A CNC cutting path optimization method and system based on quantum computing

Abstract

This invention relates to the field of CNC machining technology, specifically to a CNC cutting path optimization method and system based on quantum computing. The optimization method includes the following steps: obtaining a three-dimensional geometric model of the target workpiece and a set of cutting process parameters, including tool diameter, feed rate, depth of cut, and material removal rate threshold. A multi-objective optimization model is constructed with the objectives of minimizing the total path length, maximizing tool load balance, and minimizing idle travel time. This invention, by constructing a quantum-classical coupled multi-objective optimization model, maps the spatial correlation of cutting path points to the entanglement strength between qubits, and utilizes the parallel tunneling capability of a quantum annealing processor to overcome the dimensionality curse limitation of traditional optimization algorithms.

Description

Technical Field

[0001] This invention relates to the field of CNC machining technology, specifically to a CNC cutting path optimization method and system based on quantum computing. Background Technology

[0002] Optimizing cutting paths for complex three-dimensional curved surfaces in precision CNC machining has long faced the challenge of non-convex optimization under multi-dimensional constraints. Traditional optimization algorithms based on classical computational frameworks, such as genetic algorithms and simulated annealing, struggle to effectively reconcile the multi-objective trade-offs between path length, tool load balancing, and idle travel time when solving high-dimensional discrete path points due to the trade-off between computational complexity and convergence. Especially when dealing with nonlinear problems involving the coupling of tool kinematic constraints and dynamic machining perturbations, existing methods often get trapped in local optima and cannot efficiently explore the quantum state parameter space. Furthermore, classical optimization models lack mathematical representation of quantum characteristics emerging during machining, such as the quantum tunneling effect induced by tool-workpiece interaction. This leads to a mismatch between path planning results and the microscopic dynamic characteristics of the physical machining system, severely limiting the efficiency and surface quality of high-precision machining. Summary of the Invention

[0003] This invention provides a CNC cutting path optimization method and system based on quantum computing to solve the technical problem that classical optimization models are difficult to converge to the global Pareto front in polynomial time and cannot dynamically adapt to parameter disturbances caused by quantum fluctuations during machining.

[0004] The technical solution of the present invention to solve the above-mentioned technical problems is as follows:

[0005] A CNC cutting path optimization method based on quantum computing is provided, the steps of which include:

[0006] Obtain the three-dimensional geometric model of the target workpiece and a set of cutting process parameters, including tool diameter, feed rate, depth of cut, and material removal rate threshold. Discretize the three-dimensional geometric model into a three-dimensional point cloud matrix containing N path points, where N > 10. 4 ;

[0007] A multi-objective optimization model is constructed with the objectives of minimizing the total path length, maximizing the tool load balance, and minimizing the idle travel time. The optimization parameters of the optimization model are encoded into a superposition state of M qubits through a qubit mapper, and the value of M is dynamically determined according to the ratio of workpiece size to tool diameter.

[0008] A quantum annealing processor is used to perform a staged adiabatic evolution operation, and the magnetic field strength parameters of each stage are dynamically adjusted according to the tool motion state.

[0009] The initial path sequence is generated by analyzing the quantum measurement results. The curvature of the sequence is then smoothed by a path smoother to generate an optimized path. The optimized path is then converted into CNC code and the machining process is monitored. When an abnormal vibration spectrum is detected, a parameter adaptive adjustment mechanism is triggered.

[0010] Furthermore, the construction steps of the multi-objective optimization model include:

[0011] Establish an objective function with path length, load variance, and idle time as variables, where the weight coefficients of each variable are dynamically adjusted according to the material hardness;

[0012] Set a set of constraints for the lower limit of material removal rate, the threshold of cutting force fluctuation, and the maximum turning angle;

[0013] The three-dimensional point cloud matrix is ​​divided into multiple cutting subdomains according to the tool coverage area, and the size of each cutting subdomain is positively correlated with the tool diameter. A local energy function considering the quantum entanglement effect of path points is constructed for each cutting subdomain, and the energy functions of each subdomain are coupled through the quantum tunneling effect to form a global optimization model.

[0014] Furthermore, the computational expression for the quantum entanglement effect is as follows:

[0015] Where Δx, Δy are the coordinate differences between path points i and j, σ is the kernel function width related to the tool diameter, ξ is the quantum correction factor, and Q(ρ) i ,ρ j The quantum correlation degree is based on the density matrix, and its calculation expression is:

[0016] , where λ i k This represents the quantized eigenvalue of the k-th cutting parameter at path point i, where n=3 corresponds to the three dimensions of cutting speed, feed rate, and depth of cut.

[0017] Furthermore, the process of obtaining the quantized eigenvalues ​​includes:

[0018] For each path point, a vector space containing cutting parameters is constructed, and the parameters are mapped to a three-dimensional Hilbert space using a quantum phase encoder;

[0019] The quantum state is decomposed using the basis transformation method, and the projection components of each parameter dimension are calculated by selecting a specific orthogonal basis set.

[0020] The decomposition results are normalized and then used as input parameters for quantum correlation.

[0021] Furthermore, the steps of the phased adiabatic evolution operation include:

[0022] During the initialization phase, the maximum transverse magnetic field is applied and its intensity is gradually reduced, while the square root time function of the longitudinal magnetic field is activated.

