Optimization method and system based on intelligent numerical control machine tool machining process parameters

Abstract

The invention relates to the technical field of data processing, in particular to an optimization method and system based on machining process parameters of an intelligent numerical control machine tool, and the method comprises the steps: collecting the feeding speed of the numerical control machine tool at each moment, setting the initial disturbance range of a simulated annealing algorithm in the process of iteratively searching the optimal solution of the feeding speed at each moment, according to the method, the initial disturbance range is dynamically adjusted according to the fluctuation degree of the reference data segment and the reference data segment of the feeding speed at each moment, and in the iteration process of each round, an objective function is constructed to evaluate the optimization degree of a current solution and a new solution so as to determine the disturbance range of the current solution, optimization of the iteration search process is achieved, and the accuracy of the iteration search process is improved. The optimal processing parameters at each acquisition moment are obtained, so that the algorithm can find the optimal feeding speed at each acquisition moment more quickly and more accurately, and the optimization efficiency of the processing technological parameters is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and more particularly to an optimization method and system based on machining process parameters of intelligent numerical control machine tools. Background Art

[0002] As the core pillar of the equipment manufacturing industry, the technical level of CNC machine tools has a decisive influence on the competitive landscape in the industrial field. During the operation of CNC machine tools, the key process parameter of machine tool feed speed has traditionally been set by manual experience. In actual processing practice, due to the different materials of different workpieces, complex and changeable processing technology, and the rich and diverse types of tools, many factors are superimposed on each other, making it difficult for the feed speed value determined solely by manual experience to adapt to the dynamically changing processing requirements.

[0003] The prior art obtains the optimal solution for the feed speed of a CNC machine tool through a simulated annealing algorithm. The algorithm is based on simulating the solid heating and cooling process, and searches for the global optimal solution in a random search space. The randomness of the search is gradually reduced by controlling the temperature to achieve parameter optimization. The metal cutting processing path planning system disclosed in the existing Chinese patent application document with the publication number CN119126668A covers multiple functional modules: the image acquisition module is responsible for capturing multi-angle high-resolution images of the metal workpiece to be processed; the three-dimensional reconstruction module uses these images to reconstruct a three-dimensional model; the cutting simulation module simulates the three-dimensional model in a virtual environment and records the stress and heat distribution during the cutting process in real time; the intelligent segmentation module divides the processing process into multiple stages based on stress and heat data; the comprehensive optimization module combines genetic algorithms and simulated annealing algorithms to optimize the cutting path, depth and feed speed of each processing stage; the feedback adjustment module monitors the stress and heat changes in the actual processing, and automatically adjusts the cutting parameters or suspends the processing once the threshold is exceeded.

[0004] However, when the above technical solution uses the simulated annealing algorithm to solve the feed speed, there is a key problem: the disturbance range of the simulated annealing algorithm is pre-set and fixed, which cannot adapt to different processing task scenarios, reduces the processing accuracy, and affects the processing effect of the CNC machine tool. Specifically, when the fixed disturbance range is faced with a relatively simple processing task and a relatively obvious regularity, the algorithm will converge to the optimal solution very slowly, and a lot of time will be wasted unnecessarily; and when dealing with tasks with high complexity and strict processing requirements, it is easy to miss potential better solutions due to insufficient search. Summary of the invention

[0005] In order to solve the problem that when obtaining the optimal feed speed of a CNC machine tool, the traditional simulated annealing algorithm cannot adapt to different processing task scenarios due to the fixed disturbance range, which reduces the processing accuracy and affects the processing effect of the CNC machine tool, the present invention proposes an optimization method based on the processing parameters of an intelligent CNC machine tool, including: Collecting the feed speed of the CNC machine tool at each moment, setting the initial disturbance range of the simulated annealing algorithm in the process of iteratively searching for the optimal solution of the feed speed at each moment, and the reference data segment of the feed speed at each moment, analyzing the fluctuation degree of the reference data segment, dynamically adjusting the initial disturbance range using the fluctuation degree, and obtaining the adjusted disturbance range; In the process of iterative search, the preferred degree of the current solution generated in each round and the new solution obtained by perturbing the current solution are evaluated, and the disturbance range requirement factor of the current solution is determined based on the difference in preferred degree between the current solution and the new solution. The disturbance range requirement factor is used to reflect the size of the adjustment requirement of the current solution; The disturbance range requirement factor of the current solution generated in each round is used to correct the adjusted disturbance range, and the disturbance range of the current solution generated in each round is obtained, so as to optimize the process of iterative search for the optimal solution of the feed speed at each moment by the simulated annealing algorithm, obtain the optimal solution of the feed speed at each moment, and optimize the machining process parameters of the CNC machine tool according to the optimal solution of the feed speed at each moment.

