Control method, device and computer equipment for aspheric surface machining machine tool

Abstract

The present invention provides a control method, device and computer equipment for a non-spherical surface machining machine tool, aiming to solve the problem of insufficient adaptive adjustment and precise control in the non-spherical surface machining process in the prior art. The method automatically obtains the surface data of the non-spherical surface component, generates and updates the tool path data in real time, and ensures adaptive adjustment and path optimization of the machining process. Through multiple iterative measurements and path updates, the machining accuracy is improved and manual intervention is reduced. The application of intelligent control algorithms ensures the stability and accuracy of the tool path, thereby improving machining efficiency and product quality.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a control method, device and computer equipment for a non-spherical machining machine tool. Background Art

[0002] In modern manufacturing, precise control of aspheric surface processing machine tools has become the key to technological development. Aspheric lenses are widely used in optical and electronic equipment, and their manufacturing accuracy directly affects the performance and quality of the equipment. However, aspheric surface processing has complex curvature changes, and traditional processing methods and machine tool control technologies are difficult to meet the needs of high precision and high efficiency. Current control methods mostly rely on traditional tool path planning technologies, which often have problems with insufficient path optimization when processing complex aspheric surfaces, resulting in processing accuracy that cannot meet expected standards. At the same time, traditional technologies often find it difficult to adjust the tool path in a timely manner when dealing with dynamically changing aspheric curvatures, affecting the overall processing quality.

[0003] With the increasing demand for aspheric lenses, traditional machine tool control technology has become increasingly incompatible with market demand. When processing aspheric surfaces, traditional machine tool control systems often have difficulty adapting to the dynamic changes of complex surfaces due to their fixed control algorithms and parameter settings. This leads to low efficiency and unstable processing quality during the processing, especially when the aspheric curvature is large or the surface quality requirements are high. Therefore, the existing technology has obvious deficiencies in achieving adaptive adjustment and precise control during aspheric processing, and more advanced and intelligent control methods are urgently needed to solve these problems. Summary of the invention

[0004] The main purpose of the present invention is to provide a control method, device and computer equipment for aspheric machining machine tools to solve the technical problem that the prior art has obvious deficiencies in achieving adaptive adjustment and precise control during aspheric machining, and urgently needs a more advanced and intelligent control method.

[0005] To achieve the above-mentioned purpose, the present invention provides a control method for a non-spherical machining machine tool, comprising the following steps: acquiring the surface data of a non-spherical component to be processed, and generating tool path data of the non-spherical component to be processed based on the surface data; transmitting the tool path data to the non-spherical machining machine tool, and controlling the non-spherical machining machine tool to perform machining on the non-spherical component to be processed based on the tool path data; acquiring the surface data of the processed non-spherical component again, and generating the tool path data of the non-spherical component to be processed again based on the newly acquired surface data; transmitting the newly generated tool path data to the non-spherical machining machine tool again, and continuing to execute the step of "controlling the non-spherical machining machine tool to perform machining on the non-spherical component to be processed based on the tool path data" until all the non-spherical components to be processed are processed.

[0006] The present invention also provides a control device for a non-spherical machining machine tool, comprising: an acquisition unit, used to acquire the surface data of the non-spherical component to be processed, and generate tool path data of the non-spherical component to be processed based on the surface data; a processing unit, used to transmit the tool path data to the non-spherical machining machine tool, and control the non-spherical machining machine tool to process the non-spherical component to be processed based on the tool path data; a generation unit, used to acquire the surface data of the processed non-spherical component again, and generate the tool path data of the non-spherical component to be processed again based on the newly acquired surface data; an execution unit, used to transmit the newly generated tool path data to the non-spherical machining machine tool again, and continue to execute the step of "controlling the non-spherical machining machine tool to process the non-spherical component to be processed based on the tool path data" until all the non-spherical components to be processed are processed.

[0007] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0008] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned methods are implemented.

[0009] The control method, device and computer equipment of the non-spherical surface machining machine provided by the present invention automatically acquire and analyze the surface data of the non-spherical surface component, generate and update the tool path data in real time, and realize adaptive adjustment during the machining process. Through multiple iterative measurements and path optimization, the present invention effectively solves the technical problems of low machining accuracy, frequent manual adjustments and difficulty in real-time control in the prior art. In addition, the use of intelligent control algorithms ensures the accuracy and stability of the tool path during complex non-spherical surface machining, significantly improving machining efficiency and product quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 is a schematic diagram of the steps of a control method for an aspheric surface machining machine tool in one embodiment of the present invention;

[0011] Figure 2 is a structural block diagram of a control device for an aspheric surface machining machine tool in one embodiment of the present invention;

[0012] Figure 3 It is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0013] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0014] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0015] Reference Figure 1 , this embodiment provides a control method for an aspheric surface machining machine tool, comprising the following steps:

[0016] S1, acquiring surface data of a non-spherical component to be processed, and generating tool path data of the non-spherical component to be processed according to the surface data.

[0017] S2, transmitting the tool path data to the aspheric surface machining machine tool, and controlling the aspheric surface machining machine tool to machine the aspheric surface component to be processed based on the tool path data.

[0018] S3, acquiring the surface shape data of the processed aspheric component again, and generating the tool path data of the aspheric component to be processed again according to the newly acquired surface shape data.

[0019] S4, again transmitting the newly generated tool path data to the aspheric surface machining machine tool, and continuing to execute the step of "controlling the aspheric surface machining machine tool to perform machining on the aspheric surface component to be processed based on the tool path data" until all the aspheric surface components to be processed are processed.