[0023] During the mid-annealing process, a dynamic modulation coupling strength is introduced, and its modulation frequency establishes a non-linear relationship with the spindle speed.

[0024] In the final stage, a spatial gradient magnetic field is applied, and the field strength distribution is correlated with the coordinates of the workpiece's center of mass to ensure that the ground state probability reaches a predetermined threshold at the end of the evolution.

[0025] Furthermore, the step of dynamically modulating the coupling strength includes:

[0026] The coupling strength modulation frequency is calculated based on the real-time rotational speed of the tool, and a rotational speed-frequency conversion relationship including harmonic components is established.

[0027] A time-varying sine function is used to adjust the interaction strength between qubits, while a random quantum fluctuation term is introduced to enhance the global search capability.

[0028] Furthermore, the curvature continuity processing steps are as follows:

[0029] A composite optimization function containing a curvature square term, a quantum probability gradient term, and a fitting error term is defined, and a third-order nonlinear differential equation is derived through variational principles.

[0030] The quantum Monte Carlo method is used for numerical solution, and a dissipation coefficient is introduced to control the convergence of the solution. The iteration step size is dynamically adjusted according to the rate of change of path curvature, and the expression is:

[0031] Where Δs is the rate of change of path curvature, κ max κ represents the maximum curvature of the current path segment. avg Let be the mean curvature, and tanh be the hyperbolic tangent function.

[0032] Furthermore, the steps of the parameter adaptive adjustment mechanism include:

[0033] Real-time acquisition of cutting force signals and multi-scale wavelet packet decomposition are performed to extract energy distribution characteristics of specific frequency bands;

[0034] When an abnormal accumulation of high-frequency energy is detected, the weight coefficients of the optimization model are adjusted according to the preset quantum-classical mapping rules.

[0035] The adjustment process introduces a temperature-dependent quantum fluctuation factor to ensure that parameter updates conform to thermodynamic equilibrium conditions.

[0036] Furthermore, the quantum-classical mapping rule includes establishing a nonlinear relationship between the weight coefficient adjustment and the quantum annealing temperature, wherein the length weight adjustment term includes a temperature linear term and a periodic oscillation term, the load weight adjustment term introduces a temperature square root logarithmic function, and the idle time weight adopts a hyperbolic decay form of temperature square.

[0037] On the other hand, a quantum computing-based CNC cutting path optimization system is provided to implement the quantum computing-based CNC cutting path optimization method described above. The optimization system includes:

[0038] The parameter input interface module is used to receive workpiece geometric data and machining constraints.

[0039] A quantum coding device, connected to a parameter input interface, includes a non-uniform quantum bit allocator and a phase modulation unit;

[0040] The optimized model building module integrates the Ising model generator and dynamic constraint loader.

[0041] The quantum annealing unit is equipped with a tunable coupler and a superconducting magnetron control device.

[0042] The path resolution module includes a quantum state measuring instrument and a probability amplitude converter;

[0043] The machining monitoring module is equipped with a three-dimensional vibration sensor and a cutting force detection array.

[0044] An adaptive feedback control unit, connected to the quantum annealing processing unit and the processing monitoring module, is equipped with a quantum parameter mapping matrix and a pulse modulation circuit;

[0045] The tunable coupler includes:

[0046] The Josephson junction critical current of a superconducting quantum interference device array exhibits a logarithmic growth relationship with path complexity.

[0047] A flux bias control circuit is used to generate a modulated signal associated with the cutting parameters;

[0048] The microwave pulse generator outputs a phase-modulated waveform with QPSK encoding characteristics, and the symbol rate is synchronized with the cutting force fluctuation frequency.

[0049] The beneficial effects of this invention are:

[0050] This invention constructs a quantum-classical coupled multi-objective optimization model, mapping the spatial correlation of cutting path points to the entanglement strength between qubits, and overcoming the dimensionality curse limitation of traditional optimization algorithms by utilizing the parallel tunneling capability of a quantum annealing processor. By dynamically adjusting the magnetic field gradient distribution during the adiabatic evolution process, it achieves synergistic optimization of tool motion state and quantum ground state search, improving the global exploration efficiency of path planning in high-dimensional non-convex solution spaces. The quantum correlation calculation model based on the density matrix, for the first time, incorporates the quantized characteristics of cutting parameters, such as the energy level transition effect of cutting speed and the phase coherence of feed rate, into the optimization objective function, automatically satisfying the smoothness condition under quantum mechanical constraints for the curvature continuity of the path sequence. By designing an adaptive mapping mechanism from quantum measurement results to CNC code, the system can respond in real time to abnormal vibration spectra during machining, dynamically correcting optimization weights using a quantum parameter feedback loop to ensure simultaneous improvement in machining stability and surface accuracy.

[0051] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, the following describes the preferred embodiments of the present invention in detail with reference to the accompanying drawings. Attached Figure Description

[0052] Figure 1 This is a flowchart of a CNC cutting path optimization method in one embodiment of the present invention;

[0053] Figure 2 This is a schematic diagram of the evolution of quantum annealing energy terrain in one embodiment of the present invention (1 and 5).

