CNC cutting path optimization method and system based on quantum computing

Abstract

The invention relates to the technical field of numerical control machining, in particular to a CNC cutting path optimization method and system based on quantum computing. The optimization method comprises the following steps: acquiring a three-dimensional geometric model and a cutting process parameter set of a target workpiece, wherein the cutting process parameter set comprises a cutter diameter, a feeding rate, a cutting depth and a material removal rate threshold value; and constructing a multi-objective optimization model with the purposes of minimizing the total length of the path, maximizing the load balance degree of the cutter and minimizing the idle stroke time. According to the method, a quantum-classical coupled multi-objective optimization model is constructed, the spatial correlation of cutting path points is mapped into the entanglement strength between quantum bits, and the curse limitation of dimensionality of a traditional optimization algorithm is broken through by using the parallel tunneling capability of a quantum annealing processor.

Description

Technical Field

[0001] The present invention relates to the technical field of numerical control machining, and specifically to a CNC cutting path optimization method and system based on quantum computing. Background Art

[0002] The optimization of cutting paths for complex three-dimensional curved surface workpieces in precision numerical control machining has long faced the non-convex optimization problem under multi-dimensional constraints. Traditional optimization algorithms based on classical computing frameworks, such as genetic algorithms and simulated annealing, are limited by the contradiction between computational complexity and convergence when solving high-dimensional discrete path points, and it is difficult to effectively coordinate the multi-objective trade-off relationship among path length, tool load balance, and non-cutting time. Especially when dealing with the non-linear problem of coupling tool kinematic constraints and dynamic machining disturbances, existing methods often fall into local optimal solutions and cannot efficiently explore the quantum state parameter space. In addition, classical optimization models lack the mathematical representation ability for the quantization characteristics emerging in the machining process, such as the quantum tunneling effect caused by the tool-workpiece interaction, resulting in the mismatch between the path planning results and the microscopic dynamic characteristics of the physical machining system, which severely restricts the efficiency and surface quality of high-precision machining. Summary of the Invention

[0003] The present invention provides a CNC cutting path optimization method and system based on quantum computing to solve the technical problems that classical optimization models are difficult to converge to the global Pareto front in polynomial time and cannot dynamically adapt to parameter perturbations caused by quantum fluctuation effects during the machining process.

[0004] The technical solution of the present invention to solve the above technical problems is as follows: Provide a CNC cutting path optimization method based on quantum computing, and the steps of the optimization method include: Obtain the three-dimensional geometric model of the target workpiece and the set of cutting process parameters, where the set of cutting process parameters includes tool diameter, feed rate, cutting depth, and material removal rate threshold, and discretize the three-dimensional geometric model into a three-dimensional point cloud matrix containing N path points, where N > 10 4 ; Construct a multi-objective optimization model with the objectives of minimizing the total path length, maximizing the tool load balance degree, and minimizing the non-cutting time, and encode the optimization parameters of the optimization model into the superposition state of M qubits through a qubit mapper, where the value of M is dynamically determined according to the ratio of the workpiece size to the tool diameter; Adopt a quantum annealing processor to perform staged adiabatic evolution operations, and the magnetic field strength parameter of each stage is dynamically adjusted according to the tool motion state; Analyze the quantum measurement results to generate an initial path sequence, perform curvature continuous processing on the sequence through a path smoother to generate an optimized path, convert the optimized path into numerical control code and implement machining process monitoring, and trigger the parameter adaptive adjustment mechanism when an abnormal vibration spectrum is detected.

[0005] Furthermore, 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 material hardness; Set a set of constraint conditions including the lower limit of material removal rate, the threshold of cutting force fluctuation, and the maximum steering angle; Divide the three-dimensional point cloud matrix into multiple cutting sub-domains according to the tool coverage area, and the size of each cutting sub-domain is positively correlated with the tool diameter; construct a local energy function considering the quantum entanglement effect of path points for each cutting sub-domain, and the energy functions of each sub-domain are coupled through the quantum tunneling effect to form a global optimization model.

[0006] Furthermore, the calculation expression of the quantum entanglement effect is expressed as: , where Δx, Δy are the coordinate differences of path points i, j, σ is the kernel function width related to the tool diameter, ξ is the quantum correction factor, Q(ρ i , ρ j ) is the quantum correlation degree based on the density matrix, and the calculation expression is: , where λ i k represents the quantization eigenvalue of the kth cutting parameter at path point i, and n = 3 corresponds to the three dimensions of cutting speed, feed rate, and cutting depth.

[0007] Furthermore, the process for obtaining the quantization eigenvalue includes: Construct a vector space containing cutting parameters for each path point, and map each parameter to a three-dimensional Hilbert space through a quantum phase encoder; Use the basis vector transformation method to perform eigen-decomposition on the quantum state, and select a specific orthogonal basis vector set to calculate the projection components of each parameter dimension; Normalize the decomposition result and use it as the input parameter of the quantum correlation degree.