[0006] In the above technical solution, the initial disturbance range provides a starting exploration space for the search of the simulated annealing algorithm, which is the basis for the algorithm to start optimizing. The reference data segment uses historical data to reflect the state of the feed speed. A large fluctuation means that the processing conditions are complex and a larger disturbance range is required to explore a wider solution space. If the fluctuation is small, it means that the processing state is relatively stable, and the disturbance range can be reduced to accelerate the convergence of the algorithm. By dynamically adjusting the disturbance range, the simulated annealing algorithm can adapt to different processing scenarios. Under complex working conditions, it maintains sufficient search capabilities to avoid missing the optimal solution, thereby improving the overall processing efficiency and quality. And further, in the process of iterative search for the optimal solution, the disturbance range adjusted based on the fluctuation degree is combined with the disturbance range requirement factor determined based on the preferred degree of the current solution and the new solution, so that the algorithm can dynamically adjust the disturbance range according to the quality of the current solution and the new solution. This search strategy can find the optimal solution at each moment faster and more accurately, and optimize the process parameters of the CNC machine tool during the processing based on the optimal solution at each moment, so that it maintains the best operating state and ensures the processing effect of the CNC machine tool.

[0007] Preferably, the perturbation range of the current solution generated in each round is determined based on the following method: The disturbance range adjusted by the simulated annealing algorithm in the process of iteratively searching for the optimal solution of the feed speed at each moment is multiplied by the disturbance range requirement factor of the current solution generated in each round to realize the correction of the adjusted disturbance range, and the multiplied value is used as the disturbance range of the current solution generated in each round.

[0008] The above technical solution combines the adjusted disturbance range with the disturbance range requirement factor of the current solution. In different processing scenarios and iteration rounds, the algorithm can flexibly adjust the disturbance range according to actual conditions.

[0009] Preferably, the method for evaluating the degree of preference of the current solution generated in each round and the new solution obtained by perturbing the current solution is: Construct an objective function to reflect the preference of the current solution / new solution:

[0010] in, is the objective function value of the current solution / new solution. Both the current solution / new solution are feed speed, and the objective function value is negatively correlated with the optimization degree. is the energy consumption per unit time corresponding to the current solution / new solution, is the unit time capacity corresponding to the current solution / new solution.

[0011] The above technical solution takes energy consumption per unit time and production capacity into consideration by constructing a specific objective function, which can intuitively reflect the advantages and disadvantages of the current solution and the new solution, and provide a quantitative basis for deciding whether to accept the new solution. Since the objective function value is negatively correlated with the degree of optimization, the quality of different solutions can be clearly compared during the iteration process, guiding the algorithm to search in a better direction, which helps to improve the optimization efficiency.

[0012] Preferably, the disturbance range requirement factor of the current solution satisfies the following relationship:

[0013] In the formula, It is an iterative search In the process of finding the optimal solution for the feed speed at the moment, The perturbation range requirement factor of the current solution generated by the round iteration, is the normalization function, and Respectively The objective function values ​​of the current solution and the new solution generated by the round iteration, Iterate to search for In the process of finding the optimal solution for the feed speed at the moment, The temperature value after the round iteration, Iterate to search for The temperature threshold is set during the process of finding the optimal solution for the feed speed at each acquisition moment.

[0014] The above technical solution can accurately reflect the size of the adjustment requirement of the current solution by incorporating the difference in the objective function value between the current solution and the new solution generated in the previous round, as well as factors such as the temperature value.

[0015] Preferably, the fluctuation degree of the reference data segment is determined by: Among all the feed speeds included in the reference data segment of the feed speed at each moment, if a certain feed speed has a numerical difference with a previous feed speed, the feed speed is marked, and the numerical change of the marked feed speed is obtained; Calculate the volatility: ; In the formula, For the The fluctuation degree of the reference data segment of the feed speed at each moment, is the normalization function, is the number of feed rates within the reference data segment, For the The total number of all marked feed speeds in the reference data segment of the feed speed at the moment, For the The variance of the numerical changes of all marked feed speeds in the reference data segment of the feed speed at a moment.

[0016] The above technical solution not only considers the numerical change of the feed speed, but also considers the proportion of the feed speeds that have undergone numerical changes. This comprehensive consideration of multiple factors avoids the one-sidedness brought about by a single factor evaluation and more comprehensively and accurately quantifies the fluctuation characteristics of the reference data segment.