[0054] Figure 3 This is a schematic diagram of the Pareto front for multi-objective optimization in one embodiment of the present invention (2);

[0055] Figure 4 In one embodiment of the present invention, the quantum correlation degree J ij Spatial distribution diagram (3);

[0056] Figure 5 This is a schematic diagram of quantum state spatial projection in one embodiment of the present invention (4);

[0057] Figure 6 This is a schematic diagram (7) of curvature adaptive step size adjustment in one embodiment of the present invention;

[0058] Figure 7 This is a schematic diagram (8) of the frequency domain analysis of the cutting force signal in one embodiment of the present invention;

[0059] Figure 8 This is a schematic diagram (9) of temperature-dependent weight adjustment in one embodiment of the present invention;

[0060] Figure 9 This is a schematic diagram (10) showing the relationship between coupling strength and tool diameter in one embodiment of the present invention. Detailed Implementation

[0061] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] The term "comprising," and any variations thereof, used in the specification and claims of this application, is intended to cover a non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those explicitly listed, but may include other steps or units not explicitly listed or inherent to such process, method, product, or apparatus. Furthermore, the use of "and / or" in the specification and claims indicates at least one of the connected objects, such as A and / or B, indicating the inclusion of A alone, B alone, or both A and B.

[0063] In embodiments of the present invention, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" or "for example" in embodiments of the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Rather, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0064] The present invention provides the following preferred embodiments:

[0065] Example 1

[0066] To address the trade-off between computational complexity and convergence faced by traditional optimization algorithms when dealing with high-dimensional non-convex optimization problems, this embodiment provides a CNC cutting path optimization method, namely a three-dimensional path optimization method based on quantum computing.

[0067] like Figure 1 and Figure 2 As shown, the steps of the cutting path optimization method are as follows:

[0068] S100. Obtain the three-dimensional geometric model of the target workpiece and the set of cutting process parameters. The set of cutting process parameters includes tool diameter, feed rate, depth of cut, and material removal rate threshold. Discretize the three-dimensional geometric model into a three-dimensional point cloud matrix containing N path points, where N > 10. 4 .

[0069] S200. Construct a multi-objective optimization model with the goals of minimizing the total path length, maximizing the tool load balance, and minimizing the idle travel time. Encode the optimization parameters of the optimization model into a superposition state of M qubits through a qubit mapper. The value of M is dynamically determined according to the ratio of workpiece size to tool diameter.

[0070] The S300 uses a quantum annealing processor to perform a staged adiabatic evolution operation, and the magnetic field strength parameters of each stage are dynamically adjusted according to the tool movement state.

[0071] S400: Analyzes quantum measurement results to generate an initial path sequence, performs curvature continuity processing on the sequence through a path smoother to generate an optimized path, converts the optimized path into CNC code and implements machining process monitoring, and triggers a parameter adaptive adjustment mechanism when an abnormal vibration spectrum is detected.

[0072] First, obtain the 3D geometric model of the target workpiece and the set of cutting process parameters. This set includes tool diameter, feed rate, depth of cut, and material removal rate threshold. Using computer-aided design (CAD) software, the 3D geometric model is discretized into a 3D point cloud matrix containing N path points, where N is greater than 10. 4 This process ensures a dense distribution of path points, providing sufficient data support for subsequent optimization.

[0073] Furthermore, a multi-objective optimization model is constructed with the objectives of minimizing the total path length, maximizing tool load balance, and minimizing idle travel time. To achieve this objective, a qubit mapper is used to encode the optimization parameters of the model as a superposition of M qubits. The value of M is dynamically determined based on the ratio of workpiece size to tool diameter, ensuring that the number of optimization parameters matches the actual machining requirements and improving the flexibility and adaptability of the optimization. It is important to understand that this dynamically determined method can effectively address the optimization needs of workpieces with different sizes, thereby improving the applicability of the optimization results.

[0074] Furthermore, a quantum annealing processor is employed to perform a staged adiabatic evolution operation. The magnetic field strength parameters at each stage are dynamically adjusted according to the tool's motion state. This process is achieved through a superconducting magnetron control device within the quantum annealing processor, ensuring precise control of the magnetic field strength. It is understandable that dynamically adjusting the magnetic field strength parameters better adapts to the tool's motion state at different machining stages, thereby improving the stability and efficiency of the optimization process.

[0075] Furthermore, the quantum measurement results are analyzed to generate an initial path sequence, which is then smoothed for curvature continuity using a path smoother to generate an optimized path. The path smoother employs advanced mathematical methods, such as cubic spline interpolation or Bézier curve fitting, to ensure the smoothness and continuity of the path. The generated optimized path is then converted into CNC code and monitored during the machining process. When an abnormal vibration spectrum is detected, an adaptive parameter adjustment mechanism is triggered. This mechanism, through real-time acquisition and analysis of cutting force signals, can promptly adjust the optimized parameters upon detecting abnormal vibrations, ensuring the stability of the machining process and surface quality.

[0076] The advantage of this embodiment lies in its significant improvement in the efficiency and accuracy of CNC cutting path optimization by incorporating quantum computing technology. Utilizing the parallel tunneling capability of the quantum annealing processor, a globally optimal solution can be found in polynomial time, overcoming the limitations of traditional optimization algorithms in high-dimensional non-convex optimization problems. Simultaneously, dynamically adjusting the magnetic field strength parameters and real-time monitoring of the machining process ensure a high degree of matching between the optimization results and actual machining conditions, thereby improving machining stability and surface quality. The method in this embodiment not only effectively coordinates the multi-objective trade-offs between path length, tool load balancing, and idle travel time, but also dynamically adapts to various parameter disturbances during machining, further enhancing the efficiency and reliability of precision CNC machining.