[0008] Furthermore, the steps of the staged adiabatic evolution operation include: Apply the maximum transverse magnetic field and gradually reduce its intensity during the initialization stage, and at the same time turn on the square root time function of the longitudinal magnetic field; Introduce a dynamic modulation coupling strength during the mid-annealing stage, and its modulation frequency has a non-linear correlation with the spindle speed; Apply a spatial gradient magnetic field in the final stage. The field strength distribution is associated with the coordinates of the workpiece's mass center to ensure that the ground state probability reaches a predetermined threshold at the end of the evolution.

[0009] Furthermore, the step of dynamically modulating the coupling strength includes: Calculate the coupling strength modulation frequency based on the real-time rotational speed of the cutting tool and establish a rotational speed-frequency conversion relationship including harmonic components. Adjust the interaction strength between qubits using a time-varying sine function and introduce a random quantum fluctuation term to enhance the global search ability.

[0010] Furthermore, the steps of the curvature continuity processing are as follows: Define a composite optimization function including the square term of curvature, the quantum probability gradient term, and the fitting error term, and derive a third-order nonlinear differential equation through the variational principle. Use the quantum Monte Carlo method for numerical solution, introduce a dissipation coefficient to control the convergence of the solution, and dynamically adjust the iteration step size according to the path curvature change rate. The expression is: , where Δs is the path curvature change rate, κ max is the maximum curvature of the current path segment, κ avg is the average curvature, and tanh is the hyperbolic tangent function.

[0011] Furthermore, the steps of the parameter adaptive adjustment mechanism include: Real-time collect the cutting force signal and perform multi-scale wavelet packet decomposition to extract the energy distribution characteristics of a specific frequency band. When detecting abnormal aggregation of high-frequency energy, adjust the weight coefficients of the optimization model according to the preset quantum-classical mapping rules. Introduce a temperature-related quantum fluctuation factor during the adjustment process to ensure that the parameter update conforms to the thermodynamic equilibrium condition.

[0012] Furthermore, the quantum-classical mapping rules include establishing a nonlinear relationship between the adjustment amount of the weight coefficient and the quantum annealing temperature. Among them, the length weight adjustment term includes a temperature linear term and a periodic oscillation term, the load weight adjustment term introduces a square root logarithmic function of temperature, and the idle travel time weight adopts a hyperbolic decay form of temperature squared.

[0013] On the other hand, provide a CNC cutting path optimization system based on quantum computing for implementing the above-mentioned CNC cutting path optimization method based on quantum computing. The optimization system includes: A parameter input interface module for receiving workpiece geometric data and machining constraint conditions. A quantum encoding device connected to the parameter input interface, including a non-uniform qubit allocator and a phase modulation unit. The optimization model construction module integrates an Ising model generator and a dynamic constraint loader; The quantum annealing processing unit is equipped with a tunable coupler and a superconducting magnetron device; The path parsing module includes a quantum state measurer and a probability amplitude converter; The machining monitoring module is deployed with a three-dimensional vibration sensor and a cutting force detection array; The adaptive feedback control unit is connected to the quantum annealing processing unit and the machining monitoring module, and is configured with a quantum parameter mapping matrix and a pulse modulation circuit; The tunable coupler includes: An array of superconducting quantum interference devices, whose Josephson junction critical current has a logarithmic growth relationship with the path complexity; A flux bias control circuit for generating a modulation signal associated with the cutting parameters; A microwave pulse generator outputs a phase modulation waveform with QPSK coding characteristics, and the symbol rate is synchronized with the cutting force fluctuation frequency.

[0014] The beneficial effects of the present invention are: The present invention constructs a quantum-classical coupled multi-objective optimization model, maps the spatial correlation of cutting path points to the entanglement strength between qubits, and uses the parallel tunneling ability of the quantum annealing processor to break through the dimensionality disaster limitation of traditional optimization algorithms. By dynamically adjusting the magnetic field gradient distribution during the adiabatic evolution process, the collaborative optimization of the tool motion state and the quantum ground state search is realized, and the global exploration efficiency of path planning for high-dimensional non-convex solution spaces is improved. Based on the quantum correlation degree calculation model of the density matrix, for the first time, the quantization characteristics of cutting parameters, such as the energy level transition effect of cutting speed and the phase coherence of feed rate, are incorporated into the optimization objective function, so that the curvature continuity of the path sequence automatically satisfies the smoothing condition under quantum mechanical constraints. By designing an adaptive mapping mechanism from quantum measurement results to NC codes, the system can respond in real time to the abnormal vibration spectrum during the machining process, and dynamically correct the optimization weight using the quantum parameter feedback loop to ensure the synchronous improvement of machining stability and surface accuracy.

