of real-time monitoring data in the complex geometric space. Ψ is a complex-valued state function, which represents the distribution of sensor data in the geometric field. is the curvature tensor, reflecting the bending and deformation characteristics of the data in the geometric space, is the Kronecker delta symbol, regulating the local asymmetry of the geometric space, ω b ∧ω a is the outer product of 1-form, describing the interaction between geometric flows, Λ is the cosmological constant, controlling the global characteristics of the geometric space, and g is the metric tensor of the geometric space, controlling the mobility and distribution characteristics of data in space; S43. When the deviation between the monitoring data and the virtual model exceeds a predetermined threshold, the quantum fiber cluster state switching mechanism is activated to dynamically adjust the virtual model. The quantum fiber cluster state switching mechanism optimizes the structural parameters of the virtual model by introducing non-trivial topological switching in the high-dimensional fiber cluster path: in, is the evolution operator of the production parameter P in the switching of quantum fiber bundles, represents the sum of all possible fiber bundle paths, To reduce Planck's constant and control the scale of quantum effects, γ fib is a fiber bundle path, which is used to describe the evolution trajectory of the virtual model in the high-dimensional quantum state space. is the standard potential field, describing the quantum phase change in the fiber bundle path, is the curvature tensor of the fiber bundle, describing the high-dimensional geometric properties of the virtual model; S44. Record all deviations between actual production and the virtual model, use multi-scale quantum state analysis methods to analyze abnormal situations, and use multi-scale quantum state analysis technology to track and identify factors that cause deviations in the production process.

6. A method for optimizing micro saw blade production using a genetic algorithm according to claim 1, characterized in that: The S5 specifically includes: S51. When the deviation between the real-time monitoring data in the actual production process and the virtual model exceeds a predetermined threshold, the self-repair mechanism is automatically triggered to collect real-time production data through a distributed sensor network, identify and record the specific source and nature of the deviation, and feed the information back to the virtual simulation environment; S52. In a virtual simulation environment, the improved genetic algorithm is reapplied for optimization iteration according to the feedback deviation information. The genetic algorithm generates an updated production plan by adjusting the initial conditions, crossover and mutation rates of the population; S53. After generating a new production plan, immediately verify it, evaluate its performance in the virtual environment, select the best production plan through multiple iterations, and apply it to the actual production process; S54. After applying the new solution, the deviation between the actual production parameters and the virtual model is monitored in real time, and the new solution can be optimized to be implemented stably.

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