[0077] Example 2

[0078] To address the limitations of traditional optimization models in handling multi-objective trade-offs, this embodiment further optimizes the construction steps of the multi-objective optimization model. The specific implementation is as follows:

[0079] First, an objective function is established with path length, load variance, and idle time as variables. The weight coefficients of each variable are dynamically adjusted according to the material hardness. For example, for materials with higher hardness, the weight coefficient of path length can be appropriately increased, while the weight coefficient of idle time can be decreased to ensure the stability of the tool and the machining quality on high-hardness materials. It is important to understand that this dynamic adjustment mechanism can better adapt to the machining requirements of different materials, improving the applicability and flexibility of the optimization results.

[0080] Furthermore, a set of constraints is set, including a lower limit for material removal rate, a cutting force fluctuation threshold, and a maximum steering angle. These constraints ensure the physical feasibility and machining safety of the optimization process. For example, the lower limit for material removal rate guarantees machining efficiency, the cutting force fluctuation threshold prevents excessive cutting forces from damaging the tool and workpiece, and the maximum steering angle limits the tool's range of motion, avoiding excessive mechanical stress. Understandably, these constraints work together to ensure the rationality and reliability of the optimization results.

[0081] Furthermore, the 3D point cloud matrix is ​​divided into multiple cutting subdomains according to the tool coverage area. The size of each cutting subdomain is positively correlated with the tool diameter. This division not only simplifies the complexity of the optimization problem but also improves the accuracy of local optimization. A local energy function considering the quantum entanglement effect of path points is constructed for each cutting subdomain. These local energy functions are coupled through the quantum tunneling effect to form a global optimization model. It is important to understand that the introduction of the quantum entanglement and quantum tunneling effects enables the optimization model to more effectively explore the high-dimensional solution space, thereby finding the global optimum.

[0082] The advantage of this embodiment lies in ensuring the diversity and practicality of the optimization results by dynamically adjusting the weight coefficients of the objective function and setting reasonable constraints. Simultaneously, by dividing the 3D point cloud matrix into multiple cutting subdomains and introducing quantum entanglement and quantum tunneling effects, the global search capability and local optimization accuracy of the optimization process are significantly improved. Through the method of this embodiment, a more balanced and practical solution can be found in multi-objective trade-offs, thereby improving the overall effect of CNC cutting path optimization.

[0083] Example 3

[0084] To address the spatial correlation problem between pathpoints, this embodiment further refines the calculation expression for the quantum entanglement effect. The specific implementation method is as follows:

[0085] First, the calculation expression for the quantum entanglement effect is defined as follows:

[0086] Where Δx and Δy are the coordinate differences between path points i and j, σ is the width of the kernel function related to the tool diameter, and ξ is the quantum correction factor. It's important to understand that this expression uses a Gaussian kernel function to measure the spatial distance between path points and introduces a quantum correction factor to adjust the strength of the quantum entanglement effect.

[0087] Furthermore, the quantum correlation degree based on the density matrix is ​​defined as follows:

[0088] , where λ i k Let represent the quantized eigenvalue of the k-th cutting parameter at path point i, where n=3 corresponds to the three dimensions of cutting speed, feed rate, and depth of cut. Understandably, this calculation method quantizes the quantum correlation between path points through the interaction of quantized eigenvalues, thus considering more physical factors in the optimization process.

[0089] Furthermore, the above expression can precisely describe the quantum entanglement effect between path points. This description not only considers the spatial distance between path points but also introduces quantum correlations from quantum mechanics, enabling the optimization model to more comprehensively reflect the physical characteristics of the actual processing. It is important to understand that this comprehensive approach improves the accuracy of the optimization results.

[0090] The advantage of this embodiment lies in its ability to more accurately describe the spatial correlation between path points through detailed calculation expressions of quantum entanglement effects. This not only enhances the physical meaning of the optimization model but also improves the reliability and accuracy of the optimization results. The method described in this embodiment can find more reasonable path planning schemes in complex machining environments, thereby improving the effectiveness of CNC cutting path optimization.

[0091] Example 4

[0092] To address the problem of obtaining quantized eigenvalues, this embodiment further refines the process for obtaining quantized eigenvalues. The specific implementation method is as follows:

[0093] First, a vector space containing cutting parameters is constructed for each path point. These cutting parameters include cutting speed, feed rate, and depth of cut. These parameters are then mapped to a three-dimensional Hilbert space using a quantum phase encoder. It's important to understand that this mapping method transforms the cutting parameters into quantum states, thus leveraging the advantages of quantum computing for processing.

[0094] Furthermore, a basis vector transformation method is employed to perform eigenvalue decomposition on the quantum state. A specific set of orthogonal basis vectors is selected to calculate the projected components of each parameter dimension. This decomposition method represents the quantum state as a linear combination of a set of basis vectors, thereby extracting the characteristic information of each parameter dimension. Understandably, this eigenvalue decomposition method can more precisely describe the structure of the quantum state and improve the accuracy of quantized eigenvalues.

[0095] Furthermore, the decomposition results are normalized and used as input parameters for quantum correlation. Normalization ensures the consistency and comparability of eigenvalues, making the calculation of quantum correlation more accurate. It is important to understand that normalization is a crucial step in data preprocessing, as it eliminates the influence of different parameter scales and improves the stability of the calculation results.