[0015] The above description is only an overview of the technical solution of the present invention. In order to be able to understand the technical means of the present invention more clearly and implement it according to the content of the specification, the following takes the preferred embodiments of the present invention and describes them in detail with reference to the accompanying drawings. Description of the Drawings

[0016] Figure 1 It is a flowchart of the CNC cutting path optimization method in an embodiment of the present invention; Figure 2 It is a schematic diagram of the quantum annealing energy landscape evolution in an embodiment of the present invention (1 and 5); Figure 3Schematic diagram of the Pareto front of multi-objective optimization in an embodiment of the present invention (2); Figure 4 Quantum correlation degree J in an embodiment of the present invention ij Schematic diagram of the spatial distribution (3); Figure 5 Schematic diagram of the projection of the quantum state space in an embodiment of the present invention (4); Figure 6 Schematic diagram of curvature adaptive step size adjustment in an embodiment of the present invention (7); Figure 7 Schematic diagram of the frequency domain analysis of the cutting force signal in an embodiment of the present invention (8); Figure 8 Schematic diagram of temperature-dependent weight adjustment in an embodiment of the present invention (9); Figure 9 Schematic diagram of the coupling strength - tool diameter relationship in an embodiment of the present invention (10). Detailed implementation manners

[0017] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0018] The term "including" and any variation thereof in the specification and claims of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices. In addition, the use of "and / or" in the specification and claims means at least one of the connected objects. For example, A and / or B means including three cases: A alone, B alone, and both A and B exist.

[0019] In the embodiments of the present invention, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific manner.

[0020] The present invention provides the following preferred embodiments: Embodiment 1 To solve the contradiction 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, a three-dimensional path optimization method based on quantum computing.

[0021] As Figure 1 and Figure 2 shown, the steps of the cutting path optimization method are as follows: 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, cutting depth, 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 .

[0022] S200. Construct a multi-objective optimization model with the objectives of minimizing the total path length, maximizing the tool load balance degree, and minimizing the idle travel time. Encode the optimization parameters of the optimization model into a superposition state of M qubits through a quantum bit mapper. The value of M is dynamically determined according to the ratio of the workpiece size to the tool diameter.

[0023] S300. Use a quantum annealing processor to perform a staged adiabatic evolution operation, and the magnetic field strength parameter of each stage is dynamically adjusted according to the tool motion state.

[0024] S400. Analyze the quantum measurement results to generate an initial path sequence, perform curvature continuity processing on the sequence through a path smoother to generate an optimized path, convert the optimized path into numerical control code and implement machining process monitoring, and trigger a parameter adaptive adjustment mechanism when an abnormal vibration spectrum is detected.

[0025] First, obtain the three-dimensional geometric model of the target workpiece and the set of cutting process parameters. The set includes tool diameter, feed rate, cutting depth, and material removal rate threshold. Through computer-aided design (CAD) software, discretize the three-dimensional geometric model into a three-dimensional 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.

[0026] Furthermore, construct a multi-objective optimization model with the objectives of minimizing the total path length, maximizing the tool load balance degree, and minimizing the idle travel time. To achieve this goal, use a quantum bit mapper to encode the optimization parameters of the optimization model into a superposition state of M qubits. The value of M is dynamically determined according to the ratio of the workpiece size to the tool diameter, which ensures that the number of optimization parameters matches the actual machining requirements, improving the flexibility and adaptability of the optimization. It should be understood that this dynamically determined method can effectively meet the optimization requirements of workpieces of different sizes, thereby improving the applicability of the optimization results.

[0027] Furthermore, a quantum annealing processor is used to perform staged adiabatic evolution operations. The magnetic field strength parameters for each stage are dynamically adjusted according to the tool motion state. This process is achieved through the superconducting magnetic control device in the quantum annealing processor, ensuring precise control of the magnetic field strength. It can be understood that dynamically adjusting the magnetic field strength parameters can better adapt to the tool motion state in different machining stages, thereby improving the stability and efficiency of the optimization process.

[0028] Furthermore, the quantum measurement results are analyzed to generate an initial path sequence, and the sequence is processed by a path smoother for curvature continuity to generate an optimized path. The path smoother uses advanced mathematical methods such as cubic spline interpolation or Bezier curve fitting to ensure the smoothness and continuity of the path. The generated optimized path is then converted into numerical control code and the machining process is monitored. When an abnormal vibration spectrum is detected, a parameter adaptive adjustment mechanism is triggered. This mechanism can analyze the cutting force signal collected in real time and adjust the optimization parameters in a timely manner when an abnormal vibration is detected, ensuring the stability of the machining process and the surface quality.