[0020] That is, this embodiment aims to solve the technical problem of tool path planning and real-time adjustment in the process of complex aspheric surface processing. The core idea of ​​the solution is to gradually optimize the tool path by acquiring and analyzing the surface data of the aspheric component, and to perform repeated adaptive adjustments during the processing to ensure that the processing accuracy and surface quality meet the expected requirements.

[0021] First of all, the first step of this control method is to obtain the surface data of the aspheric component to be processed. Surface data is important information that describes the geometric shape of the aspheric component. It includes detailed data such as the coordinates of the points on the surface and the curvature change. The acquisition of these data usually requires high-precision measuring equipment, such as a three-coordinate measuring machine or a laser scanner, to ensure the accuracy of the data. After obtaining the surface data, the tool path will be generated based on this data. Tool path planning is one of the key steps in aspheric processing, because the curvature changes of the aspheric surface are complex, and traditional fixed path planning methods are often difficult to cope with such dynamic changes. In this scheme, the generation of the tool path is adaptively performed based on the surface data, which means that it can dynamically adjust the path to match the curvature changes of the aspheric surface, thereby improving the accuracy of the processing.

[0022] Next, the tool path data is transmitted to the aspheric surface machining machine, which means that the control system will drive the machine to perform precise machining operations according to the planned path. The operation of the aspheric surface machining machine is generally controlled by a CNC system. By transmitting the path data, the machine can move the tool according to the preset path and perform layer-by-layer cutting. During this process, the shape of the aspheric component will gradually approach the design target. However, in traditional machining methods, this is often a static process that lacks the ability to dynamically adjust. Assuming that the curvature of certain areas is particularly large, or tool wear occurs during the machining process, it is difficult for traditional methods to adjust the path in time, which will lead to a decrease in machining accuracy.

[0023] To solve this problem, this solution obtains the processed surface data again after completing the initial processing. This step is a key link in adaptive control. By measuring again, the control system can evaluate the gap between the current processing result and the ideal state. For example, if the surface accuracy of certain areas does not meet the requirements, the system can detect the error and then regenerate the tool path through the newly acquired surface data. At this time, the adjustment of the tool path is not only based on the original design data, but also takes into account the actual situation after processing, which means that the control system can dynamically optimize the path based on real-time feedback.

[0024] Once the new tool path is generated, the data is transferred back to the machine tool for the next round of processing. This adaptive feedback control method is extremely flexible and can ensure that the machine tool can maintain high processing accuracy even in complex curvature changes. For example, in the processing of aspherical lenses, certain lens areas may require extremely high surface accuracy, which is usually required to be within a few nanometers. By repeatedly measuring and adjusting the path, this solution can meet these stringent requirements.

[0025] This control method is not only applicable to high-precision processing scenarios, but also can improve processing efficiency. In traditional methods, since the path cannot be adjusted in real time, high-precision processing is often achieved through multiple trial cuts and manual corrections, which is not only time-consuming and labor-intensive, but also increases processing costs. This solution greatly reduces the need for manual intervention through automated feedback adjustment. At the same time, the system can immediately adjust the path when an error is detected, avoiding the number of repeated processing and further improving efficiency.

[0026] In addition, this technical solution is not limited to the processing of a single aspheric component, but can also be extended to the processing of aspheric surfaces of different shapes and sizes. Since the control method is essentially based on real-time feedback and adaptive adjustment, whether it is processing a lens with a large curvature change or a tiny and complex aspheric structure, the system can maintain efficient and accurate processing through dynamic acquisition of surface data and continuous optimization of the path.

[0027] In general, the technical solution of the present invention constructs an adaptive and intelligent processing control system by acquiring surface data, generating tool paths, repeatedly iterating measurements and optimizing paths. It solves the problems of unstable processing accuracy and low efficiency caused by fixed path planning in traditional processing methods, and ensures high efficiency and accuracy of processing by dynamically adjusting the path. In the field of aspheric processing, especially in high-precision manufacturing fields such as optical lenses and aerospace components, this method has broad application prospects.

[0028] In one example, controlling the aspheric machining machine tool to perform machining on the aspheric component to be processed based on the tool path data includes: real-time monitoring parameter information of the aspheric machining machine tool for machining the aspheric component, wherein the parameter information includes at least tool position data and tool speed data; and adjusting the tool path data in real time according to the parameter information, and using the tool path data adjusted in real time to process the aspheric component to be processed.

[0029] In this example, when controlling the aspheric surface machining machine tool for machining based on the tool path data, the core lies in the real-time monitoring and dynamic adjustment of the machining process. First, the system monitors the key parameter information of the machining machine tool in real time, including the position data and speed data of the tool. This information is used to track the actual motion state of the tool to ensure that the tool cuts according to the predetermined path and speed. However, due to errors or deviations that may occur during the machining process, such as slight offsets in the tool position or unstable speed, the system will adjust the tool path data in real time based on the monitored parameter information. This means that the tool path will be optimized at any time according to the actual situation to ensure that the machining accuracy and surface quality are always in the best state. The adjusted path data is used immediately for machining to ensure that the machining process is adaptive and efficient, thereby ensuring that the machining effect of the aspheric components meets the design requirements.