[0096] The advantage of this embodiment is that, through a detailed process for obtaining quantized eigenvalues, the quantized characteristics of the cutting parameters can be described more accurately. This method not only improves the accuracy of quantum correlation calculations but also enhances the physical meaning of the optimization model.

[0097] Example 5

[0098] like Figure 2 As shown, to address the efficiency issue of the quantum annealing processor when performing adiabatic evolution operations, this embodiment further refines the steps of the staged adiabatic evolution operation. The specific implementation is as follows:

[0099] First, during the initialization phase, a maximum transverse magnetic field is applied and its intensity is gradually reduced, while the square root time function of the longitudinal magnetic field is activated. This initialization method ensures a smooth transition of the quantum system from its initial state to intermediate states, reducing unnecessary energy loss. It is important to understand that the combined control of the transverse and longitudinal magnetic fields can effectively guide the evolution of the quantum system.

[0100] Furthermore, during the mid-annealing stage, a dynamic modulation coupling strength is introduced, with its modulation frequency establishing a non-linear relationship with the spindle speed. This dynamic modulation method can better adapt to the tool's motion state at different machining stages, improving the stability and efficiency of the optimization process. It is understandable that by dynamically modulating the coupling strength, optimal quantum state evolution can be achieved at different machining stages, thereby finding a better path planning scheme.

[0101] Furthermore, in the final stage, a spatial gradient magnetic field is applied, with the field strength distribution correlated to the coordinates of the workpiece's center of mass, ensuring that the ground state probability reaches a predetermined threshold at the end of the evolution. This method of applying the spatial gradient magnetic field can guide the quantum system to eventually converge to the global optimum. It is important to understand that precise control of the spatial gradient magnetic field can ensure the high quality and accuracy of the optimization results.

[0102] The advantage of this embodiment is that, through staged adiabatic evolution operations, the quantum system can be efficiently guided to smoothly transition from its initial state to the global optimum. This approach not only improves the operating efficiency of the quantum annealing processor but also enhances the stability and reliability of the optimization results.

[0103] Example 6

[0104] To address the efficiency and global search capability issues of quantum annealing processors when dynamically modulating coupling strength, this embodiment further optimizes the dynamic coupling strength modulation step. The specific implementation is as follows:

[0105] First, the coupling strength modulation frequency is calculated based on the real-time tool rotation speed, establishing a speed-frequency conversion relationship that includes harmonic components. This conversion relationship is determined using Fourier analysis, accurately capturing the frequency characteristics at different speeds. It's important to understand that introducing harmonic components better simulates the complex motion states in actual machining processes, thereby improving the adaptability and accuracy of the modulation frequency.

[0106] Furthermore, a time-varying sine function is used to adjust the interaction strength between qubits. The time-varying sine function has the form: A(t) = A0sin(2πft + ϕ), where A0 is the amplitude, f is the frequency, and ϕ is the phase. This time-varying function can flexibly adjust the interaction strength between qubits to adapt to different processing stages. Simultaneously, a random quantum fluctuation term is introduced to enhance the global search capability. The random quantum fluctuation term is implemented using a Gaussian white noise generator, and its expression is η(t) = σ⋅N(0,1), where σ is the standard deviation, and N(0,1) is a standard normal distribution. It can be understood that the random quantum fluctuation term can break the constraints of local optima, improving the efficiency and success rate of the global search.

[0107] Furthermore, by combining the aforementioned modulation frequency and harmonic components, along with the time-varying sine function and random quantum fluctuation terms, a complete dynamic modulation coupling strength model is formed. This model can not only precisely control the interaction strength between qubits but also perform efficient global searches. It is important to understand that this comprehensive modulation method can significantly improve the operating efficiency of the quantum annealing processor and the quality of the optimization results.

[0108] The advantage of this embodiment lies in its ability to more accurately control the interaction strength between qubits and enhance global search capabilities through detailed dynamic modulation of coupling strength steps. This not only improves the stability and reliability of the optimization process but also ensures high-quality optimization results. Using this method, more reasonable path planning schemes can be found in complex machining environments, thereby improving the smoothness of CNC cutting paths and machining accuracy.

[0109] Example 7

[0110] To address the numerical solution problem in path curvature continuity processing, this embodiment further refines the steps of curvature continuity processing. The specific implementation method is as follows:

[0111] First, a composite optimization function is defined, comprising a curvature square term, a quantum probability gradient term, and a fitting error term. This composite optimization function takes the form F(s) = ακ²(s) + β∇P(s) + γE(s), where α, β, and γ are weighting coefficients, κ(s) is the curvature, ∇P(s) is the quantum probability gradient, and E(s) is the fitting error. A third-order nonlinear differential equation is derived using variational principles to describe the curvature variation of the path. It is important to understand that this composite optimization function can comprehensively consider the geometric characteristics and quantum effects of the path, thereby improving the smoothness and continuity of the path.

[0112] Furthermore, the quantum Monte Carlo method is employed for numerical solution. The quantum Monte Carlo method, through random sampling techniques, can efficiently solve high-dimensional nonlinear differential equations. To control the convergence of the solution, a dissipation coefficient λ is introduced. The expression for the dissipation coefficient is:

[0113] , where κ max κ represents the maximum curvature of the current path segment. avg Let be the average curvature, and Δs be the rate of change of path curvature. The iteration step size is dynamically adjusted according to the rate of change of path curvature to ensure the stability and accuracy of the numerical solution. Understandably, this dynamic adjustment method can better adapt to different curvature variations of the path, improving the efficiency and accuracy of the numerical solution.