[0029] The benefits of this embodiment are that by combining quantum computing technology, the efficiency and accuracy of CNC cutting path optimization are significantly improved. Utilizing the parallel tunneling ability of the quantum annealing processor, the global optimal solution can be found in polynomial time, overcoming the limitations of traditional optimization algorithms in high-dimensional non-convex optimization problems. At the same time, 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 the actual machining conditions, thereby improving the stability of machining and the surface quality. Through the method of this embodiment, not only can the multi-objective trade-off relationship among path length, tool load balance, and idle travel time be effectively coordinated, but also various parameter perturbations can be dynamically adapted during the machining process, further enhancing the efficiency and reliability of precision CNC machining.

[0030] Embodiment 2 To address the limitations of traditional optimization models in the multi-objective trade-off relationship, this embodiment further optimizes the construction steps of the multi-objective optimization model. The specific implementation method is as follows: First, an objective function with path length, load variance, and idle travel time as variables is established. 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 the path length can be appropriately increased and the weight coefficient of the idle travel time can be reduced to ensure the stability and machining quality of the tool on high-hardness materials. It should be understood that this dynamic adjustment mechanism can better adapt to the machining requirements of different materials, improving the applicability and flexibility of the optimization results.

[0031] Furthermore, a set of constraint conditions for the lower limit of material removal rate, the threshold of cutting force fluctuation, and the maximum steering angle is set. These constraint conditions ensure the physical feasibility and machining safety during the optimization process. For example, the lower limit of material removal rate guarantees the machining efficiency, the threshold of cutting force fluctuation prevents excessive cutting force from damaging the tool and the workpiece, and the maximum steering angle limits the movement range of the tool, avoiding excessive mechanical stress. It can be understood that these constraint conditions work together to ensure the rationality and reliability of the optimization results.

[0032] Furthermore, the three-dimensional point cloud matrix is divided into multiple cutting sub-domains according to the tool coverage area. The size of each cutting sub-domain is positively correlated with the tool diameter. This division method 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 sub-domain. These local energy functions are coupled through the quantum tunneling effect to form a global optimization model. It should be understood that the introduction of the quantum entanglement effect and the quantum tunneling effect enables the optimization model to more effectively explore the high-dimensional solution space, thereby finding the global optimal solution.

[0033] The benefits of this embodiment are that by dynamically adjusting the weight coefficients of the objective function and setting reasonable constraint conditions, the diversity and practicality of the optimization results are ensured. At the same time, by dividing the three-dimensional point cloud matrix into multiple cutting sub-domains and introducing the quantum entanglement effect and the quantum tunneling effect, the global search ability 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 the multi-objective trade-off relationship, thereby enhancing the overall effect of CNC cutting path optimization.

[0034] Embodiment Three To solve the problem of spatial correlation between path points, this embodiment further refines the calculation expression of the quantum entanglement effect. The specific implementation is as follows: First, the calculation expression of the quantum entanglement effect is defined as: , where Δx and Δy are the coordinate differences between path points i and j, σ is the kernel function width related to the tool diameter, and ξ is the quantum correction factor. It should be understood that this expression measures the spatial distance between path points through the Gaussian kernel function and introduces the quantum correction factor to adjust the intensity of the quantum entanglement effect.

[0035] Furthermore, the quantum correlation degree based on the density matrix is defined, and the expression is: , where λ i kIt represents the quantization eigenvalue of the k-th cutting parameter at path point i. When n = 3, it corresponds to three dimensions: cutting speed, feed rate, and cutting depth. It can be understood that through the interaction of quantization eigenvalues, this calculation method quantifies the quantum correlation degree between path points, thereby considering more physical factors in the optimization process.

[0036] Furthermore, through the above expression, the quantum entanglement effect between path points can be accurately described. This description method not only considers the spatial distance between path points but also introduces the quantum correlation degree in quantum mechanics, enabling the optimization model to more comprehensively reflect the physical characteristics in the actual machining process. It should be understood that this comprehensive consideration method can improve the accuracy of the optimization result.

[0037] The benefit of this embodiment is that through the detailed calculation expression of the quantum entanglement effect, the spatial correlation between path points can be more accurately described. This not only improves the physical meaning of the optimization model but also enhances the reliability and accuracy of the optimization result. Through the method of this embodiment, a more reasonable path planning scheme can be found in a complex machining environment, thereby improving the effect of CNC cutting path optimization.

[0038] Embodiment Four To solve the problem of obtaining quantization eigenvalues, this embodiment further refines the process of obtaining quantization eigenvalues. The specific implementation method is as follows: First, a vector space containing cutting parameters is constructed for each path point. These cutting parameters include cutting speed, feed rate, and cutting depth. Through a quantum phase encoder, these parameters are mapped to a three-dimensional Hilbert space. It should be understood that this mapping method can transform cutting parameters into quantum states, thereby utilizing the advantages of quantum computing for processing.