[0030] In one example, before the tool path data is adjusted in real time according to the parameter information, the control method also includes: identifying and processing the parameter information to divide it into multiple data sets, wherein different data sets correspond to different data types; performing time domain analysis on the multiple data sets, dividing the multiple data sets into multiple frequency ranges, and identifying the noise characteristics of each frequency range; calculating the filtering parameters of the bandpass filter based on the noise characteristics of each frequency range, and using the filtering parameters to perform time domain denoising on the parameter information to obtain filtered parameter information; using the z-score method to analyze the filtered parameter information, identify abnormal data points that deviate from a preset range, and smooth the abnormal data points to obtain smoothed parameter information; scaling the smoothed parameter information to within a preset range, and formatting the scaled parameter information to obtain preprocessed parameter information.

[0031] That is, through the steps of identifying and processing parameter information and dividing it into multiple data sets, the parameter information obtained from different sources is effectively organized so that each data can be processed independently according to its source. This classification processing method greatly reduces the interference between different data sources and helps to improve the accuracy of data processing. By analyzing multiple data sets in the time domain, dividing them into multiple frequency ranges and identifying noise characteristics, the noise components that have a greater impact on processing can be effectively separated. For example, when processing high-precision aspherical lenses, some high-frequency noise may come from the microscopic vibration between the tool and the material, while low-frequency noise may come from the overall vibration of the machine tool. By identifying and processing these noises separately, their negative impact on processing accuracy can be significantly reduced.

[0032] Based on the noise characteristics of each frequency range identified, the filter parameters of the bandpass filter are calculated and time domain denoising is performed. This step ensures that the system can retain useful data information to the maximum extent while removing unnecessary noise interference. The denoising data will be purer, which will help with subsequent processing path optimization. Taking high-precision aspheric processing as an example, by removing the noise interference caused by tool vibration, the system can more accurately perceive the position and cutting force of the tool, thereby making more accurate judgments when adjusting the tool path.

[0033] Next, the z-score method is used to analyze the filtered parameter information to identify abnormal data points that deviate from the preset range. Abnormal data points may be caused by occasional mechanical failures or sudden changes in the external environment during the processing process. If not handled, they may have an adverse effect on the optimization of the overall processing path. By smoothing these abnormal data points, the system can effectively eliminate the influence of these accidental factors, thereby ensuring the stability of the processing path. For example, in the manufacturing process of aspherical lenses, if the cutting force suddenly increases due to changes in material hardness at a certain moment, the system can identify this anomaly and adjust the data through smoothing to avoid misleading the overall processing path.

[0034] Finally, the smoothed parameter information is scaled to the preset range and formatted, so that the scales of different data are consistent, further improving the availability and consistency of the data. This step is especially important for the processing of complex aspheric surfaces, because different processing stages may involve different parameter magnitudes. Through scaling, the system can better coordinate the data to ensure that the final tool path optimization results can fully reflect the true situation of each parameter. For example, in the manufacturing process of some high-precision optical lenses, temperature changes have a greater impact on material properties. By unifying the temperature data with the cutting force data, the system can more accurately adjust the tool path to ensure the accuracy and stability of the processing.

[0035] In summary, these newly added steps significantly enhance the system's ability to process parameter information. By separating and removing noise, smoothing outliers, and formatting and scaling data, the data ultimately used for tool path optimization is ensured to be more reliable and accurate, thereby improving the overall quality and efficiency of aspheric machining.

[0036] In one example, tool path data of the non-spherical component to be processed is generated according to the surface shape data, including: constructing a digital model of the non-spherical component based on the surface shape data of the non-spherical component; using the aspherical machining machine tool data optimization algorithm to perform data processing on the digital model of the non-spherical component to obtain the tool path data of the non-spherical component to be processed.

[0037] In this example, the process of generating tool path data for the non-spherical component to be processed is based on the surface data of the component. First of all, surface data refers to the shape information of the component surface, including its geometric features, curvature changes, etc. By obtaining these surface data, the system can build a digital model of the non-spherical component. This model accurately reflects the three-dimensional shape of the component and becomes the basis for subsequent tool path planning. The construction of the digital model is equivalent to virtualizing the actual non-spherical component, providing a reference basis for the processing process, so that the subsequent tool path can more accurately match the shape of the component.

[0038] Next, the digital model will be input into the aspheric machining machine data optimization algorithm for processing. The function of this algorithm is to intelligently optimize the tool path according to the complex geometric features of the component. The shape of aspheric components usually has complex curvature changes. If the traditional tool path planning method is used, it will often lead to the cutting path being inaccurate or not smooth enough, affecting the processing accuracy and efficiency. Through the data optimization algorithm, the system can comprehensively consider the geometric shape of the aspheric component and the parameters of the machining machine (such as the cutting ability of the tool, the processing speed, etc.) to optimize the path. This optimization can not only ensure that the tool path is highly matched with the surface shape of the component, but also reduce errors in processing and improve surface quality.

[0039] The optimized tool path data is the specific cutting trajectory generated by the system based on the digital model of the aspheric component and the machine tool capabilities. These path data will directly control the movement of the tool in the processing machine to ensure that the tool processes the component in the best cutting method. In this process, each step of the tool path is accurately calculated, taking into account factors such as the local curvature of the aspheric surface, the position change of the tool, and the feed speed, ultimately ensuring the accuracy and efficiency of the processing results.