[0017] Preferably, the method of dynamically adjusting the initial disturbance range by using the fluctuation degree to obtain the adjusted disturbance range is: The initial disturbance range in the process of iteratively searching for the optimal solution of the feed speed at each moment by the simulated annealing algorithm is multiplied by the fluctuation degree of the reference data segment of the feed speed at each moment to achieve dynamic adjustment of the initial disturbance range, and the multiplied value is used as the adjusted disturbance range.

[0018] The above technical solution dynamically adjusts the disturbance range according to the fluctuation characteristics of the actual processing data by multiplying the fluctuation degree with the initial disturbance range, which is beneficial to avoid falling into the local optimal solution while ensuring the search accuracy, and improves the efficiency of finding the global optimal feed speed.

[0019] Preferably, the method for setting the reference data segment of the feed speed at each moment is: The feed rate immediately before each moment The feed speed at each moment constitutes the reference data segment of the feed speed at that moment. is the default value.

[0020] Preferably, a method for optimizing the machining process parameters of a CNC machine tool according to the optimal solution of the feed speed at each moment is: A fixed time interval is set. During the processing, every time a time interval passes, the optimal processing parameters of the feed speed at the last moment of the time interval are obtained, and the feed speed of the CNC machine tool is set according to the optimal processing parameters.

[0021] Preferably, another method for optimizing the machining process parameters of a CNC machine tool according to the optimal solution of the feed speed at each moment is: The progress of multiple processing tasks is set. During the processing, each time a progress is reached, the optimal processing parameters of the feed speed at the corresponding moment of the progress are obtained, and the feed speed of the CNC machine tool is set according to the optimal processing parameters.

[0022] The present invention also provides an optimization system based on intelligent CNC machine tool processing parameters. The process parameter optimization system includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the optimization method.

[0023] The present invention has the following effects: In the process of iteratively searching for the optimal solution of each feed speed by the simulated annealing algorithm, the present invention first dynamically adjusts the initial disturbance range of the simulated annealing algorithm, and then constructs an objective function to further evaluate the degree of preference of the current solution and the new solution generated in each round of the iteration process, so as to determine the disturbance range of the current solution. This series of operations enables the simulated annealing algorithm to better adapt to different processing task scenarios, avoid excessive search and accelerate convergence in simple tasks, enhance the search capability in complex tasks and do not miss potential optimal solutions, effectively improve the optimization effect of CNC machine tool processing parameters, improve processing accuracy and overall processing quality, and enhance the adaptability and stability of CNC machine tools in various processing processes. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein: Figure 1 It is a schematic flow chart of the method of the present invention. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0026] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0027] Reference Figure 1 The optimization method of machining process parameters based on intelligent CNC machine tools provided by the present invention comprises steps S1 to S4: S1: Collect the feed speed of CNC machine tools.

[0028] In one embodiment, a high-precision speed sensor is used to collect data on the feed speed of a CNC machine tool at a frequency of 10 times per second to ensure that rich and representative feed speed data is obtained, providing a data basis for subsequent process parameter optimization.

[0029] S2: Setting a reference data segment of the feed speed at each moment, and adjusting the initial disturbance range of the feed speed using the fluctuation degree of the reference data segment.

[0030] The simulated annealing algorithm is derived from the simulation of the physical annealing process. At high temperatures, particles have higher energy and can move freely in a larger range. As the temperature decreases, the particles gradually stabilize in the lowest energy state. When solving a problem, the algorithm will randomly search in the neighborhood of the current solution and accept new solutions with a certain probability to avoid falling into the local optimal solution.

[0031] In the simulated annealing algorithm, the perturbation range is a core parameter. It clarifies the neighborhood boundaries that can be explored from the current solution, and then determines the size of the neighborhood of the current solution. The neighborhood is the solution space around the current solution, and is the local space for searching the optimal solution. The optimal solution may be located in this neighborhood. The simulated annealing algorithm is based on the current solution and searches for possible new solutions in the neighborhood according to the perturbation range. The larger the perturbation range, the greater the change in the current solution, and the greater the difference between the new solution and the current solution, and vice versa. The new solution may be better or worse than the current solution. The simulated annealing algorithm will accept the new solution with a certain probability, even if the new solution is worse.

[0032] In the current scenario, the traditional simulated annealing algorithm sets a fixed initial perturbation range of the same size for all feed rates. , the unit is , which is an empirical value. This initial perturbation range provides a starting exploration space scale for the algorithm's search. The parameters in the simulated annealing algorithm also include: the initial temperature value is set to 100 degrees Celsius, the temperature threshold is set to 50 degrees Celsius, the cooling coefficient is 0.97, and the initial solution (the current solution in the first round of iteration) when iterating the optimal solution of the feed speed at each moment is set to 500, and the unit is , these values ​​are all empirical values.