[0039] Furthermore, the basis transformation method is used to perform eigen-decomposition on the quantum state. A specific set of orthogonal basis vectors is selected to calculate the projection components of each parameter dimension. This decomposition method can represent the quantum state as a linear combination of a set of basis vectors, thereby extracting the characteristic information of each parameter dimension. It can be understood that this eigen-decomposition method can more precisely describe the structure of the quantum state and improve the accuracy of quantization eigenvalues.

[0040] Furthermore, after normalizing the decomposition result, it is used as the input parameter of the quantum correlation degree. The normalization process ensures the consistency and comparability of eigenvalues, making the calculation of the quantum correlation degree more accurate. It should be understood that the normalization process is an important step in data preprocessing, which can eliminate the influence brought by different parameter scales and improve the stability of the calculation result.

[0041] The benefit of this embodiment is that through the detailed process of obtaining quantization eigenvalues, the quantization characteristics of cutting parameters can be described more accurately. This obtaining method not only improves the accuracy of quantum correlation degree calculation but also enhances the physical meaning of the optimization model.

[0042] Embodiment Five As Figure 2 shown, to solve the efficiency problem of the quantum annealing processor during the adiabatic evolution operation, this embodiment further refines the steps of the staged adiabatic evolution operation. The specific implementation method is as follows: First, in the initialization stage, apply the maximum transverse magnetic field and gradually reduce its intensity, while turning on the square root time function of the longitudinal magnetic field. This initialization method ensures a smooth transition of the quantum system from the initial state to the intermediate state, reducing unnecessary energy loss. It should be understood that the combined control of the transverse magnetic field and the longitudinal magnetic field can effectively guide the evolution process of the quantum system.

[0043] Furthermore, in the middle stage of annealing, introduce a dynamic modulation of the coupling strength, and its modulation frequency has a non-linear correlation with the spindle speed. This dynamic modulation method can better adapt to the motion state of the tool in different machining stages, improving the stability and efficiency of the optimization process. It can be understood that by dynamically modulating the coupling strength, the optimal quantum state evolution can be achieved in different machining stages, thus finding a better path planning scheme.

[0044] Furthermore, in the final stage, apply a spatial gradient magnetic field, and the field strength distribution is correlated with the workpiece mass center coordinates to ensure that the ground state probability reaches a predetermined threshold at the end of the evolution. This application method of the spatial gradient magnetic field can guide the quantum system to finally converge to the global optimal solution. It should be understood that through the precise control of the spatial gradient magnetic field, the high quality and high precision of the optimization result can be ensured.

[0045] The benefit of this embodiment is that through the staged adiabatic evolution operation, the quantum system can be efficiently guided to smoothly transition from the initial state to the global optimal solution. This operation method not only improves the operation efficiency of the quantum annealing processor but also enhances the stability and reliability of the optimization result.

[0046] Embodiment Six To solve the efficiency and global search ability problems of the quantum annealing processor during the dynamic modulation of the coupling strength, this embodiment further optimizes the steps of the dynamic modulation of the coupling strength. The specific implementation method is as follows: First, calculate the coupling strength modulation frequency based on the real-time rotational speed of the tool, and establish a rotational speed-frequency conversion relationship that includes harmonic components. This conversion relationship is determined by the Fourier analysis method and can accurately capture the frequency characteristics at different rotational speeds. It should be understood that introducing harmonic components can better simulate the complex motion states in the actual machining process, thereby improving the adaptability and accuracy of the modulation frequency.

[0047] Furthermore, use a time-varying sine function to adjust the interaction strength between qubits. The form of the time-varying sine function is: 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 machining stages. At the same time, introduce a random quantum fluctuation term to enhance the global search ability. The random quantum fluctuation term is implemented through 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 the standard normal distribution. It can be understood that the random quantum fluctuation term can break the bondage of local optimal solutions and improve the efficiency and success rate of global search.

[0048] Furthermore, combine the above modulation frequency and harmonic components, as well as the time-varying sine function and random quantum fluctuation term to form a complete dynamic modulation coupling strength model. This model can not only accurately control the interaction strength between qubits but also perform efficient search globally. It should be understood that this comprehensive modulation method can significantly improve the operating efficiency of the quantum annealing processor and the quality of the optimization results.

[0049] The benefit of this embodiment is that through the detailed steps of dynamic modulation of the coupling strength, the interaction strength between qubits can be controlled more accurately, and the global search ability can be enhanced. This not only improves the stability and reliability of the optimization process but also ensures the high quality of the optimization results. Through this method, a more reasonable path planning scheme can be found in a complex machining environment, thereby improving the smoothness and machining accuracy of the CNC cutting path.