[0114] Furthermore, curvature continuity can be efficiently achieved through the aforementioned composite optimization function and quantum Monte Carlo method. This approach not only considers the geometric properties of the path but also introduces quantum effects, thereby improving the smoothness and continuity of the path. It is important to understand that this comprehensive approach can significantly improve the quality and processing efficiency of path planning.

[0115] The advantage of this embodiment lies in its ability to more accurately smooth the path through detailed curvature continuity processing steps. This not only improves the geometric quality and machining effect of the path but also enhances the reliability and stability of the optimization results. This method allows for the discovery of more reasonable path planning schemes in complex machining environments, thereby improving the continuity of CNC cutting paths and machining quality.

[0116] Example 8

[0117] To address the real-time signal processing and parameter update issues in the parameter adaptive adjustment mechanism, this embodiment further optimizes the steps of the parameter adaptive adjustment mechanism. The specific implementation is as follows:

[0118] First, the cutting force signal is acquired in real time and subjected to multi-scale wavelet packet decomposition. Multi-scale wavelet packet decomposition can decompose the cutting force signal into energy distribution features of multiple frequency bands, thereby extracting information on high-frequency energy anomalies. It is important to understand that multi-scale wavelet packet decomposition can effectively capture subtle changes in the cutting force signal, improving the sensitivity and accuracy of detection.

[0119] Furthermore, when an abnormal accumulation of high-frequency energy is detected, the weight coefficients of the optimization model are adjusted according to a pre-defined quantum-classical mapping rule. The quantum-classical mapping rule is established by relating the weight coefficient adjustment to the quantum annealing temperature. For example, the length weight adjustment term includes a temperature linear term and a periodic oscillation term, while the load weight adjustment term includes a temperature exponential term and a constant term. This ensures that the parameters can be adaptively adjusted at different processing stages to achieve the best optimization results.

[0120] Furthermore, through the aforementioned multi-scale wavelet packet decomposition and quantum-classical mapping rules, the parameters of the optimization model can be adjusted in real time, thereby improving the adaptability and robustness of path planning. It is important to understand that this adaptive adjustment method can significantly improve the flexibility and adaptability of path planning, thus maintaining stable performance in complex processing environments.

[0121] The advantage of this embodiment lies in its ability to more accurately respond to dynamic changes during the machining process through real-time signal processing and adaptive parameter adjustment mechanisms. This not only improves the adaptability of path planning but also enhances the reliability and stability of the optimization results. Using this method, more reasonable path planning schemes can be found in complex machining environments, thereby improving the stability of CNC cutting paths and machining efficiency.

[0122] Example 9

[0123] To address the issue of weight coefficient adjustment in CNC cutting path optimization, this embodiment further optimizes the quantum-classical mapping rule. The specific implementation method is as follows:

[0124] To address the issue of adaptive adjustment of weighting coefficients at different processing stages, this embodiment proposes a method for establishing the weighting coefficient adjustment based on the nonlinear relationship of quantum annealing temperature. Specifically, the length weighting adjustment term includes a temperature linear term and a periodic oscillation term, the load weighting adjustment term incorporates a temperature square root logarithmic function, and the idle time weight adopts a hyperbolic decay form based on the square of temperature.

[0125] First, define the length weight adjustment term Δw. L for:

[0126] Where T is the quantum annealing temperature, k1 and k2 are constant coefficients, f is the frequency, and ϕ is the phase. It's important to understand that the linear temperature term k1T is used to capture the global effect of temperature, while the periodic oscillation term... It is used to simulate periodic fluctuations in temperature changes, thereby better adapting to different processing conditions.

[0127] Furthermore, define the load weight adjustment term Δw. F for:

[0128] Where k3 is a constant coefficient. This can be understood as the square root logarithm of temperature. The changes are slower at lower temperatures and faster at higher temperatures. This characteristic helps to achieve smooth weight adjustments across different temperature ranges, thereby improving the stability of the processing.

[0129] Furthermore, define the idle time weighting adjustment term Δw. T for:

[0130] Where k4 and k5 are constant coefficients. It is important to understand the hyperbolic decay form of the temperature square. The value approaches 0 at lower temperatures and approaches 1 at higher temperatures. This characteristic helps to quickly adjust the idle time weight under high-temperature conditions, thereby improving processing efficiency.

[0131] Using the aforementioned quantum-classical mapping rule, the weight coefficients can be adaptively adjusted at different processing stages to achieve optimal optimization results. It's important to understand that this nonlinear relationship can more accurately reflect the impact of temperature changes on the weight coefficients, thereby improving the flexibility and adaptability of path planning.

[0132] The advantage of this embodiment lies in its ability to more accurately adapt to the needs of different processing stages by establishing the weighting coefficient adjustment based on the nonlinear relationship of quantum annealing temperature. This not only improves the adaptability of path planning but also enhances the reliability and stability of the optimization results. Through this method, more reasonable path planning schemes can be found in complex processing environments.