[0040] In one example, the tool path data is adjusted in real time according to the parameter information, including: using a real-time surface reconstruction algorithm, dynamically adjusting the digital model of the non-curved component based on the preprocessed parameter information to obtain an updated digital model, wherein the updated digital model reflects the actual surface morphology of the non-spherical component during the machining process; based on the updated digital model, dynamically adjusting the parameters in the aspheric machining machine tool data optimization algorithm, and inputting the preprocessed parameter information into the dynamically adjusted non-spherical machining machine tool data optimization algorithm to obtain adjusted tool path data.

[0041] In this example, the process of adjusting tool path data in real time is designed to ensure high precision and consistency in the machining of aspheric components. The entire process uses advanced real-time surface reconstruction algorithms and dynamic parameter adjustment to accurately reflect the actual shape of the aspheric components during machining and dynamically optimize the tool path, thereby further overcoming the problem of insufficient precision caused by static path planning in traditional machining methods.

[0042] First, the system uses a real-time surface reconstruction algorithm to process the parameter information obtained during the machining process. These parameter information include the position data and speed data of the tool, which reflect the actual motion state and cutting conditions of the tool during the machining process. By preprocessing these data, the system can accurately capture the slight deviations or changes that may occur during the machining process. For example, when machining a complex aspheric lens, the tool may produce a slight offset in position or speed due to wear or changes in material stress. Based on these preprocessed parameter information, the real-time surface reconstruction algorithm dynamically adjusts the digital model of the aspheric component and generates an updated digital model that reflects the current actual machining status. This updated model not only accurately reflects the actual surface morphology of the aspheric component during the machining process, but also provides the latest geometric information for subsequent tool path optimization.

[0043] Next, based on the updated digital model, the system dynamically adjusts the parameters in the data optimization algorithm of the aspheric surface machining machine. Traditional tool path planning usually relies on preset parameters and static models, which makes it difficult to cope with real-time changes in the machining process. In this example, the data optimization algorithm can be adjusted according to the latest digital model to ensure that the tool path is always highly matched with the actual surface morphology. For example, if the curvature of a certain area changes during the machining process, the optimization algorithm will recalculate the cutting path of the tool based on the new surface information to ensure that the machining accuracy and surface quality are not affected. The adjusted tool path data is then applied to the aspheric surface machining machine to guide the tool for further processing. This means that each adjustment of the tool path is based on the latest machining status, thereby achieving adaptive control of the machining process. For example, during the machining process, if a section of the tool path needs to adjust the machining speed due to uneven material hardness, the system can detect this change in real time and dynamically optimize the path and cutting speed to ensure the stability and efficiency of the machining process.

[0044] Through this method of real-time monitoring and dynamic adjustment, the system can continuously optimize the tool path during the processing process, respond to changes in various processing conditions in a timely manner, and significantly improve the processing accuracy and consistency of aspheric components. To give a specific example, suppose that when processing a high-precision aspheric optical lens, it is found that the surface of some areas is slightly deviated after preliminary processing. The system will immediately obtain the latest surface data, update the digital model using the real-time surface reconstruction algorithm, and adjust the tool path through the optimization algorithm, and re-plan the cutting path to correct these deviations. In this way, the entire processing process does not need to rely on multiple manual adjustments or repeated trial cutting, which greatly improves processing efficiency and quality.

[0045] In general, adaptive adjustment and precise control of the aspheric machining process are achieved through real-time data acquisition, dynamic model update and tool path optimization, solving the accuracy and efficiency problems caused by static path planning in traditional methods.

[0046] In one example, based on the updated digital model, the parameters in the aspheric machining machine tool data optimization algorithm are dynamically adjusted, including: performing geometric analysis on the updated digital model to extract geometric features, wherein the geometric features include: curvature radius, surface normal, surface intersection line, surface gradient change value and concave-convex value; using linear discriminant analysis to identify multiple target features that contribute most to data changes in the geometric features, and performing covariance calculation on the multiple target features to obtain a covariance matrix; calculating the eigenvalues ​​and eigenvectors of the covariance matrix, and mapping the eigenvalues ​​and eigenvectors of the covariance matrix to a low-dimensional space to obtain low-dimensional feature data; The low-dimensional feature data form an initial feature matrix, wherein the initial feature matrix is ​​an N×M matrix, M is the number of target features, and N is the sampling times of each target feature; the mean number of each column in the initial feature matrix and the standard deviation of the initial feature matrix are determined, and each eigenvalue in the initial feature matrix is ​​subtracted from the mean number of the column where the eigenvalue is located, and divided by the standard deviation of the initial feature matrix to obtain a final target feature matrix; the target feature matrix is ​​converted into a multi-objective fitness function by a weighted summation method, and the multi-objective fitness function is used to dynamically adjust the inertia weight and local search parameters in the aspheric machining machine tool data optimization algorithm.

[0047] That is, the control method of the aspheric surface machining machine tool performs geometric analysis on the updated digital model and further uses Linear Discriminant Analysis (LDA) to process the geometric features, thereby dynamically adjusting the parameters in the data optimization algorithm. The core of this process is to improve the adaptability and accuracy of the machining process by deeply analyzing and optimizing the geometric features in the digital model.

[0048] First, the updated digital model is geometrically analyzed to extract multiple geometric features, including the radius of curvature, surface normal, surface intersection line, surface gradient change value, and concave and convex values. These geometric features can accurately reflect the complex surface morphology and its changes of the aspheric components. For example, the radius of curvature reflects the degree of curvature of the surface, the surface normal describes the directionality of the surface at different points, and the surface intersection line, gradient change value, and concave and convex value can further characterize the overall morphology of the surface and its local characteristics.