[0050] Embodiment Seven To solve the numerical solution problem in the continuous curvature processing of the path, this embodiment further refines the steps of continuous curvature processing. The specific implementation method is as follows: First, define a composite optimization function that includes a curvature squared term, a quantum probability gradient term, and a fitting error term. The form of this composite optimization function is F(s) = ακ²(s) + β∇P(s) + γE(s), where α, β, and γ are weight 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 through the variational principle to describe the curvature change of the path. It should be understood 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.

[0051] Furthermore, the quantum Monte Carlo method is used for numerical solution. The quantum Monte Carlo method can efficiently solve high-dimensional nonlinear differential equations through random sampling techniques. To control the convergence of the solution, a dissipation coefficient λ is introduced. The expression for the dissipation coefficient is: , where κ max is the maximum curvature of the current path segment, κ avg is the average curvature, and Δs is the path curvature change rate. The iteration step size is dynamically adjusted according to the path curvature change rate to ensure the stability and accuracy of the numerical solution. It can be understood that this dynamic adjustment method can better adapt to different curvature changes of the path, improving the efficiency and accuracy of the numerical solution.

[0052] Furthermore, through the above composite optimization function and the quantum Monte Carlo method, curvature continuousization processing can be efficiently performed. This processing method not only considers the geometric characteristics of the path but also introduces quantum effects, thereby improving the smoothness and continuity of the path. It should be understood that this comprehensive consideration method can significantly improve the quality of path planning and the processing effect.

[0053] The benefit of this embodiment is that through detailed curvature continuousization processing steps, the path can be smoothed more accurately. This not only improves the geometric quality and processing effect of the path but also enhances the reliability and stability of the optimization results. Through this method, a more reasonable path planning scheme can be found in a complex processing environment, thereby improving the continuity and processing quality of the CNC cutting path.

[0054] Embodiment Eight To solve the problems of real-time signal processing and parameter update in the parameter adaptive adjustment mechanism, this embodiment further optimizes the steps of the parameter adaptive adjustment mechanism. The specific implementation is as follows: First, the cutting force signal is collected 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 characteristics in multiple frequency bands, thereby extracting information on abnormal aggregation of high-frequency energy. It should be understood that multi-scale wavelet packet decomposition can effectively capture subtle changes in the cutting force signal, improving the sensitivity and accuracy of detection.

[0055] Furthermore, when abnormal aggregation of high-frequency energy is detected, the weight coefficients of the optimization model are adjusted according to the preset quantum-classical mapping rules. The quantum-classical mapping rules are implemented by establishing the relationship between the adjustment amount of the weight coefficients and the quantum annealing temperature. For example, the length weight adjustment term includes a temperature linear term and a periodic oscillation term, and the load weight adjustment term includes a temperature exponential term and a constant term. This can ensure that the parameters can be adaptively adjusted at different processing stages to achieve the best optimization effect.

[0056] Furthermore, through the above 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 should be understood that this adaptive adjustment method can significantly improve the flexibility and adaptability of path planning, so as to maintain stable performance in a complex processing environment.

[0057] The benefit of this embodiment is that through real-time signal processing and parameter adaptive adjustment mechanism, it can more accurately cope with the dynamic changes in the processing process. This not only improves the adaptability of path planning, but also enhances the reliability and stability of the optimization results. Through this method, a more reasonable path planning scheme can be found in a complex processing environment, thereby improving the stability and processing efficiency of the CNC cutting path.

[0058] Embodiment Nine To solve the problem of weight coefficient adjustment in CNC cutting path optimization, this embodiment further optimizes the quantum-classical mapping rules. The specific implementation method is as follows: To solve the problem of adaptive adjustment of weight coefficients in different processing stages, this embodiment proposes a method for establishing the adjustment amount of weight coefficients based on the non-linear relationship of quantum annealing temperature. Specifically, 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.

[0059] First, define the length weight adjustment term Δw L as: , where T is the quantum annealing temperature, k1 and k2 are constant coefficients, f is the frequency, and ϕ is the phase. It should be understood that the temperature linear term k1T is used to capture the global influence of temperature, while the periodic oscillation term It is used to simulate the periodic fluctuations in temperature changes, so as to better adapt to different processing conditions.

[0060] Furthermore, define the load weight adjustment term Δw F as: , where k3 is a constant coefficient. It can be understood that the square root logarithmic function of temperature changes slowly at low temperatures and quickly at high temperatures. This characteristic helps to achieve smooth weight adjustment in different temperature ranges, thereby improving the stability of the processing process.

[0061] Furthermore, define the rapid traverse time weight adjustment term Δw T as: , where k4 and k5 are constant coefficients. It should be understood that the hyperbolic decay form of the square of temperature is close to 0 at low temperatures and close to 1 at high temperatures. This characteristic helps to quickly adjust the rapid traverse time weight under high temperature conditions, thereby improving the processing efficiency.