[0133] Example 10

[0134] To address the integration and control issues of CNC cutting path optimization systems, this embodiment further refines the quantum computing-based CNC cutting path optimization system. The specific implementation method is as follows:

[0135] To address the integration and control challenges in CNC cutting path optimization, including parameter input, quantum encoding, model building, quantum annealing, path resolution, and machining monitoring, this embodiment presents a quantum computing-based CNC cutting path optimization system.

[0136] First, the system includes a parameter input interface module for receiving workpiece geometry data and machining constraints. This data serves as the foundational input for the optimization process, ensuring the accuracy and feasibility of path planning. It is important to understand that the parameter input interface module should be designed with high precision and real-time performance to meet the demands of complex machining environments.

[0137] Furthermore, the system is equipped with a quantum encoding device connected to the parameter input interface module. The quantum encoding device comprises a non-uniform qubit allocator and a phase modulation unit. The non-uniform qubit allocator dynamically allocates qubit resources based on workpiece geometry and machining constraints to optimize quantum computing efficiency. The phase modulation unit ensures the accuracy of quantum computing by precisely controlling the phase of the quantum states. Understandably, the design of the quantum encoding device should consider both efficient utilization of qubit resources and precise control of quantum states to improve the overall system performance.

[0138] Furthermore, the system integrates an optimization model building module, which includes an Ising model generator and a dynamic constraint loader. The Ising model generator produces an Ising model suitable for quantum computing based on the input geometric data and constraints. The dynamic constraint loader updates the constraints in the model in real time according to changes during the actual processing, ensuring the dynamic adaptability of the optimization process. It is important to understand that the design of the optimization model building module should possess high flexibility and real-time performance to cope with various changes during the processing.

[0139] Furthermore, the system is equipped with a quantum annealing processing unit, which features a tunable coupler and a superconducting magnetic control device. The tunable coupler contains an array of superconducting quantum interference devices (SQIs), whose Josephson junction critical current grows logarithmically with the path complexity. A flux bias control circuit generates a modulated signal associated with the cutting parameters, and a microwave pulse generator outputs a phase-modulated waveform with QPSK encoding characteristics, the symbol rate being synchronized with the cutting force fluctuation frequency. Understandably, the quantum annealing processing unit is designed with high precision and reliability to ensure the stability and accuracy of quantum computing.

[0140] Furthermore, the system also includes a path resolution module, which comprises a quantum state measurement instrument and a probability amplitude converter. The quantum state measurement instrument measures the quantum state after quantum computation, while the probability amplitude converter converts the probability amplitude of the quantum state into the actual path planning result. It is important to understand that the path resolution module should be designed with high precision and real-time performance to ensure the accuracy and practicality of the path planning results.

[0141] Furthermore, the system deploys a machining monitoring module equipped with a three-dimensional vibration sensor and a cutting force detection array. The three-dimensional vibration sensor monitors vibration during machining, while the cutting force detection array detects changes in cutting force in real time. Understandably, the machining monitoring module is designed with high sensitivity and real-time performance to ensure the stability and safety of the machining process.

[0142] Furthermore, the system is equipped with an adaptive feedback control unit, which connects the quantum annealing processing unit and the processing monitoring module. This unit includes a quantum parameter mapping matrix and a pulse modulation circuit. The quantum parameter mapping matrix maps the data collected by the processing monitoring module to quantum computing parameters, while the pulse modulation circuit generates a corresponding modulation signal based on the mapping result to adjust the operating state of the quantum annealing processing unit in real time. It is important to understand that the adaptive feedback control unit must be designed with high precision and real-time performance to ensure the system's adaptive capability.

[0143] Through the integration and control of the above system, efficient CNC cutting path optimization can be achieved in complex machining environments. The advantage of this embodiment lies in the fact that, through detailed design and optimization of the functions of each module and the logical connections between them, the overall performance and stability of the system can be improved.

[0144] The embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made on the basis of the technical solution of the present invention should be included within the scope of protection of the present invention.

Claims

1. A CNC cutting path optimization method based on quantum computing, characterized in that, The optimization method includes the following steps: Obtain the three-dimensional geometric model of the target workpiece and a set of cutting process parameters, including tool diameter, feed rate, depth of cut, and material removal rate threshold. Discretize the three-dimensional geometric model into a three-dimensional point cloud matrix containing N path points, where N > 10. 4 ; A multi-objective optimization model is constructed with the objectives of minimizing the total path length, maximizing the tool load balance, and minimizing the idle travel time. The optimization parameters of the optimization model are encoded into a superposition state of M qubits through a qubit mapper, and the value of M is dynamically determined according to the ratio of workpiece size to tool diameter. A quantum annealing processor is used to perform a staged adiabatic evolution operation, and the magnetic field strength parameters of each stage are dynamically adjusted according to the tool motion state. The initial path sequence is generated by analyzing the quantum measurement results. The curvature of the sequence is then smoothed by a path smoother to generate an optimized path. The optimized path is then converted into CNC code and the machining process is monitored. When an abnormal vibration spectrum is detected, a parameter adaptive adjustment mechanism is triggered.