[0049] After extracting these geometric features, linear discriminant analysis (LDA) is then used to identify multiple target features that contribute most to data variation among these geometric features. Linear discriminant analysis is a statistical method for dimensionality reduction and feature selection that can find those features in the feature set that best distinguish different categories or best explain data variation. In this example, LDA identifies multiple key target features that are the basis for subsequent optimization calculations.

[0050] Subsequently, the covariance of these target features is calculated to obtain the covariance matrix. The covariance matrix describes the correlation between different target features, which can help understand how these features jointly affect the overall processing process. By calculating the eigenvalues ​​and eigenvectors of the covariance matrix and mapping these results to a low-dimensional space, low-dimensional feature data is obtained. This step is equivalent to simplifying complex high-dimensional geometric features into more easily processable low-dimensional data, thereby simplifying subsequent calculations.

[0051] These low-dimensional feature data are combined into an initial feature matrix, in which each column represents a target feature and each row represents a different sampling point of the feature. In order to normalize the data, the mean and standard deviation of each column in the initial feature matrix are further calculated, and the initial feature matrix is ​​standardized by subtracting the mean from each eigenvalue and dividing it by the standard deviation to obtain the final target feature matrix. This standardization process helps to eliminate the magnitude differences between different features, thereby improving the stability and accuracy of the algorithm processing.

[0052] Finally, the target feature matrix is ​​converted into a multi-objective fitness function by weighted summation. The multi-objective fitness function is a comprehensive indicator that can quantify the overall performance of multiple target features in the optimization process. Through this function, the inertia weight and local search parameters in the aspheric machining machine tool data optimization algorithm are dynamically adjusted. This dynamic adjustment mechanism enables the optimization algorithm to automatically adjust its internal parameters according to the real-time updated geometric features and machining conditions, thereby maintaining efficient and accurate machining performance at different machining stages.

[0053] For example, during the precision machining of an aspherical optical lens, the actual shape of the surface may change slightly due to the heterogeneity of the material or slight changes in the machining environment. These changes will be reflected in the geometric features. For example, a slight adjustment of the radius of curvature may affect the planning of the entire machining path. In traditional methods, these changes are often difficult to respond to in real time. With the above-mentioned extended control method, the system can automatically identify these changes and dynamically adjust the algorithm parameters so that the tool path can always be consistent with the actual surface shape, thereby ensuring high precision and consistency of the machining results.

[0054] In one example, the inertia weight in the aspheric surface machining machine tool data optimization algorithm is dynamically adjusted using the multi-objective fitness function, including: obtaining a first adjustment formula, calculating the first adjustment formula in combination with the multi-objective fitness function, and dynamically adjusting the inertia weight in the aspheric surface machining machine tool data optimization algorithm according to the calculation result;

[0055] Wherein, the first adjustment formula is: , is the inertia weight of the next step, is the maximum value of the inertia weight, is the minimum value of the inertia weight, is the fitness value of the current solution of the multi-objective fitness function, is the maximum value of fitness, is the minimum value of fitness.

[0056] In this example, a multi-objective fitness function is used to dynamically adjust the inertia weight in the aspheric machining machine tool data optimization algorithm, and a specific formula is used to accurately calculate and adjust the inertia weight. The core of this process is to use the results of the fitness function to optimize the inertia weight of the algorithm in real time, thereby improving the efficiency and quality of the machining process.

[0057] First of all, inertia weight is a key parameter in the optimization algorithm, especially in swarm intelligence algorithms such as particle swarm optimization (PSO) algorithm, where inertia weight directly affects the algorithm's search behavior. Inertia weight determines whether the algorithm is more inclined to conduct an extensive search (larger inertia weight) or a detailed search (smaller inertia weight) when searching the solution space. In the control process of aspheric machining machine tools, dynamically adjusting the inertia weight can enable the algorithm to adaptively adjust the search strategy according to real-time data and condition changes during the machining process, thereby improving the accuracy of tool path planning.

[0058] In this example, the dynamic adjustment of the inertia weight is based on a multi-objective fitness function. The multi-objective fitness function is derived by integrating multiple objective features, and it reflects the overall performance of the current solution on multiple optimization objectives. After obtaining the first adjustment formula, the inertia weight is calculated and adjusted using this formula and the result of the fitness function.

[0059] Specifically, the dynamic adjustment of inertia weight follows the following formula:

[0060] The mechanism of this formula can be understood as follows: when the fitness value of the current solution is close to the maximum value of the fitness value, the inertia weight will tend to a larger value, which means that the algorithm tends to search the solution space more extensively to find a possible better solution. However, when the fitness value of the current solution is close to the minimum value of the fitness value, the inertia weight will tend to a smaller value, which means that the algorithm will focus more on fine search to avoid falling into the local optimal solution.

[0061] This dynamic adjustment mechanism ensures that the algorithm adopts the most suitable search strategy at different stages of processing. For example, in the initial stage, aspheric processing may face large geometric changes and uncertainties. At this time, the algorithm will set a larger inertia weight to search in a larger solution space to find the optimal tool path. In the later stage of processing, when the processing path gradually converges and higher precision requirements are required, the inertia weight will gradually decrease, and the algorithm will pay more attention to precise control to ensure the high precision of the final processing results.

[0062] In this way, the data optimization algorithm of the aspheric surface machining machine tool can dynamically adjust the inertia weight according to the changes in the real-time multi-objective fitness function, thereby achieving optimal tool path planning and machining control at different machining stages. Such a control strategy not only improves machining efficiency, but also ensures the quality of the final product.