[0062] Through the above quantum-classical mapping rules, the weight coefficients can be adaptively adjusted in different processing stages to achieve the best optimization effect. It should be understood that this non-linear relationship can more accurately reflect the influence of temperature changes on the weight coefficients, thereby improving the flexibility and adaptability of path planning.

[0063] The benefit of this embodiment is that by establishing the weight coefficient adjustment amount based on the non-linear relationship of the quantum annealing temperature, it can more accurately meet the requirements of different processing stages. This not only improves the self-adaptability of path planning, but also enhances the reliability and stability of the optimization results. Through this method, a more reasonable path planning scheme can be found in a complex processing environment.

[0064] Embodiment Ten To solve the integration and control problems of the CNC cutting path optimization system, this embodiment further refines the CNC cutting path optimization system based on quantum computing. The specific implementation is as follows: To solve the integration and control problems of parameter input, quantum encoding, model construction, quantum annealing processing, path parsing, and processing monitoring in the CNC cutting path optimization process, this embodiment designs a CNC cutting path optimization system based on quantum computing.

[0065] First, the system includes a parameter input interface module for receiving workpiece geometric data and machining constraint conditions. These data will serve as the basic input for the optimization process to ensure the accuracy and feasibility of path planning. It should be understood that the parameter input interface module should be designed with high precision and real-time performance to meet the requirements in complex machining environments.

[0066] Furthermore, the system is equipped with a quantum encoding device connected to the parameter input interface module. The quantum encoding device includes a non-uniform qubit allocator and a phase modulation unit. The non-uniform qubit allocator dynamically allocates qubit resources according to the workpiece geometric data and machining constraint conditions to optimize the efficiency of quantum computing. The phase modulation unit ensures the accuracy of quantum computing by precisely controlling the phase of the quantum state. It can be understood that the design of the quantum encoding device should consider the efficient utilization of qubit resources and the precise control of the quantum state to improve the performance of the overall system.

[0067] Furthermore, the system integrates an optimization model construction module, which includes an Ising model generator and a dynamic constraint loader. The Ising model generator generates an Ising model suitable for quantum computing based on the input geometric data and constraint conditions. The dynamic constraint loader updates the constraint conditions in the model in real time according to the changes in the actual machining process to ensure the dynamic adaptability of the optimization process. It should be understood that the optimization model construction module should be designed with high flexibility and real-time performance to cope with various changes in the machining process.

[0068] Furthermore, the system is configured with a quantum annealing processing unit, which is equipped with a tunable coupler and a superconducting magnetron device. The tunable coupler includes an array of superconducting quantum interference devices, and the critical current of its Josephson junction has a logarithmic growth relationship with the path complexity. The flux bias control circuit is used to generate a modulation signal associated with the cutting parameters, and the microwave pulse generator outputs a phase modulation waveform with QPSK encoding characteristics, and the symbol rate is synchronized with the cutting force fluctuation frequency. It can be understood that the design of the quantum annealing processing unit should have high precision and high reliability to ensure the stability and accuracy of quantum computing.

[0069] Furthermore, the system also includes a path parsing module, which includes a quantum state measurer and a probability amplitude converter. The quantum state measurer is used to measure the quantum state after quantum computing, and the probability amplitude converter converts the probability amplitude of the quantum state into the actual path planning result. It should be understood that the path parsing module should be designed with high precision and real-time performance to ensure the accuracy and practicality of the path planning result.

[0070] Furthermore, the system deploys a machining monitoring module, which is equipped with a three-dimensional vibration sensor and a cutting force detection array. The three-dimensional vibration sensor is used to monitor the vibration during the machining process, while the cutting force detection array is used to detect the change of the cutting force in real time. It can be understood that the design of the machining monitoring module should have high sensitivity and real-time performance to ensure the stability and safety of the machining process.

[0071] Furthermore, the system configures an adaptive feedback control unit, which is connected to the quantum annealing processing unit and the machining monitoring module, and is equipped with a quantum parameter mapping matrix and a pulse modulation circuit. The quantum parameter mapping matrix is used to map the data collected by the machining monitoring module to the quantum computing parameters, and the pulse modulation circuit generates corresponding modulation signals according to the mapping results to adjust the working state of the quantum annealing processing unit in real time. It should be understood that the design of the adaptive feedback control unit should have high precision and real-time performance to ensure the adaptive ability of the system.

[0072] Through the integration and control of the above system, efficient CNC cutting path optimization can be achieved in a complex machining environment. The benefit of this embodiment is that by designing and optimizing the functions of each module and the logical connection relationships between them in detail, the overall performance and stability of the system can be improved.

[0073] The above-described embodiments have further elaborated on the object, technical solution, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made on the basis of the technical solution of the present invention shall be included within the protection scope of the present invention.