[0033] At the beginning of the simulated annealing algorithm, a certain amount of random perturbation will be performed on each feed speed based on this initial perturbation range to generate a new solution. By comparing the advantages and disadvantages of the new solution and the current solution, and accepting the new solution with a certain probability, the algorithm will gradually search to try to find the optimal solution for the feed speed at each moment. If the initial perturbation range is set too large, the algorithm may be too aggressive in the early stage of the search and skip some potential high-quality solution areas; if it is set too small, the algorithm may fall into a long search in a local area, making it difficult to jump out of the local optimal solution and unable to fully explore the entire solution space.

[0034] Therefore, this solution first obtains the fluctuation degree of the reference data segment of the feed speed at each moment (equivalent to the neighborhood of the feed speed), and uses the fluctuation degree to dynamically adjust the initial disturbance range of the simulated annealing algorithm in the process of iteratively searching for the optimal solution of the feed speed at the moment, and obtains the adjusted disturbance range. And further, in the process of iteratively searching for the optimal solution of the feed speed at the acquisition moment, the disturbance range requirement factor of the current solution generated in each round is accurately obtained, and the disturbance range requirement factor of the current solution is used to perform a secondary correction on the adjusted disturbance range to obtain the disturbance range of the current solution generated in each round, so as to optimize the process of iteratively searching for the optimal solution of the feed speed at each moment by the simulated annealing algorithm.

[0035] In one embodiment, the method for setting the reference data segment of the feed speed at each moment is: The feed rate immediately before each moment The feed speed at each moment constitutes the reference data segment of the feed speed at the acquisition moment. This value is an empirical value. If the feed rate before a certain moment is less than 100, the feed rate can be increased by an additional 60 seconds in advance to ensure that there is enough data to construct the reference data segment.

[0036] After obtaining the reference data segment of the feed speed at each moment, it is necessary to analyze the fluctuation degree of the reference data segment. The more frequent and obvious the changes in the values ​​within a reference data segment are, the greater the fluctuation degree is.

[0037] In one embodiment, a method for obtaining the fluctuation degree of the reference data segment of the feed speed at each moment includes: For The reference data segment of the feed speed at the acquisition moment is used to determine the following feed speeds among all the feed speeds contained in the reference data segment: If there is a numerical difference between a certain feed speed and a previous feed speed, the feed speed is marked, and the numerical change of the marked feed speed is obtained.

[0038] For example, if the reference data segment contains all feed rates: 10, 12, 12, 13. It can be seen that the first 12 and 13 have numerical differences with their previous feed speeds. Mark the first 12 and 13. The numerical change of the first 12 is , the value change of 13 is .

[0039] Calculate the degree of fluctuation based on the above judgment results:

[0040] In the formula, For the The fluctuation degree of the reference data segment of the feed speed at each moment, is the number of feed rates within the reference data segment, For the The total number of all marked feed speeds in the reference data segment of the feed speed at the acquisition time, For the The variance of the value changes of all marked feed speeds in the reference data segment of the feed speed at the acquisition moment, is the normalization function.

[0041] In this formula, The larger the The higher the proportion of the marked feed speed in the reference data segment of the feed speed at a certain moment is, the more frequent the value changes in the reference data segment are, which means that the fluctuation degree of the reference data segment will be greater.

[0042] In this formula, The larger the The more obvious the value change of the feed speed at each moment in the reference data segment is, the more frequent the value change in the reference data segment is, the greater the credibility is, and the greater the fluctuation degree of the reference data segment will be.

[0043] After the fluctuation degree of the reference data segment of the feed speed at each moment is obtained, it is necessary to adaptively adjust the initial disturbance range of the feed speed at that moment according to the fluctuation degree.

[0044] In one embodiment, the disturbance range of the feed speed at each moment is adaptively adjusted according to the following formula:

[0045] In the formula, For the The disturbance range after the feed speed adjustment at the moment is The initial disturbance range of the feed speed at each moment is used to make a preliminary adaptive result. is the initial disturbance range uniformly set by the simulated annealing algorithm for the feed rate at all times, Indicates The fluctuation degree of the reference data segment of the feed speed at a certain moment.

[0046] If the fluctuation degree of the reference data segment of the feed speed at a certain moment is greater, it means that in the process of using the simulated annealing algorithm to obtain the optimal solution of the feed speed at that moment, the feed speed contained in the reference data segment of the feed speed is more discrete. In order to maintain the flexibility of the search under large data fluctuations, avoid falling into local extreme values ​​and find the global optimal solution more easily, it is necessary to set the disturbance range of the feed speed at that moment to be larger. Therefore, the formula is designed. The greater the fluctuation of the reference data segment of the feed speed at the moment, The larger the disturbance range after the feed speed adjustment at each moment.