[0063] In one example, the multi-objective fitness function is used to dynamically adjust the local search parameters in the aspheric machining machine tool data optimization algorithm, including: obtaining a second adjustment formula, calculating the second adjustment formula in combination with the multi-objective fitness function, and dynamically adjusting the local search parameters in the aspheric machining machine tool data optimization algorithm according to the calculation result;

[0064] The second adjustment formula is: ,

[0065] Said is the next search step length, is the maximum value of the search step size, is the minimum value of the search step, is the fitness value of the current solution of the multi-objective fitness function, is the maximum value of fitness, is the minimum value of fitness.

[0066] In this example, a multi-objective fitness function is used to dynamically adjust the local search parameters in the aspheric machining machine data optimization algorithm, and the search step size is calculated and adjusted through a specific formula. The core of this process is to dynamically adjust the algorithm's search step size based on the results of the fitness function, thereby improving the efficiency and accuracy of the optimization algorithm, especially in complex aspheric machining processes.

[0067] Local search parameters usually refer to the ability of the algorithm to make small adjustments in the solution space, that is, the strength of fine search near the current solution. In the process of aspheric machining, the adjustment of local search parameters can enable the algorithm to flexibly adjust the search step size according to real-time feedback information, thereby ensuring the global search capability while achieving accurate optimization of the local solution.

[0068] The mechanism of action of this formula can be understood as follows: when the fitness value of the current solution is close to the maximum value of the fitness value, the search step will tend to a smaller value, which means that the algorithm will conduct a more detailed local search and focus on the precise optimization of the area near the current solution. This is especially important in the precision stage of aspheric machining, because a smaller search step can improve the accuracy of the tool path and avoid machining errors caused by excessive adjustments. On the contrary, when the fitness value of the current solution is close to the minimum value of the fitness value, the search step will tend to a larger value, which means that the algorithm will conduct a larger search in the solution space to avoid falling into the local optimal solution, thereby having a greater probability of finding the global optimal solution. In the initial stage of aspheric machining or when the algorithm is stagnant, a larger search step can help the algorithm jump out of the local area and conduct a wider search to find a better tool path.

[0069] This dynamic adjustment mechanism ensures that the algorithm can make optimal search step adjustments based on real-time fitness feedback at different stages of the machining process. For example, in the initial rough machining stage, the fitness value is low, and the algorithm may take a larger search step to quickly cover the solution space and find a suitable tool path. However, in the later finishing stage, as the fitness value increases, the search step gradually decreases, and the algorithm will focus more on subtle adjustments to ensure the accuracy and machining quality of the final tool path.

[0070] By combining the multi-objective fitness function and the above adjustment formula, the data optimization algorithm of the aspheric surface machining machine tool can realize the intelligent dynamic adjustment of the local search parameters, so that it can not only efficiently find the global optimal solution in the entire machining process, but also perform fine optimization at critical moments to ensure that the final machining effect reaches the best state. This adjustment strategy not only improves the efficiency of the optimization algorithm, but also provides high-precision control in complex aspheric surface machining, ensuring the quality of the final product.

[0071] In one example, a real-time surface reconstruction algorithm is used to dynamically adjust the digital model of the non-surface component based on the preprocessed parameter information to obtain an updated digital model, including: inputting the preprocessed parameter information into the real-time surface reconstruction algorithm; calculating the parameter information using the real-time surface reconstruction algorithm, and adjusting the control point positions and node vectors of the digital model according to the calculation results to obtain an updated digital model, wherein the array model is represented by B-spline.

[0072] In this example, a real-time surface reconstruction algorithm is used to dynamically adjust the digital model of the non-surface component based on the pre-processed parameter information in order to obtain a more accurate updated digital model to adapt to the complex changes of the surface during the actual processing. This process involves using a digital model represented by B-splines, and through the dynamic adjustment of the control point positions and node vectors, the model can reflect the real surface morphology of the non-spherical component during the processing in real time.

[0073] First, the pre-processed parameter information is obtained through early signal processing and data optimization. These data usually include the geometric characteristics of the aspheric components, processing status information, and real-time feedback data from the processing machine. These parameter information is input into the real-time surface reconstruction algorithm. The algorithm analyzes these data and dynamically adjusts the geometric characteristics of the digital model so that it can better fit the changes in the surface during the actual processing process.

[0074] The core of the real-time surface reconstruction algorithm is to use B-spline surfaces to represent and adjust digital models. B-spline is a flexible and powerful tool that describes the surface morphology through a set of control points and node vectors. Unlike traditional polynomial curves, B-spline has the characteristic of local control, that is, by adjusting a certain control point, the morphology of the surface will only change near the point without affecting the morphology of the entire surface, which makes it particularly suitable for accurate modeling of complex surfaces.

[0075] In this process, the real-time surface reconstruction algorithm determines the adjustment of the B-spline control point positions and node vectors by calculating the preprocessing parameter information. The positions of these control points determine the shape of the surface, while the node vectors affect the smoothness and frequency of changes of the surface. By dynamically adjusting these parameters, the digital model can respond to changes in the processing in real time, so that the model can accurately reflect the surface shape of the current processing stage.