Claims

1. A CNC cutting path optimization method based on quantum computing, characterized in that: The steps of the optimization method include: Obtain a 3D geometric model of the target workpiece and a set of cutting process parameters, wherein the set of cutting process parameters includes tool diameter, feed rate, cutting depth and material removal rate threshold, discretize the 3D geometric model into a 3D point cloud matrix containing N path points, wherein N>10 4 ; A multi-objective optimization model with the objectives of minimizing the total path length, maximizing the tool load balance and minimizing the idle travel time is constructed, and the optimization parameters of the optimization model are encoded into a superposition state of M quantum bits through a quantum bit mapper, where the value of M is dynamically determined according to the ratio of the workpiece size to the tool diameter; A quantum annealing processor is used to perform a staged adiabatic evolution operation, and the magnetic field intensity parameters of each stage are dynamically adjusted according to the tool motion state; The quantum measurement results are analyzed to generate an initial path sequence, which is then processed by a path smoother to generate an optimized path. The optimized path is converted into numerical control 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 claimed in claim 1, characterized in that: The steps of constructing the multi-objective optimization model include: An objective function with path length, load variance and idle time as variables is established, where the weight coefficient of each variable is dynamically adjusted according to the material hardness; Set a set of constraints including a lower limit of material removal rate, a threshold of cutting force fluctuation, and a maximum steering 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 the 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 claimed in claim 2, characterized in that: The calculation expression of the quantum entanglement effect is expressed as: , where Δx, Δy are the coordinate differences of path points i and j, σ is the kernel function width related to the tool diameter, ξ is the quantum correction factor, Q(ρ i ,ρ j ) is the quantum correlation degree based on the density matrix, and the calculation expression is: , where λ i k It represents the quantized eigenvalue of the kth cutting parameter at path point i, and n=3 corresponds to the three dimensions of cutting speed, feed rate, and cutting depth.

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

5. The CNC cutting path optimization method based on quantum computing as claimed in claim 1, characterized in that: The steps of the staged adiabatic evolution operation include: In the initialization phase, the maximum transverse magnetic field is applied and the intensity is gradually reduced, while the square root time function of the longitudinal magnetic field is turned on; Dynamic modulation coupling strength is introduced in the middle stage of annealing, and its modulation frequency establishes a nonlinear relationship with the spindle speed; In the final stage, a spatial gradient magnetic field is applied, and the field intensity distribution is associated with the coordinates of the workpiece mass center 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 claimed in claim 5, characterized in that: The step of dynamically modulating the coupling strength comprises: The coupling intensity modulation frequency is calculated according to the real-time rotation speed of the tool, and a rotation speed-frequency conversion relationship including harmonic components is established; A time-varying sine function is used to adjust the interaction strength between quantum bits, and 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 claimed in claim 1, characterized in that: The steps of curvature continuity processing are: Define a composite optimization function including the squared curvature term, quantum probability gradient term and fitting error term, and derive the third-order nonlinear differential equation through the variational principle; The quantum Monte Carlo method is used for numerical solution, and the 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 the path curvature. The expression is: , where Δs is the rate of change of path curvature, κ max is the maximum curvature of the current path segment, κ avg is the mean curvature, and tanh is the hyperbolic tangent function.

8. The CNC cutting path optimization method based on quantum computing as claimed in claim 1, characterized in that: The steps of the parameter adaptive adjustment mechanism include: Collect cutting force signals in real time and perform multi-scale wavelet packet decomposition to extract energy distribution characteristics of specific frequency bands; When abnormal high-frequency energy accumulation is detected, the weight coefficient of the optimization model is adjusted according to the preset quantum-classical mapping rules; The adjustment process introduces temperature-related quantum fluctuation factors to ensure that parameter updates meet thermodynamic equilibrium conditions.

9. The CNC cutting path optimization method based on quantum computing as claimed in claim 8, characterized in that: The quantum-classical mapping rule includes establishing a nonlinear relationship between the weight coefficient adjustment amount 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 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 comprises: Parameter input interface module, used to receive workpiece geometry data and processing constraints; A quantum encoding device, connected to the parameter input interface, comprising a non-uniform quantum bit distributor and a phase modulation unit; Optimization model building module, which integrates Ising model generator and dynamic constraint loader; A quantum annealing processing unit, equipped with a tunable coupler and superconducting magnetron; A path resolution module, including a quantum state meter and a probability amplitude converter; The processing 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 configured with a quantum parameter mapping matrix and a pulse modulation circuit; The tunable coupler comprises: Superconducting quantum interference device array, the Josephson junction critical current grows logarithmically with the path complexity; A flux bias control circuit for generating a modulation signal associated with a cutting parameter; The microwave pulse generator outputs a phase modulated waveform with QPSK coding characteristics, and the symbol rate keeps a synchronous relationship with the cutting force fluctuation frequency.

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