[0047] S3: When iteratively searching for the optimal solution of each feed speed, further correct the disturbance range of the adjusted feed speed to optimize the process of iteratively searching for the optimal solution of the feed speed at each moment by the simulated annealing algorithm.

[0048] Although the previous steps have obtained the preliminary adaptive results of the initial disturbance range of the feed speed at each moment, that is, the disturbance range after the feed speed is adjusted. However, since the simulated annealing algorithm is an iterative search process to obtain the optimal solution of the feed speed at each moment, in each round of iteration, the disturbance range determines the range size of the new solution generated from the current solution (the size of the space for searching new solutions). If the disturbance range after the feed speed adjustment is used as the standard during the iterative search process, it is impossible to dynamically adapt according to the actual situation in the iterative process, and it is very likely that the disturbance range will be too large or too small, affecting the search efficiency of the optimal solution. In other words, the disturbance range after the feed speed adjustment initially obtained at each moment cannot accurately match the optimal disturbance range required for each round of iteration.

[0049] Based on the principle of simulated annealing algorithm, when iteratively searching for the optimal solution of the feed speed at each moment, each round of iteration will generate a current solution. Based on the current solution, a new solution will be generated by perturbing it according to the perturbation range. In this process, the optimal perturbation range of the current solution generated by each round of iteration is not fixed, but is usually closely related to two factors: One is the difference in the objective function values ​​corresponding to the current solution and the new solution generated by the previous iteration. Because the simulated annealing algorithm simulates the physical annealing process, it gradually searches for the optimal solution through iteration. The difference in the objective function values ​​between the current solution and the new solution generated by each iteration reflects whether the current search direction is close to the optimal solution and the degree of closeness. If the difference is large, it means that the current perturbation range may make the search span larger. In subsequent iterations, it may be necessary to adjust the perturbation range appropriately according to the situation to improve the search efficiency. On the contrary, if the difference is small, it means that the search may have approached a better area. At this time, it is necessary to fine-tune the perturbation range to avoid missing the optimal solution.

[0050] The second is the temperature value corresponding to each round of iteration. The temperature value in the simulated annealing algorithm is a key control parameter, which determines the probability of the algorithm accepting a worse solution. As the iteration proceeds, the temperature gradually decreases, and the algorithm tends to accept better solutions more and more. In the high temperature stage, a larger perturbation range is allowed to explore in a wider solution space; in the low temperature stage, in order to converge to the optimal solution, the perturbation range should be reduced accordingly. Therefore, the temperature value directly affects the optimal perturbation range of the current solution in each round of iteration.

[0051] Therefore, in this step, when iteratively searching for the optimal solution of each feed speed, the difference between the objective function value of the new solution generated in each round and the objective function value of the current solution generated in each round, as well as the temperature value after each round of iteration, is deeply analyzed to determine the disturbance range requirement factor of the current solution generated in each round to reflect the adjustment requirement size of the current solution, and then the disturbance range requirement factor of the current solution is used to further correct the disturbance range after the feed speed adjustment, so as to obtain the disturbance range of the current solution (applicable) generated in each round, so as to optimize the process of iteratively searching for the optimal solution of the feed speed at each moment by the simulated annealing algorithm.

[0052] In one embodiment, during the iterative search process, an objective function is constructed to evaluate the preference of the current solution generated in each round and the new solution obtained by perturbing the current solution.

[0053] The constructed objective function is:

[0054] In this formula, is the objective function value of the current solution / new solution, and the objective function value is negatively correlated with the degree of optimization.

[0055] In this formula, is the energy consumption per unit time corresponding to the current solution / new solution, specifically: a certain feed rate (either the feed rate corresponding to the current solution or the feed rate corresponding to the new solution obtained by perturbing the current solution) is used to process the energy consumption per unit time corresponding to the current solution / new solution. The energy consumed in one minute when processing a workpiece with the same cutting area at the feed speed at the collection moment can be accurately obtained from the rich historical data with the help of big data technology. The larger the value of The larger it is, the more difficult it is for the feed speed to become the ideal optimal solution in the process of screening the optimal solution, that is, the lower the degree of preference.

[0056] In this formula, is the unit time capacity corresponding to the current solution / new solution, specifically: a certain feed rate (either the feed rate corresponding to the current solution or the feed rate corresponding to the new solution obtained by perturbing the current solution), in processing The length of the workpiece that can be processed in one minute when the workpiece has the same cutting area as the feed speed at the acquisition moment. This value can also be extracted from historical data using big data technology. The larger the value of is, the smaller the objective function value is. This means that in the process of iterative search for the optimal solution, The larger the feed speed value, the higher its priority in the screening process and the more likely it is to become the optimal solution sought in the end.