[0076] For example, suppose that during the machining process, the machine detects an unexpected change in curvature in a certain area of ​​the aspheric component, or a machining error occurs due to a change in material properties. At this time, the real-time surface reconstruction algorithm will fine-tune the control points of the corresponding area based on these changes so that the surface shape of the digital model can reflect this change. At the same time, the adjustment of the node vector can properly control the smoothness of the surface and avoid excessive adjustments that cause discontinuities or unnatural shapes on the surface.

[0077] The updated digital model not only reflects the actual surface morphology of the aspheric component during the current machining process, but can also be used for subsequent tool path planning and machining parameter adjustment. This dynamic adjustment mechanism ensures the real-time and accuracy of the machining process, allowing the machining of aspheric components to reach higher quality standards.

[0078] By adopting the real-time surface reconstruction algorithm, the aspheric surface processing machine tool can dynamically adjust the digital model based on real-time feedback data in a complex processing environment, ensuring the accuracy and flexibility of the processing process. This method has a wide range of application prospects in high-precision manufacturing, especially in scenarios where fine processing of surfaces is required, such as the manufacture of optical lenses and complex molds. Through this method, the processing efficiency can be greatly improved, the processing error can be reduced, and ultimately a higher quality product output can be achieved.

[0079] refer to Figure 2 In an embodiment of the present invention, a control device for a non-spherical surface machining machine tool is also provided, comprising:

[0080] The acquisition unit 1 is used to acquire the surface data of the non-spherical component to be processed, and generate the tool path data of the non-spherical component to be processed according to the surface data.

[0081] The processing unit 2 is used to transmit the tool path data to the aspheric surface processing machine tool, and control the aspheric surface processing machine tool to process the aspheric surface component to be processed based on the tool path data.

[0082] The generating unit 3 is used to obtain the surface shape data of the processed aspheric component again, and to generate the tool path data of the aspheric component to be processed again according to the newly obtained surface shape data.

[0083] Execution unit 4 is used to transmit the newly generated tool path data to the aspheric surface machining machine tool again, and continue to execute the step of "controlling the aspheric surface machining machine tool to process the aspheric surface component to be processed based on the tool path data" until all the aspheric surface components to be processed are processed.

[0084] In this embodiment, for the specific implementation of each unit in the above device embodiment, please refer to the above method embodiment, which will not be repeated here.

[0085] Reference Figure 3 In an embodiment of the present invention, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0086] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0087] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented. It can be understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0088] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided by the present invention and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM.

[0089] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0090] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A control method for an aspheric surface machining machine tool, characterized in that: The following steps are involved: S1, acquiring the surface data of the non-spherical component to be processed, and generating the tool path data of the non-spherical component to be processed according to the surface data; wherein, generating the tool path data of the non-spherical component to be processed according to the surface data comprises: constructing a digital model of the non-spherical component based on the surface data of the non-spherical component; performing data processing on the digital model of the non-spherical component by using a non-spherical machining machine tool data optimization algorithm to obtain the tool path data of the non-spherical component to be processed; S2, transmitting the tool path data to the aspheric surface processing machine tool, and controlling the aspheric surface processing machine tool to process the aspheric surface component to be processed based on the tool path data; controlling the aspheric surface processing machine tool to process the aspheric surface component to be processed based on the tool path data, including: real-time monitoring of parameter information of the aspheric surface processing machine tool for processing the aspheric surface component, wherein the parameter information at least includes: tool position data and tool speed data; and adjusting the tool path data in real time according to the parameter information, and using the tool path data adjusted in real time to process the aspheric surface to be processed. The method comprises: using a real-time surface reconstruction algorithm to dynamically adjust the digital model of the aspheric component based on the preprocessed parameter information to obtain an updated digital model, wherein the updated digital model reflects the real surface morphology of the aspheric component during the processing; dynamically adjusting the parameters in the aspheric processing machine tool data optimization algorithm based on the updated digital model, and inputting the preprocessed parameter information into the dynamically adjusted aspheric processing machine tool data optimization algorithm to obtain adjusted tool path data ... A digital model is provided to dynamically adjust the parameters in the aspheric machining machine tool data optimization algorithm, including: performing geometric analysis on the updated digital model to extract geometric features, wherein the geometric features include: curvature radius, surface normal, surface intersection line, surface gradient change value and concave-convex value; using linear discriminant analysis to identify multiple target features that contribute most to data change in the geometric features, and performing covariance calculation on the multiple target features to obtain a covariance matrix; calculating the eigenvalues ​​and eigenvectors of the covariance matrix, and mapping the eigenvalues ​​and eigenvectors of the covariance matrix to a low-dimensional space to obtain low-dimensional feature data; and mapping the low-dimensional feature data to a low-dimensional space. An initial feature matrix is ​​formed, wherein the initial feature matrix is ​​an N×M matrix, M is the number of target features, and N is the number of sampling times of each target feature; the mean number of each column in the initial feature matrix and the standard deviation of the initial feature matrix are determined, and each eigenvalue in the initial feature matrix is ​​subtracted from the mean number of the column where the eigenvalue is located, and divided by the standard deviation of the initial feature matrix to obtain a final target feature matrix; the target feature matrix is ​​converted into a multi-objective fitness function by a weighted summation method, and the multi-objective fitness function is used to dynamically adjust the inertia weight and local search parameters in the aspheric machining machine tool data optimization algorithm; S3, acquiring the surface shape data of the processed aspheric component again, and generating the tool path data of the aspheric component to be processed again according to the newly acquired surface shape data; S4. Repeat steps S2-S3 based on the newly generated tool path data until all the aspheric components to be processed are processed.