2. The CNC cutting path optimization method based on quantum computing as described in claim 1, characterized in that, The steps for constructing the multi-objective optimization model include: Establish an objective function with path length, load variance, and idle time as variables, where the weight coefficients of each variable are dynamically adjusted according to the material hardness; Set a set of constraints for the lower limit of material removal rate, the threshold of cutting force fluctuation, and the maximum turning angle; The three-dimensional point cloud matrix is ​​divided into multiple cutting subdomains according to the tool coverage area, and the size of each cutting subdomain is positively correlated with the tool diameter. A local energy function considering the quantum entanglement effect of path points is constructed for each cutting subdomain, and the energy functions of each subdomain are coupled through the quantum tunneling effect to form a global optimization model.

3. The CNC cutting path optimization method based on quantum computing as described in claim 2, characterized in that, The computational expression for the quantum entanglement effect is as follows: Where Δx, Δy are the coordinate differences between path points i and j, σ is the kernel function width related to the tool diameter, ξ is the quantum correction factor, and Q(ρ) i ,ρ j The quantum correlation degree is based on the density matrix, and its calculation expression is: , where λ (i) k This represents the quantized eigenvalue of the k-th cutting parameter at path point i, where n=3 corresponds to the three dimensions of cutting speed, feed rate, and depth of cut.

4. The CNC cutting path optimization method based on quantum computing as described in claim 3, characterized in that, The process of obtaining the quantized eigenvalues ​​includes: For each path point, a vector space containing cutting parameters is constructed, and the parameters are mapped to a three-dimensional Hilbert space using a quantum phase encoder; The quantum state is decomposed using the basis transformation method, and the projection components of each parameter dimension are calculated by selecting a specific orthogonal basis set. The decomposition results are normalized and then used as input parameters for quantum correlation.

5. The CNC cutting path optimization method based on quantum computing as described in claim 1, characterized in that, The steps of the phased adiabatic evolution operation include: During the initialization phase, the maximum transverse magnetic field is applied and its intensity is gradually reduced, while the square root time function of the longitudinal magnetic field is activated. During the mid-annealing process, a dynamic modulation coupling strength is introduced, and its modulation frequency establishes a non-linear relationship with the spindle speed. In the final stage, a spatial gradient magnetic field is applied, and the field strength distribution is correlated with the coordinates of the workpiece's center of mass to ensure that the ground state probability reaches a predetermined threshold at the end of the evolution.

6. The CNC cutting path optimization method based on quantum computing as described in claim 5, characterized in that, The step of dynamically modulating the coupling strength includes: The coupling strength modulation frequency is calculated based on the real-time rotational speed of the tool, and a rotational speed-frequency conversion relationship including harmonic components is established. A time-varying sine function is used to adjust the interaction strength between qubits, while a random quantum fluctuation term is introduced to enhance the global search capability.

7. The CNC cutting path optimization method based on quantum computing as described in claim 1, characterized in that, The curvature continuity processing steps are as follows: A composite optimization function containing a curvature square term, a quantum probability gradient term, and a fitting error term is defined, and a third-order nonlinear differential equation is derived through variational principles. The quantum Monte Carlo method is used for numerical solution, and a dissipation coefficient is introduced to control the convergence of the solution. The iteration step size is dynamically adjusted according to the rate of change of path curvature, and the expression is: Where Δs is the rate of change of path curvature, κ max κ represents the maximum curvature of the current path segment. avg Let be the mean curvature, and tanh be the hyperbolic tangent function.

8. The CNC cutting path optimization method based on quantum computing as described in claim 1, characterized in that, The steps of the parameter adaptive adjustment mechanism include: Real-time acquisition of cutting force signals and multi-scale wavelet packet decomposition are performed to extract energy distribution characteristics of specific frequency bands; When an abnormal accumulation of high-frequency energy is detected, the weight coefficients of the optimization model are adjusted according to the preset quantum-classical mapping rules. The adjustment process introduces a temperature-dependent quantum fluctuation factor to ensure that parameter updates conform to thermodynamic equilibrium conditions.

9. The CNC cutting path optimization method based on quantum computing as described in claim 8, characterized in that, The quantum-classical mapping rule includes establishing a nonlinear relationship between the weight coefficient adjustment and the quantum annealing temperature. The length weight adjustment term includes a temperature linear term and a periodic oscillation term. The load weight adjustment term introduces a temperature square root logarithmic function. The idle time weight adopts a hyperbolic decay form based on the square of the temperature.

10. A CNC cutting path optimization system based on quantum computing, used to implement the CNC cutting path optimization method based on quantum computing as described in claims 1 to 9, characterized in that, The optimization system includes: The parameter input interface module is used to receive workpiece geometric data and machining constraints. A quantum coding device, connected to a parameter input interface, includes a non-uniform quantum bit allocator and a phase modulation unit; The optimized model building module integrates the Ising model generator and dynamic constraint loader. The quantum annealing unit is equipped with a tunable coupler and a superconducting magnetron control device. The path resolution module includes a quantum state measuring instrument and a probability amplitude converter; The machining monitoring module is equipped with a three-dimensional vibration sensor and a cutting force detection array. An adaptive feedback control unit, connected to the quantum annealing processing unit and the processing monitoring module, is equipped with a quantum parameter mapping matrix and a pulse modulation circuit; The tunable coupler includes: The Josephson junction critical current of a superconducting quantum interference device array exhibits a logarithmic growth relationship with path complexity. A flux bias control circuit is used to generate a modulated signal associated with the cutting parameters; The microwave pulse generator outputs a phase-modulated waveform with QPSK encoding characteristics, and the symbol rate is synchronized with the cutting force fluctuation frequency.

Images