[0057] In one embodiment, when iteratively searching for the optimal solution of each feed speed, the method for obtaining the disturbance range requirement factor of the current solution generated in each round is: In the iterative search In the process of finding the optimal solution of the feed speed at the acquisition time, if the The greater the difference between the objective function value of the current solution and the objective function value of the new solution generated by the round iteration, the greater the difference between the objective function value of the current solution and the new solution. The higher the preferred degree of the new solution generated by the round iteration, the better the The new solution obtained by the iteration is closer to the optimal solution. At this time, there is no need to search extensively, so the subsequent search needs to be continued in a relatively small range. The more times the number of iterations is, the smaller the perturbation range requirement factor of the new solution is. The difference between the objective function value of the current solution and the objective function value of the new solution generated by the round iteration is small, indicating that If the new solution generated by the iteration is not obviously better than the current solution, then the search needs to be continued in a relatively large range. The more rounds of iteration, the greater the perturbation range requirement factor of the new solution.

[0058] Calculate according to the following relationship:

[0059] In the formula, It uses simulated annealing algorithm to iteratively search for In the process of finding the optimal solution for the feed speed at the moment, The perturbation range requirement factor of the current solution generated by the round iteration, is the normalization function, and Respectively The objective function values ​​of the current solution and the new solution generated by the round iteration, To use the simulated annealing algorithm in the iterative search In the process of finding the optimal solution for the feed speed at the moment, The temperature value after the round iteration, To use the simulated annealing algorithm in the iterative search The temperature threshold is set during the process of finding the optimal solution for the feed speed at each moment.

[0060] In the formula, in the iterative search In the process of finding the optimal solution of the feed speed at the acquisition time, The current solution generated by the round iteration and new solution The difference in the objective function values ​​of each is greater. The larger the value, the greater the During the round iteration, the greater the degree of preference of the new solution obtained after random perturbation, the better the In the iteration of the round, the new solution obtained after random perturbation is closer to the optimal solution. In order to output the optimal solution more accurately, During the round iteration, The smaller perturbation range requirement factor for the current solution generated by the round iteration.

[0061] Specifically: The objective function value and The greater the difference in the objective function value, the greater the difference in the objective function value. There are two cases: The first one is Very big, Very small, that is The objective function value of the current solution generated by the round iteration is large, and the objective function value of the new solution is small. Since the objective function value and the degree of optimization are negatively correlated, it means that in the first During the round iteration, the current solution has a low degree of preference, and the new solution obtained after the perturbation operation on the current solution has a high degree of preference. This means that the closer the new solution is to the optimal solution, the better the optimization is in the first round. When the round is iterated, the smaller the perturbation range is, the smaller the perturbation range is. The more iterations are generated, the smaller the perturbation range requirement factor of the current solution will be.

[0062] at this time is a large positive number, in the formula Build and negative correlation, and The larger the value, the The objective function value and The greater the difference in the objective function values, the The perturbation range requirement factor of the current solution generated by the round iteration It will get smaller.

[0063] The second is Very small, Very large, that is The objective function value of the current solution generated by the round iteration is small, and the objective function value of the new solution is large. Since the objective function value and the optimization degree are negatively correlated, it means that in the first The current solution generated by the round iteration has a high degree of preference, and the new solution obtained after the perturbation operation on the current solution has a low degree of preference, which means that the further the new solution is from the optimal solution, the better the When the round is iterated, a larger disturbance range should be set, that is, The more rounds of iteration, the larger the perturbation range requirement factor of the current solution.

[0064] at this time is a very small negative number, in the formula Constructed and The positive correlation is, and the smaller the negative number is, the The objective function value and The greater the difference in the objective function value, the greater the The larger the value of The perturbation range requirement factor of the current solution generated by the round iteration The bigger it is.

[0065] In this formula, Indicates The temperature value after the round of iteration is relative to the temperature threshold. The smaller the value is, the As the iterations get closer to the last iteration, the simulated annealing algorithm will tend to perform a more detailed local search around the current solution, reducing the amplitude of the global search, and thus gradually converge to an optimal solution. The required factor of the perturbation range of the current solution generated by the round iteration will be smaller.

[0066] In summary, the above scheme enables the simulated annealing algorithm to dynamically adjust the perturbation range of the new solution generated in the current round according to the iteration results of the previous round and the temperature state of the current round, so as to improve the accuracy and efficiency of the algorithm's iterative search for the optimal solution and avoid falling into the problem of local optimality or inappropriate search range.