2. The control method according to claim 1, characterized in that: Before adjusting the tool path data in real time according to the parameter information, the control method further includes: Identify and process the parameter information to divide it into multiple data sets, where different data sets correspond to different data types; Performing time domain analysis on the multiple data sets, dividing the multiple data sets into multiple frequency ranges, and identifying noise characteristics of each frequency range; Calculating filtering parameters of a bandpass filter based on the noise characteristics of each frequency range, and performing time-domain denoising processing on the parameter information using the filtering parameters to obtain filtered parameter information; The filtered parameter information is analyzed by using a z-score method to identify abnormal data points that deviate from a preset range, and the abnormal data points are smoothed to obtain smoothed parameter information; The smoothed parameter information is scaled to within a preset range, and the scaled parameter information is formatted to obtain pre-processed parameter information.

3. The control method according to claim 1, characterized in that: The multi-objective fitness function is used to dynamically adjust the inertia weight in the aspheric machining machine tool data optimization algorithm, including: Obtaining a first adjustment formula, calculating the first adjustment formula in combination with the multi-objective fitness function, and dynamically adjusting the inertia weight in the aspheric machining machine tool data optimization algorithm according to the calculation result; Wherein, the first adjustment formula is: , Said is the inertia weight of the next step, is the maximum value of the inertia weight, is the minimum value of the inertia weight, is the fitness value of the current solution of the multi-objective fitness function, is the maximum value of fitness, is the minimum value of fitness.

4. The control method according to claim 3, characterized in that: The multi-objective fitness function is used to dynamically adjust the local search parameters in the aspheric machining machine tool data optimization algorithm, including: Obtaining a second adjustment formula, calculating the second adjustment formula in combination with the multi-objective fitness function, and dynamically adjusting the local search parameters in the aspheric machining machine tool data optimization algorithm according to the calculation result; The second adjustment formula is: , Said is the next search step length, is the maximum value of the search step size, is the minimum value of the search step, is the fitness value of the current solution of the multi-objective fitness function, is the maximum value of fitness, is the minimum value of fitness.

5. A control device for an aspheric surface machining machine tool, characterized in that: include: An acquisition unit is used to execute step S1, wherein step S1 includes acquiring the surface data of the non-spherical component to be processed, and generating the tool path data of the non-spherical component to be processed according to the surface data; wherein generating the tool path data of the non-spherical component to be processed according to the surface data includes: constructing a digital model of the non-spherical component based on the surface data of the non-spherical component; performing data processing on the digital model of the non-spherical component using a non-spherical machining machine tool data optimization algorithm to obtain the tool path data of the non-spherical component to be processed; A processing unit, configured to execute step S2, wherein step S2 includes transmitting the tool path data to the aspheric surface processing machine tool, and controlling the aspheric surface processing machine tool to process the aspheric surface component to be processed based on the tool path data; The generating unit is used to execute step S3, wherein step S3 includes acquiring the surface data of the processed non-spherical component again, and generating the tool path data of the non-spherical component to be processed again according to the newly acquired surface data; controlling the non-spherical processing machine tool to process the non-spherical component to be processed based on the tool path data, including: real-time monitoring of parameter information of the non-spherical processing machine tool for processing the non-spherical component, wherein the parameter information at least includes: tool position data and tool speed data; and adjusting the tool path data in real time according to the parameter information, and using the tool path data adjusted in real time to process the non-spherical component. The non-spherical component to be processed is processed, and the tool path data is adjusted in real time according to the parameter information, including: using a real-time surface reconstruction algorithm, dynamically adjusting the digital model of the non-spherical component based on the pre-processed parameter information to obtain an updated digital model, wherein the updated digital model reflects the real surface morphology of the non-spherical component during the processing; based on the updated digital model, dynamically adjusting the parameters in the non-spherical processing machine tool data optimization algorithm, and inputting the pre-processed parameter information into the dynamically adjusted non-spherical processing machine tool data optimization algorithm to obtain adjusted tool path data, wherein, Based on the updated digital model, the parameters in the aspheric machining machine tool data optimization algorithm are dynamically adjusted, including: performing geometric analysis on the updated digital model to extract geometric features, wherein the geometric features include: curvature radius, surface normal, surface intersection line, surface gradient change value and concave-convex value; using linear discriminant analysis to identify multiple target features that contribute most to data changes in the geometric features, and performing covariance calculation on the multiple target features to obtain a covariance matrix; calculating the eigenvalues ​​and eigenvectors of the covariance matrix, and mapping the eigenvalues ​​and eigenvectors of the covariance matrix to a low-dimensional space to obtain low-dimensional feature data; and mapping the low-dimensional feature data to a low-dimensional space. The feature data is used to form an initial feature matrix, wherein the initial feature matrix is ​​an N×M matrix, M is the number of target features, and N is the sampling number of each target feature; the mean number of each column in the initial feature matrix and the standard deviation of the initial feature matrix are determined, and each eigenvalue in the initial feature matrix is ​​subtracted from the mean number of the column where the eigenvalue is located, and the result is divided by the standard deviation of the initial feature matrix to obtain a final target feature matrix; the target feature matrix is ​​converted into a multi-objective fitness function by a weighted summation method, and the multi-objective fitness function is used to dynamically adjust the inertia weight and local search parameters in the aspheric machining machine tool data optimization algorithm; The execution unit is used to repeatedly execute steps S2-S3 based on the newly generated tool path data until all the aspheric components to be processed are processed.

6. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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