[0067] In one embodiment, the perturbation range of the current solution generated in each round is determined based on the following method: In the process of obtaining the optimal solution of the feed speed at each moment, the smaller the disturbance range requirement factor of the new solution generated by a certain round of iteration, the smaller the fluctuation amplitude of the reference data segment of the feed speed at that moment, which can also reflect that the new solution generated by this round of iteration is more preferred and the temperature value corresponding to this round of iteration is lower. Therefore, a smaller disturbance range should be set for the new solution generated by this round of iteration to ensure more accurate output of the optimal solution.

[0068] According to the above logic, the calculation formula for the perturbation range of the new solution generated in each round of iteration is:

[0069] In the formula, Indicates that in the iterative search In the process of finding the optimal solution for the feed speed at the moment, The perturbation range of the current solution generated by the round iteration, For the The disturbance range after feed speed adjustment at each acquisition moment, It uses simulated annealing algorithm to iteratively search for In the process of finding the optimal solution of the feed speed at the acquisition time, The perturbation range requirement factor for the current solution produced by the round iteration.

[0070] In this formula, since the disturbance range requirement factor is determined based on factors such as the difference in the objective function value between the current solution and the new solution, the disturbance range requirement factor can reflect the adjustment requirement of the current solution. In this way, in different processing scenarios and iteration rounds, the algorithm can flexibly adjust the disturbance range according to actual conditions. When the processing process is more complex and there may be a large deviation between the current solution and the optimal solution, a larger disturbance range requirement factor will prompt the disturbance range to increase appropriately, so that the algorithm has a larger search space to find a better solution, ensuring that the algorithm will not fall into the local optimum; when the processing is relatively stable and the current solution is close to the optimal solution, a smaller disturbance range requirement factor will reduce the disturbance range, so that the algorithm can perform a more detailed search when it is close to the optimal solution, accelerate the convergence speed of the algorithm, improve the efficiency of finding the global optimal solution, and enhance the dynamic adaptability of the simulated annealing algorithm to different processing task scenarios.

[0071] S4: The optimal solution of the feed speed at each moment is obtained according to the optimized iterative search process, and the machining process parameters of the CNC machine tool are optimized according to the optimal solution of the feed speed at each moment.

[0072] Through step S2-step S3, the process of iteratively searching the feed speed at each moment by the simulated annealing algorithm is optimized. When the iterative search is completed, the optimal processing parameters at each moment are obtained.

[0073] In one embodiment, the method for optimizing the machining process parameters of a CNC machine tool is as follows: According to the characteristics of the processing technology and the performance of the machine tool, a fixed time interval is set, and the empirical value is 5 minutes. During the processing, every 5 minutes, the optimal solution of the feed speed at the last moment of the 5 minutes is obtained according to step S2-step S3, and the feed speed of the CNC machine tool is set according to the optimal solution to optimize the processing technology parameters of the CNC machine tool.

[0074] In one embodiment, the method for optimizing the machining process parameters of a CNC machine tool is as follows: Determine the adjustment interval according to the progress of the processing task. Suppose the entire processing task is to process a length of For workpieces, you can set the time for each tenth of the workpiece to be completed. The processing length reaches a progress, and the optimal processing parameters corresponding to the time when the progress is reached are obtained according to steps S2-S3, and the feed speed of the CNC machine tool is set according to the optimal processing parameters to optimize the processing parameters of the CNC machine tool.

[0075] By providing the most suitable feed speed for the machine tool at different processing stages, it is ensured that the machine tool can maintain the best operating state throughout the entire processing process, avoiding too frequent adjustment of processing parameters. At the same time, according to different situations in the processing process, the optimal feed speed can be provided to the CNC machine tool in a timely manner to ensure the optimization of processing effects and machine tool performance.

[0076] In addition, the present invention also provides an optimization system based on intelligent CNC machine tool processing parameters, the intelligent teaching system includes a memory and a processor, the memory stores a computer program, and the processor executes the computer program to implement the steps of the optimization method.

[0077] In the description of this specification, "multiple" or "several" means at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0078] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

Claims

1. An optimization method based on processing parameters of intelligent CNC machine tools, characterized in that: include: Collecting the feed speed of the CNC machine tool at each moment, setting the initial disturbance range of the simulated annealing algorithm in the process of iteratively searching for the optimal solution of the feed speed at each moment, and the reference data segment of the feed speed at each moment, analyzing the fluctuation degree of the reference data segment, dynamically adjusting the initial disturbance range using the fluctuation degree, and obtaining the adjusted disturbance range; In the process of iterative search, the preferred degree of the current solution generated in each round and the new solution obtained by perturbing the current solution are evaluated, and the disturbance range requirement factor of the current solution is determined based on the difference in preferred degree between the current solution and the new solution. The disturbance range requirement factor is used to reflect the size of the adjustment requirement of the current solution; The disturbance range requirement factor of the current solution generated in each round is used to correct the adjusted disturbance range, and the disturbance range of the current solution generated in each round is obtained, so as to optimize the process of iterative search for the optimal solution of the feed speed at each moment by the simulated annealing algorithm, obtain the optimal solution of the feed speed at each moment, and optimize the machining process parameters of the CNC machine tool according to the optimal solution of the feed speed at each moment.

2. The optimization method based on processing parameters of intelligent CNC machine tools according to claim 1 is characterized in that: The perturbation range of the current solution generated in each round is determined based on the following method: The disturbance range adjusted by the simulated annealing algorithm in the process of iteratively searching for the optimal solution of the feed speed at each moment is multiplied by the disturbance range requirement factor of the current solution generated in each round to realize the correction of the adjusted disturbance range, and the multiplied value is used as the disturbance range of the current solution generated in each round.

3. The optimization method based on processing parameters of intelligent CNC machine tools according to claim 1 is characterized in that: The method for evaluating the optimality of the current solution generated in each round and the new solution obtained by perturbing the current solution is: Construct an objective function to reflect the preference of the current solution / new solution: ； in, is the objective function value of the current solution / new solution. Both the current solution / new solution are feed speed, and the objective function value is negatively correlated with the optimization degree. is the energy consumption per unit time corresponding to the current solution / new solution, is the unit time capacity corresponding to the current solution / new solution.

4. The optimization method based on processing parameters of intelligent CNC machine tools according to claim 3 is characterized in that: The disturbance range requirement factor of the current solution satisfies the following relationship: ； In the formula, It is an iterative search In the process of finding the optimal solution for the feed speed at the moment, The perturbation range requirement factor of the current solution generated by the round iteration, is the normalization function, and Respectively The objective function values ​​of the current solution and the new solution generated by the round iteration, To iterate the search In the process of finding the optimal solution for the feed speed at the moment, The temperature value after the round iteration, Iterate to search for The temperature threshold is set during the process of finding the optimal solution for the feed speed at each acquisition moment.

5. The optimization method based on processing parameters of intelligent CNC machine tools according to claim 1 is characterized in that: The degree of fluctuation of the reference data segment is determined in the following way: Among all the feed speeds included in the reference data segment of the feed speed at each moment, if a certain feed speed has a numerical difference with a previous feed speed, the feed speed is marked, and the numerical change of the marked feed speed is obtained; Calculate the volatility: ; In the formula, For the The fluctuation degree of the reference data segment of the feed speed at each moment, is the normalization function, is the number of feed rates within the reference data segment, For the The total number of all marked feed speeds in the reference data segment of the feed speed at the moment, For the The variance of the numerical changes of all marked feed speeds in the reference data segment of the feed speed at a moment.

6. The optimization method based on processing parameters of intelligent CNC machine tools according to claim 5 is characterized in that: The method of dynamically adjusting the initial disturbance range by using the fluctuation degree to obtain the adjusted disturbance range is: The initial disturbance range in the process of iteratively searching for the optimal solution of the feed speed at each moment by the simulated annealing algorithm is multiplied by the fluctuation degree of the reference data segment of the feed speed at that moment to achieve dynamic adjustment of the initial disturbance range, and the multiplied value is used as the adjusted disturbance range.

7. The optimization method based on processing parameters of intelligent CNC machine tools according to claim 6 is characterized in that: The method for setting the reference data segment of the feed speed at each moment is: The feed rate immediately before each moment The feed speed at each moment constitutes the reference data segment of the feed speed at that moment. is the default value.

8. The optimization method based on processing parameters of intelligent CNC machine tools according to claim 1 is characterized in that: One method to optimize the machining process parameters of CNC machine tools according to the optimal solution of feed speed at each moment is: A fixed time interval is set. During the processing, every time a time interval passes, the optimal processing parameters of the feed speed at the last moment of the time interval are obtained, and the feed speed of the CNC machine tool is set according to the optimal processing parameters.

9. The optimization method based on processing parameters of intelligent CNC machine tools according to claim 1 is characterized in that: Another method to optimize the machining process parameters of CNC machine tools according to the optimal solution of feed speed at each moment is: The progress of multiple processing tasks is set. During the processing, each time a progress is reached, the optimal processing parameters of the feed speed at the corresponding moment of the progress are obtained, and the feed speed of the CNC machine tool is set according to the optimal processing parameters.

10. An optimization system based on intelligent CNC machine tool processing parameters, characterized in that: The process parameter optimization system comprises a memory and a processor, wherein a computer program is stored in the memory, and the processor executes the computer program to implement the steps of the optimization method according to any one of claims 1 to 9.

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